Related Observation Info List
Get a list of RelatedObservationInfo objects.
GET /api/v3/relatedobservationinfos/?format=api&offset=100
{ "count": 1153, "next": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=200", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100", "results": [ { "ob_id": 108, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." }, "objectObservation": { "ob_id": 6146, "uuid": "63c30b6310faa22e90b7e4a7adce1fa2", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface winds from the WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface wind measurements are available from the Vaisala WXT510 surface meteorology instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales from 2007 to 2015. Wind speed and direction are measured by a WINDCAP (R) sensor which consists of an array of three equally-spaced ultrasonic transducers. These transducers are situated approximately 11 cm apart in a horizontal plane, leading to minimum, mean, and maximum values of speed and direction to be recorded. Data are available in netCDF formatted data files to all CEDA registered users under the UK Open Government licence.\r\n\r\nThis instrument has since been replaced by a Vaisala WXT520 surface meteorology instrument at the site.\r\n\r\nNote - the wind data from this instrument are known to be highly constrained by the valley topography in which the instrument is sited. As such it should not be used as a representation of the broad scale wind field, but may be of interest to those wishing to study valley flows." } }, { "ob_id": 109, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." }, "objectObservation": { "ob_id": 6146, "uuid": "63c30b6310faa22e90b7e4a7adce1fa2", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface winds from the WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface wind measurements are available from the Vaisala WXT510 surface meteorology instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales from 2007 to 2015. Wind speed and direction are measured by a WINDCAP (R) sensor which consists of an array of three equally-spaced ultrasonic transducers. These transducers are situated approximately 11 cm apart in a horizontal plane, leading to minimum, mean, and maximum values of speed and direction to be recorded. Data are available in netCDF formatted data files to all CEDA registered users under the UK Open Government licence.\r\n\r\nThis instrument has since been replaced by a Vaisala WXT520 surface meteorology instrument at the site.\r\n\r\nNote - the wind data from this instrument are known to be highly constrained by the valley topography in which the instrument is sited. As such it should not be used as a representation of the broad scale wind field, but may be of interest to those wishing to study valley flows." } }, { "ob_id": 110, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." }, "objectObservation": { "ob_id": 6166, "uuid": "8d7a920827e6137145f75dfe08d322dc", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface pressure, temperature and relative humidity data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface pressure, temperature and humidity data (PTU) were collected by a Vaisala WXT510 instrument located at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales.\r\n\r\nRainfall rate data from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." } }, { "ob_id": 111, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 6146, "uuid": "63c30b6310faa22e90b7e4a7adce1fa2", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface winds from the WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface wind measurements are available from the Vaisala WXT510 surface meteorology instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales from 2007 to 2015. Wind speed and direction are measured by a WINDCAP (R) sensor which consists of an array of three equally-spaced ultrasonic transducers. These transducers are situated approximately 11 cm apart in a horizontal plane, leading to minimum, mean, and maximum values of speed and direction to be recorded. Data are available in netCDF formatted data files to all CEDA registered users under the UK Open Government licence.\r\n\r\nThis instrument has since been replaced by a Vaisala WXT520 surface meteorology instrument at the site.\r\n\r\nNote - the wind data from this instrument are known to be highly constrained by the valley topography in which the instrument is sited. As such it should not be used as a representation of the broad scale wind field, but may be of interest to those wishing to study valley flows." }, "objectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." } }, { "ob_id": 112, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 6146, "uuid": "63c30b6310faa22e90b7e4a7adce1fa2", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface winds from the WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface wind measurements are available from the Vaisala WXT510 surface meteorology instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales from 2007 to 2015. Wind speed and direction are measured by a WINDCAP (R) sensor which consists of an array of three equally-spaced ultrasonic transducers. These transducers are situated approximately 11 cm apart in a horizontal plane, leading to minimum, mean, and maximum values of speed and direction to be recorded. Data are available in netCDF formatted data files to all CEDA registered users under the UK Open Government licence.\r\n\r\nThis instrument has since been replaced by a Vaisala WXT520 surface meteorology instrument at the site.\r\n\r\nNote - the wind data from this instrument are known to be highly constrained by the valley topography in which the instrument is sited. As such it should not be used as a representation of the broad scale wind field, but may be of interest to those wishing to study valley flows." }, "objectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." } }, { "ob_id": 113, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 6146, "uuid": "63c30b6310faa22e90b7e4a7adce1fa2", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface winds from the WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface wind measurements are available from the Vaisala WXT510 surface meteorology instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales from 2007 to 2015. Wind speed and direction are measured by a WINDCAP (R) sensor which consists of an array of three equally-spaced ultrasonic transducers. These transducers are situated approximately 11 cm apart in a horizontal plane, leading to minimum, mean, and maximum values of speed and direction to be recorded. Data are available in netCDF formatted data files to all CEDA registered users under the UK Open Government licence.\r\n\r\nThis instrument has since been replaced by a Vaisala WXT520 surface meteorology instrument at the site.\r\n\r\nNote - the wind data from this instrument are known to be highly constrained by the valley topography in which the instrument is sited. As such it should not be used as a representation of the broad scale wind field, but may be of interest to those wishing to study valley flows." }, "objectObservation": { "ob_id": 6166, "uuid": "8d7a920827e6137145f75dfe08d322dc", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface pressure, temperature and relative humidity data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface pressure, temperature and humidity data (PTU) were collected by a Vaisala WXT510 instrument located at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales.\r\n\r\nRainfall rate data from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." } }, { "ob_id": 114, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 6146, "uuid": "63c30b6310faa22e90b7e4a7adce1fa2", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface winds from the WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface wind measurements are available from the Vaisala WXT510 surface meteorology instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales from 2007 to 2015. Wind speed and direction are measured by a WINDCAP (R) sensor which consists of an array of three equally-spaced ultrasonic transducers. These transducers are situated approximately 11 cm apart in a horizontal plane, leading to minimum, mean, and maximum values of speed and direction to be recorded. Data are available in netCDF formatted data files to all CEDA registered users under the UK Open Government licence.\r\n\r\nThis instrument has since been replaced by a Vaisala WXT520 surface meteorology instrument at the site.\r\n\r\nNote - the wind data from this instrument are known to be highly constrained by the valley topography in which the instrument is sited. As such it should not be used as a representation of the broad scale wind field, but may be of interest to those wishing to study valley flows." }, "objectObservation": { "ob_id": 6166, "uuid": "8d7a920827e6137145f75dfe08d322dc", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface pressure, temperature and relative humidity data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface pressure, temperature and humidity data (PTU) were collected by a Vaisala WXT510 instrument located at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales.\r\n\r\nRainfall rate data from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." } }, { "ob_id": 115, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 6138, "uuid": "5aa035592990b6915c2184d0c155cb60", "short_code": "ob", "title": "Met Office 915 MHz UHF Radar Data deployed at the NERC MST Radar Facility, Capel Dewi, Wales (1995 -2002)", "abstract": "Data from the Met Office's 915 MHz LAP3000 UHF (Ultra High Frequency) boundary layer wind profiler deployed at the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. The instrument was deployed from February 1995 to March 2002. These data are made available under the NERC-Met Office agreement." }, "objectObservation": { "ob_id": 6111, "uuid": "e63cba90f831f00e9f32319422dc834d", "short_code": "ob", "title": "Met Office: Vaisala Radian LAP3000 915 MHz vertical wind profiler measurements at Capel Dewi, UK (1999 - 2002)", "abstract": "The Met Office deployed a Vaisala Radian LAP3000 915 MHz wind profiler at the Natural Environment Research Council's (NERC) Mesophere-Stratosphere-Troposphere (MST) Radar Facility site at Capel Dewi, near Absersywyth, Wales, from November 1999 to March 2002. This deployment was to co-locate this UHF boundary layer wind profiler with the NERC MST VHF wind profiling radar - giving a combined coverage between the two instruments from around 300m to 20 km. At the time the Met Office's 915 Mhz wind profiler was an integral part of the Met Office's UK \"Operational Upper Air Network\", providing high resolution wind information from just above the surface (around 300m) up to a maximum of 8 km, depending on the atmospheric conditions. The Capel Dewi site is located at 52.42 N, 4.01 W and a height of 92 m above mean sea-level. The site has a WMO id of 03501. The instrument's hight resolution is 60/200 m depending on the operational mode. It has a beam angle of 15.0 degrees and is operated with an averaging period of 30 minutes. This instrument was subsequently deployed at South Uist and then at its present location on the Isle of Man.\r\n\r\nThis dataset contains vertical wind profiles from the Vaisala Radian LAP3000 915MHz wind profiler located at Capel Dewi." } }, { "ob_id": 116, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 6111, "uuid": "e63cba90f831f00e9f32319422dc834d", "short_code": "ob", "title": "Met Office: Vaisala Radian LAP3000 915 MHz vertical wind profiler measurements at Capel Dewi, UK (1999 - 2002)", "abstract": "The Met Office deployed a Vaisala Radian LAP3000 915 MHz wind profiler at the Natural Environment Research Council's (NERC) Mesophere-Stratosphere-Troposphere (MST) Radar Facility site at Capel Dewi, near Absersywyth, Wales, from November 1999 to March 2002. This deployment was to co-locate this UHF boundary layer wind profiler with the NERC MST VHF wind profiling radar - giving a combined coverage between the two instruments from around 300m to 20 km. At the time the Met Office's 915 Mhz wind profiler was an integral part of the Met Office's UK \"Operational Upper Air Network\", providing high resolution wind information from just above the surface (around 300m) up to a maximum of 8 km, depending on the atmospheric conditions. The Capel Dewi site is located at 52.42 N, 4.01 W and a height of 92 m above mean sea-level. The site has a WMO id of 03501. The instrument's hight resolution is 60/200 m depending on the operational mode. It has a beam angle of 15.0 degrees and is operated with an averaging period of 30 minutes. This instrument was subsequently deployed at South Uist and then at its present location on the Isle of Man.\r\n\r\nThis dataset contains vertical wind profiles from the Vaisala Radian LAP3000 915MHz wind profiler located at Capel Dewi." }, "objectObservation": { "ob_id": 6138, "uuid": "5aa035592990b6915c2184d0c155cb60", "short_code": "ob", "title": "Met Office 915 MHz UHF Radar Data deployed at the NERC MST Radar Facility, Capel Dewi, Wales (1995 -2002)", "abstract": "Data from the Met Office's 915 MHz LAP3000 UHF (Ultra High Frequency) boundary layer wind profiler deployed at the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. The instrument was deployed from February 1995 to March 2002. These data are made available under the NERC-Met Office agreement." } }, { "ob_id": 117, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 6122, "uuid": "51234b47e27aa55e50f8e2eb44927c1e", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface Meteorological Data, Capel Dewi site, Wales", "abstract": "Surface meteorological data are measured by a number of instruments deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. This dataset consists of data collected by the following suite instruments connected to a Campbell Scientific CR10 Climate Data Logger: Campbell Scientific 107 thermistor temperature probe mounted inside an URS1 unaspirated radiation shield. Accuracy: +/- 0.4 degrees C. Vaisala PTB101B barometric pressure sensor. Accuracy: +/- 2.0 hPa. Vaisala HMP45C temperature and relative humidity probe (from which only the humidity measurements are used) mounted inside an URS1 un-aspirated radiation shield. Accuracy: +/- 0.3%. Environmental Measurements ARG100 tipping bucket raingauge. Kipp and Zonen CM3 thermopile pyranometer (WMO second class). Accuracy: +/- 0.5%. The raingauge is located on the ground. All other sensors are mounted on a post approximately 1 m above the ground. \r\n\r\nThe data logger initially samples the atmospheric temperature, pressure and humidity sensors at 5 s intervals. Mean values are calculated over each 60 s and the outputs from the logger represent minima, means and maxima of these 60 s means over each 10 minute sample period. The data logger is connected to a tipping bucket raingauge (sampled every 1 s, and recording tips for each 0.20 mm accumulation of rain); pyranometer (sampled every every 5 s, recording the down-welling radiation within a hemispheric field of view with a flat response in the spectral range 305 - 2800 nm); \r\n\r\nThe data are available in NASA-Ames formatted files.\r\n\r\nIndependent surface meteorological data are also available from the Vaisala WXT510 instrument also located at the site and are available in the wxt510-precipitation and PTU datasets." }, "objectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." } }, { "ob_id": 118, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 6122, "uuid": "51234b47e27aa55e50f8e2eb44927c1e", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface Meteorological Data, Capel Dewi site, Wales", "abstract": "Surface meteorological data are measured by a number of instruments deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. This dataset consists of data collected by the following suite instruments connected to a Campbell Scientific CR10 Climate Data Logger: Campbell Scientific 107 thermistor temperature probe mounted inside an URS1 unaspirated radiation shield. Accuracy: +/- 0.4 degrees C. Vaisala PTB101B barometric pressure sensor. Accuracy: +/- 2.0 hPa. Vaisala HMP45C temperature and relative humidity probe (from which only the humidity measurements are used) mounted inside an URS1 un-aspirated radiation shield. Accuracy: +/- 0.3%. Environmental Measurements ARG100 tipping bucket raingauge. Kipp and Zonen CM3 thermopile pyranometer (WMO second class). Accuracy: +/- 0.5%. The raingauge is located on the ground. All other sensors are mounted on a post approximately 1 m above the ground. \r\n\r\nThe data logger initially samples the atmospheric temperature, pressure and humidity sensors at 5 s intervals. Mean values are calculated over each 60 s and the outputs from the logger represent minima, means and maxima of these 60 s means over each 10 minute sample period. The data logger is connected to a tipping bucket raingauge (sampled every 1 s, and recording tips for each 0.20 mm accumulation of rain); pyranometer (sampled every every 5 s, recording the down-welling radiation within a hemispheric field of view with a flat response in the spectral range 305 - 2800 nm); \r\n\r\nThe data are available in NASA-Ames formatted files.\r\n\r\nIndependent surface meteorological data are also available from the Vaisala WXT510 instrument also located at the site and are available in the wxt510-precipitation and PTU datasets." }, "objectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." } }, { "ob_id": 119, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." }, "objectObservation": { "ob_id": 6122, "uuid": "51234b47e27aa55e50f8e2eb44927c1e", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface Meteorological Data, Capel Dewi site, Wales", "abstract": "Surface meteorological data are measured by a number of instruments deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. This dataset consists of data collected by the following suite instruments connected to a Campbell Scientific CR10 Climate Data Logger: Campbell Scientific 107 thermistor temperature probe mounted inside an URS1 unaspirated radiation shield. Accuracy: +/- 0.4 degrees C. Vaisala PTB101B barometric pressure sensor. Accuracy: +/- 2.0 hPa. Vaisala HMP45C temperature and relative humidity probe (from which only the humidity measurements are used) mounted inside an URS1 un-aspirated radiation shield. Accuracy: +/- 0.3%. Environmental Measurements ARG100 tipping bucket raingauge. Kipp and Zonen CM3 thermopile pyranometer (WMO second class). Accuracy: +/- 0.5%. The raingauge is located on the ground. All other sensors are mounted on a post approximately 1 m above the ground. \r\n\r\nThe data logger initially samples the atmospheric temperature, pressure and humidity sensors at 5 s intervals. Mean values are calculated over each 60 s and the outputs from the logger represent minima, means and maxima of these 60 s means over each 10 minute sample period. The data logger is connected to a tipping bucket raingauge (sampled every 1 s, and recording tips for each 0.20 mm accumulation of rain); pyranometer (sampled every every 5 s, recording the down-welling radiation within a hemispheric field of view with a flat response in the spectral range 305 - 2800 nm); \r\n\r\nThe data are available in NASA-Ames formatted files.\r\n\r\nIndependent surface meteorological data are also available from the Vaisala WXT510 instrument also located at the site and are available in the wxt510-precipitation and PTU datasets." } }, { "ob_id": 120, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 6169, "uuid": "62b7c0a31297c5a0fe115083eb8036c6", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface precipitation data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface precipitation measurements from the precipitation sensor on the Vaisala WXT510 instrument deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. \r\n\r\nThese data are available to any registered CEDA user under the UK Open Government Licence.\r\n\r\nSurface pressure, temperature and humidity data (PTU) from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." }, "objectObservation": { "ob_id": 6122, "uuid": "51234b47e27aa55e50f8e2eb44927c1e", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface Meteorological Data, Capel Dewi site, Wales", "abstract": "Surface meteorological data are measured by a number of instruments deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. This dataset consists of data collected by the following suite instruments connected to a Campbell Scientific CR10 Climate Data Logger: Campbell Scientific 107 thermistor temperature probe mounted inside an URS1 unaspirated radiation shield. Accuracy: +/- 0.4 degrees C. Vaisala PTB101B barometric pressure sensor. Accuracy: +/- 2.0 hPa. Vaisala HMP45C temperature and relative humidity probe (from which only the humidity measurements are used) mounted inside an URS1 un-aspirated radiation shield. Accuracy: +/- 0.3%. Environmental Measurements ARG100 tipping bucket raingauge. Kipp and Zonen CM3 thermopile pyranometer (WMO second class). Accuracy: +/- 0.5%. The raingauge is located on the ground. All other sensors are mounted on a post approximately 1 m above the ground. \r\n\r\nThe data logger initially samples the atmospheric temperature, pressure and humidity sensors at 5 s intervals. Mean values are calculated over each 60 s and the outputs from the logger represent minima, means and maxima of these 60 s means over each 10 minute sample period. 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The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." }, "objectObservation": { "ob_id": 6122, "uuid": "51234b47e27aa55e50f8e2eb44927c1e", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface Meteorological Data, Capel Dewi site, Wales", "abstract": "Surface meteorological data are measured by a number of instruments deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. This dataset consists of data collected by the following suite instruments connected to a Campbell Scientific CR10 Climate Data Logger: Campbell Scientific 107 thermistor temperature probe mounted inside an URS1 unaspirated radiation shield. Accuracy: +/- 0.4 degrees C. Vaisala PTB101B barometric pressure sensor. Accuracy: +/- 2.0 hPa. Vaisala HMP45C temperature and relative humidity probe (from which only the humidity measurements are used) mounted inside an URS1 un-aspirated radiation shield. Accuracy: +/- 0.3%. Environmental Measurements ARG100 tipping bucket raingauge. Kipp and Zonen CM3 thermopile pyranometer (WMO second class). Accuracy: +/- 0.5%. The raingauge is located on the ground. All other sensors are mounted on a post approximately 1 m above the ground. \r\n\r\nThe data logger initially samples the atmospheric temperature, pressure and humidity sensors at 5 s intervals. Mean values are calculated over each 60 s and the outputs from the logger represent minima, means and maxima of these 60 s means over each 10 minute sample period. The data logger is connected to a tipping bucket raingauge (sampled every 1 s, and recording tips for each 0.20 mm accumulation of rain); pyranometer (sampled every every 5 s, recording the down-welling radiation within a hemispheric field of view with a flat response in the spectral range 305 - 2800 nm); \r\n\r\nThe data are available in NASA-Ames formatted files.\r\n\r\nIndependent surface meteorological data are also available from the Vaisala WXT510 instrument also located at the site and are available in the wxt510-precipitation and PTU datasets." } }, { "ob_id": 122, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 6166, "uuid": "8d7a920827e6137145f75dfe08d322dc", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface pressure, temperature and relative humidity data from the Vaisala WXT510 instrument, Capel Dewi site, Wales (2007-2015)", "abstract": "Surface pressure, temperature and humidity data (PTU) were collected by a Vaisala WXT510 instrument located at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales.\r\n\r\nRainfall rate data from this instrument are also available as a separate dataset within the MST Radar Facility dataset collection.\r\n\r\nThe WXT-510 instrument at the site began operational recording in December 2007 and ceased in January 2015, subsequently being replaced by a Vaisala WXT-520 instrument. The WXT520 data are also available from CEDA as part of the MST Radar Facility's dataset collection.\r\n\r\nIndependent surface meteorological data are also collected from a suite of instruments by a Campbell Scientific CR10 Climate Data Logger. These data are available as a separate dataset within the MST Radar Facility dataset collection." }, "objectObservation": { "ob_id": 6122, "uuid": "51234b47e27aa55e50f8e2eb44927c1e", "short_code": "ob", "title": "NERC Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: Surface Meteorological Data, Capel Dewi site, Wales", "abstract": "Surface meteorological data are measured by a number of instruments deployed at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. This dataset consists of data collected by the following suite instruments connected to a Campbell Scientific CR10 Climate Data Logger: Campbell Scientific 107 thermistor temperature probe mounted inside an URS1 unaspirated radiation shield. Accuracy: +/- 0.4 degrees C. Vaisala PTB101B barometric pressure sensor. Accuracy: +/- 2.0 hPa. Vaisala HMP45C temperature and relative humidity probe (from which only the humidity measurements are used) mounted inside an URS1 un-aspirated radiation shield. Accuracy: +/- 0.3%. Environmental Measurements ARG100 tipping bucket raingauge. Kipp and Zonen CM3 thermopile pyranometer (WMO second class). Accuracy: +/- 0.5%. The raingauge is located on the ground. All other sensors are mounted on a post approximately 1 m above the ground. \r\n\r\nThe data logger initially samples the atmospheric temperature, pressure and humidity sensors at 5 s intervals. Mean values are calculated over each 60 s and the outputs from the logger represent minima, means and maxima of these 60 s means over each 10 minute sample period. The data logger is connected to a tipping bucket raingauge (sampled every 1 s, and recording tips for each 0.20 mm accumulation of rain); pyranometer (sampled every every 5 s, recording the down-welling radiation within a hemispheric field of view with a flat response in the spectral range 305 - 2800 nm); \r\n\r\nThe data are available in NASA-Ames formatted files.\r\n\r\nIndependent surface meteorological data are also available from the Vaisala WXT510 instrument also located at the site and are available in the wxt510-precipitation and PTU datasets." } }, { "ob_id": 124, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 24693, "uuid": "294b4075ddbc4464bb06742816813bdc", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02", "abstract": "The CO2_SCI_BESD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (CO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board the European Space Agency's (ESA's) environmental research satellite ENVISAT. It has been produced using the Bremen Optimal Estimation DOAS (BESD) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.\r\n\r\nThe Bremen Optimal Estimation DOAS (BESD) algorithm is a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information about clouds and aerosols. This is the Greenhouse Gases CCI baseline algorithm for deriving SCIAMACHY XCO2 data. A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this BESD product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.\r\n\r\nFor further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents." }, "objectObservation": { "ob_id": 14575, "uuid": "0ecfa9cc4f81459bba840aead5eda6cd", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CO2 Level 2 Data Product (CO2_SCI_BESD), version 02.01.01, generated with the BESD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the BESD XCO2 SCIAMACHY product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been produced with the Bremen Optimal Estimation DOAS (BESD) algorithm, a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information of clouds and aerosols. This is the GHG CCI baseline algorithm for deriving SCIAMACHY XCO2 data: A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage in the documentation section. \r\n\r\nFor further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 125, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25916, "uuid": "e493802d83c846c8b76f817866fb74cc", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the WFMD algorithm (CO2_SCI_WFMD), v4.0", "abstract": "The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.\r\n\r\nThe WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. \r\n\r\nThe data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section." }, "objectObservation": { "ob_id": 14577, "uuid": "95b804971495428285e136edaa6ac066", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CO2 Level 2 Data Product (CO2_SCI_WFMD), version 3.9, generated with the WFMD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the WFMD XCO2 SCIAMACHY product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG CCI, and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage provided in the documentation section. \r\n\r\nThe data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 126, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25918, "uuid": "9255faeb392f41debf5402caa40dada8", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Column-averaged CO2 from GOSAT generated with the OCFP (UoL-FP) algorithm (CO2_GOS_OCFP), v7.0", "abstract": "The CO2_GOS_OCFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the University of Leicester Full-Physics Retrieval Algorithm, which is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the alternative SRFP algorithm, is also available. The OCFP product is considered the GHG_cci baseline product and it is advised that users who aren't sure which of the two products to use, use this product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.\r\n\r\nThe XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG)." }, "objectObservation": { "ob_id": 14563, "uuid": "3c098ba124a347678a00b0102bab9f0a", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CO2 Level 2 Data Product (CO2_GOS_OCFP) version 6.0, generated with the OCFP (UoL-FP) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project, the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). The University of Leicester Full-Physics Retrieval Algorithm has been applied to the TANSO-FTS data, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the SRFP algorithm, is also available.\r\n\r\nThe XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 127, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25920, "uuid": "e61704b00267405082fbd41bb710dd74", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from GOSAT generated with the SRFP (RemoTeC) algorithm (CO2_GOS_SRFP), v2.3.8", "abstract": "The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project. This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4).\r\n\r\nThe RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product. It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product. For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage. \r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document." }, "objectObservation": { "ob_id": 14565, "uuid": "c00d02a4c7fa4fbea2d6d8ebbc3be5c0", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CO2 Level 2 Data Product (CO2_GOS_SRFP) version 2.3.7, generated with the SRFP (RemoTeC) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and Climate Research Data Package Number 2 (CRDP#3), the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). In this case, the RemoTeC Full Physics (SRFP) algorithm, jointly developed at SRON and KIT, has been applied to the TANSO-FTS data. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available.\r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 128, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25922, "uuid": "9f002827ba7d48f59019fcfd3577a57e", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged CO2 Merged Product generated with the EMMA algorithm (CO2_EMMA), v2.2", "abstract": "The CO2_EMMA dataset comprises of level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using the ensample median algorithm EMMA to produce a merged SCIAMACHY and GOSAT XCO2 Level 2 product, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v2.2, and forms part of the Climate Research Data Package 4.\r\n\r\nThe EMMA algorithm has been applied to level 2 data from multiple XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This merged SCIAMACHY and GOSAT XCO2 Level 2 product is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). \r\n\r\nFor further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR)." }, "objectObservation": { "ob_id": 14579, "uuid": "3e06538585d04d9e8c848215eedeb5a4", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Merged CO2 Level 2 Data Product (CO2_EMMA), version 2.1, generated with the EMMA algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG), the XCO2 EMMA product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced by applying the ensemble median algorithm EMMA to level 2 data of 7 XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This is therefore a merged SCIAMACHY and GOSAT XCO2 Level 2 product, primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). This version of the product covers 4 years. \r\n\r\nFor further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR) in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 129, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25924, "uuid": "aa09603e91b44f3cb1573c9dd415e8a8", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the WFMD algorithm (CH4_SCI_WFMD), version 4.0", "abstract": "The CH4_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT, as part of the ESA's Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 4.0, and forms part of the Climate Research Data Package 4.\r\n\r\nThe Weighting Function Modified DOAS (WFMD) algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. \r\n\r\nThe data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. \r\n\r\nFor further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents." }, "objectObservation": { "ob_id": 14572, "uuid": "89a49c8e8dbb4a1bb8799589ffd39dc7", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CH4 Level 2 Data Product (CH4_SCI_WFMD), version 3.9, generated with the WFMD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 SCI product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived by applying the Weighting Function Modified DOAS (WFMD) algorithm to the SCIAMACHY data, a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. \r\n\r\nThe data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. \r\n\r\nFor further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 130, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25926, "uuid": "8f5623a85d2e4b9b8ab5313f65a7c994", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from SCIAMACHY generated with the IMAP-DOAS algorithm (CH4_SCI_IMAP), v7.2", "abstract": "The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4). It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm. It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4.\r\n\r\nThe IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. \r\n\r\nThe data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document." }, "objectObservation": { "ob_id": 14568, "uuid": "33cd85fdc2454d2796c64c673b9427c9", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CH4 Level 2 Data Product (CH4_SCI_IMAP), version 7.1, generated with the IMAP-DOAS algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 SCI product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived by applying the IMAP-DOAS algorithm developed at the University of Heidelberg and SRON to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. \r\n\r\nThe data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 131, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25930, "uuid": "56f81895cb094bd8a1638aa12d6c7499", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCFP (UoL-FP) algorithm (CH4_GOS_OCFP), version 2.1", "abstract": "The CH4_GOS_OCFP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the University of Leicester Full-Physics Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version is version 2.1 and forms part of the Climate Research Data Package 4.\r\n\r\nThe University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available.\r\n\r\nThe XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG)." }, "objectObservation": { "ob_id": 14585, "uuid": "f94a00d0282d4bd1b3a7dd07777c874d", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CH4 Full Physics Level 2 Data Product, version 1.0 (CH4_GOS_OCFP) generated with the OCFP (UoL-FP) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 GOS Full Physics product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). \r\n\r\nThis version of the full physics product (version 1.0) has been generated using the OCFP University of Leicester Full-Physics Methane Retrieval Algorithm, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra baseline algorithm. This algorithm has been designated as an 'alternative' algorithm for the GHG CCI full physics methane retrievals. A second product has also been generated from the TANSO-FTS data by applying the baseline GHG CCI full physics algorithm, the RemoTeC SRFP algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use the baseline product generated with the SRFP baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.\r\n\r\nThe product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-FP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 132, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25932, "uuid": "46d136149d0a4f1cb8de7efbe8abf4b2", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRFP (RemoTeC) Full Physics algorithm (CH4_GOS_SRFP), version 2.3.8", "abstract": "The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4.\r\n\r\nThe RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. \r\n\r\nFor further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document." }, "objectObservation": { "ob_id": 14552, "uuid": "0fb6a635c881494ea1a22fce7718d2b2", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CH4 Full Physics Level 2 Data Product (CH4_GOS_SRFP), version 2.3.7, generated with the SRFP (RemoTeC) algorithm", "abstract": "Created as part of The European Space Agency's (ESA) GHG CCI project, the XCH4 GOS Full Physics (FP) data product is a level 2, column-averaged mole fraction (mixing ratio) of methane (CH4). The product is part of Climate Research Data Package Number 3 (CRDP#3) and is based upon data generated for the years 2009-2013. It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). By contrast to the Proxy (PR) versions of the product generated with proxy algorithms, the FP products have been produced using full physics algorithms, in this case the RemoTeC SRFP baseline algorithm.\r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. \r\n\r\nFor further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section. \r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases.\r\nTo register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 133, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25934, "uuid": "96d5b75ea29946c5aab8214ddbab252b", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8", "abstract": "The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. \r\n\r\nThis Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. \r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents." }, "objectObservation": { "ob_id": 14554, "uuid": "cca6035bb0f240ffbb035e9355f09fe1", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CH4 Proxy Level 2 Data Product (CH4_GOS_SRPR), version 2.3.7, generated with the SRPR (RemoTeC) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 GOS SRPR (Proxy) product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). \r\n\r\nThis proxy version of the product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. \r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases.\r\nTo register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 134, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25974, "uuid": "de33ae6e5b724a41be34d0f107a65ce2", "short_code": "ob", "title": "HadISDH: gridded global monthly land surface humidity data version 4.0.0.2017f", "abstract": "This is the 4.0.0.2017f version of the HadISDH land data. These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2017. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nThis version extends the 3.0.0.2016p version to the end of 2017 and constitutes a major update to HadISDH due to a change to using the 1981-2010 period as its climatological reference period both to make it more consistent with other monitoring products and to maximise station coverage now that it uses the larger station database of HadISD2. Users are advised to read the update document in the docs section for full details. This version now uses the 1981-2010 period as its climatological reference period both to make it more consistent with other monitoring products and to maximise station coverage now that it uses the larger station database of HadISD2. \r\n\r\nAdditionally, there has been a small methodological change. Stations with large adjustments made during homogenisation are removed based on thresholds for q (>3g/kg), RH (>15%rh), T (>5degC) and Td (>5degC) rather than just T and Td. This results in 54 stations being removed as opposed to 29 last year. All other processing steps for HadISDH remain identical. \r\n\r\nThe new version of HadISD2 (2.0.2.2017p) has pulled through some historical changes to stations which are passed on to HadISDH. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data. \r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more detail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 19171, "uuid": "f34f97696cd94946b6ace2e8738df805", "short_code": "ob", "title": "HadISDH: gridded global monthly land surface humidity data version 3.0.0.2016p", "abstract": "This is the 3.0.0.2016p version of the HadISDH land data. The data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2016. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) and station products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nThis version extends the 2.1.0.2015p version to the end of 2016 and constitutes a major update to HadISDH due to a major update of the source data HadISD. Improvements in this version include increased numbers of stations (~8000) and updated methodologies. Users are advised to read the update document in the docs section for full details.\r\n\r\nUncertainty estimates are provided at the station and gridbox level covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more detail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013." } }, { "ob_id": 136, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25976, "uuid": "acee665e3e664a73b8ad247e99b343d5", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2017, v2.0.2.2017f", "abstract": "This is version 2.0.2.2017f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.1.2016p to include 2017 and so spans 1931-2017, it replaces the preliminary version (v2.0.2.2017p) as the ISD data for 2017 are now finalised.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20171231_v2-0-2-2017f.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nFor a more detailed description of precipitation see: http://hadisd.blogspot.co.uk/2018/03/precipitation-in-hadisd.html\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 23991, "uuid": "aeedfad6a2f345a8a0b50f40f4c0787f", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2016, v2.0.1.2016f", "abstract": "This is version 2.0.1.2016f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.0.2015p to span 1931-2016 and includes an increase in the number of stations and an updated methodology and is the final version of the 2016 data. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v2-0-1-2016p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 137, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 20359, "uuid": "844b55466c9044b5a8cb35037992790b", "short_code": "ob", "title": "HadISDH: gridded global monthly land surface humidity data version 2.1.0.2015p", "abstract": "This is the 2.1.0.2015p version of the HadISDH land data. The data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2015. \r\n\r\nThis version extends the 2.0.1.2014p version to the end of 2015 and includes some minor updates users are advised to read the update document in the docs section for full details. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) and station products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nUncertainty estimates are provided at the station and gridbox level covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more detail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 13528, "uuid": "fd038d5dcba14f58ac2770ad47acf39c", "short_code": "ob", "title": "HadISDH: gridded global monthly land surface humidity data version 2.0.1.2014p", "abstract": "This is the 2.0.1.2014p version of the HadISDH land data. The data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2014.\r\n\r\nMonthly gridded (5 degree by 5 degree) and station products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nUncertainty estimates are provided at the station and gridbox level covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more detail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013." } }, { "ob_id": 138, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19171, "uuid": "f34f97696cd94946b6ace2e8738df805", "short_code": "ob", "title": "HadISDH: gridded global monthly land surface humidity data version 3.0.0.2016p", "abstract": "This is the 3.0.0.2016p version of the HadISDH land data. The data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2016. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) and station products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nThis version extends the 2.1.0.2015p version to the end of 2016 and constitutes a major update to HadISDH due to a major update of the source data HadISD. Improvements in this version include increased numbers of stations (~8000) and updated methodologies. Users are advised to read the update document in the docs section for full details.\r\n\r\nUncertainty estimates are provided at the station and gridbox level covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more detail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 20359, "uuid": "844b55466c9044b5a8cb35037992790b", "short_code": "ob", "title": "HadISDH: gridded global monthly land surface humidity data version 2.1.0.2015p", "abstract": "This is the 2.1.0.2015p version of the HadISDH land data. The data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2015. \r\n\r\nThis version extends the 2.0.1.2014p version to the end of 2015 and includes some minor updates users are advised to read the update document in the docs section for full details. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) and station products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nUncertainty estimates are provided at the station and gridbox level covering station uncertainty (climatological, homogenisation and measurement uncertainty), gridbox spatial and temporal sampling uncertainty and combined station and sampling uncertainty.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more detail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013." } }, { "ob_id": 139, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 25976, "uuid": "acee665e3e664a73b8ad247e99b343d5", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2017, v2.0.2.2017f", "abstract": "This is version 2.0.2.2017f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.1.2016p to include 2017 and so spans 1931-2017, it replaces the preliminary version (v2.0.2.2017p) as the ISD data for 2017 are now finalised.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20171231_v2-0-2-2017f.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nFor a more detailed description of precipitation see: http://hadisd.blogspot.co.uk/2018/03/precipitation-in-hadisd.html\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 25833, "uuid": "3c67dfe728594fadabb920564af4df4a", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2017, v2.0.2.2017p", "abstract": "This is version 2.0.2.2017p of Met Office Hadley Centre's Integrated Surface Database, HadISD. 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Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20171231_v2-0-2-2017p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 140, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 25833, "uuid": "3c67dfe728594fadabb920564af4df4a", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2017, v2.0.2.2017p", "abstract": "This is version 2.0.2.2017p of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.1.2016f to include 2017 and so spans 1931-2017. These data include an update to the station selected and contain 8103 stations. These are the preliminary data for this version, a finalised version will be released in a few months with any station updates.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20171231_v2-0-2-2017p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 25976, "uuid": "acee665e3e664a73b8ad247e99b343d5", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2017, v2.0.2.2017f", "abstract": "This is version 2.0.2.2017f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.1.2016p to include 2017 and so spans 1931-2017, it replaces the preliminary version (v2.0.2.2017p) as the ISD data for 2017 are now finalised.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20171231_v2-0-2-2017f.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nFor a more detailed description of precipitation see: http://hadisd.blogspot.co.uk/2018/03/precipitation-in-hadisd.html\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 141, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 23991, "uuid": "aeedfad6a2f345a8a0b50f40f4c0787f", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2016, v2.0.1.2016f", "abstract": "This is version 2.0.1.2016f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.0.2015p to span 1931-2016 and includes an increase in the number of stations and an updated methodology and is the final version of the 2016 data. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v2-0-1-2016p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 20357, "uuid": "de98d82f34d14e888bde6d56aaf5420c", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2016, v2.0.1.2016p", "abstract": "This is version 2.0.1.2016p of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.0.2015p to span 1931-2016 and includes an increase in the number of stations and an updated methodology. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v2-0-1-2016p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 142, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 20357, "uuid": "de98d82f34d14e888bde6d56aaf5420c", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2016, v2.0.1.2016p", "abstract": "This is version 2.0.1.2016p of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.0.2015p to span 1931-2016 and includes an increase in the number of stations and an updated methodology. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v2-0-1-2016p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 23991, "uuid": "aeedfad6a2f345a8a0b50f40f4c0787f", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2016, v2.0.1.2016f", "abstract": "This is version 2.0.1.2016f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v2.0.0.2015p to span 1931-2016 and includes an increase in the number of stations and an updated methodology and is the final version of the 2016 data. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v2-0-1-2016p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 143, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 20039, "uuid": "64ee8b9d6e0048cfa903f1c21937b83a", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2012, v1.0.1.2012p", "abstract": "This is version 1.0.1.2012p of HadISD the Met Office Hadley Centre's global sub-daily data, extending v1.0.0.2011f to span 1/1/1973 - 31/12/2012. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19730101-20121231_v1-0-1-2012p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1" }, "objectObservation": { "ob_id": 20034, "uuid": "522c2801b6994745a32b6b63a03891f0", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2011, v1.0.0.2011f", "abstract": "This is version 1.0.0.2011f of HadISD the Met Office Hadley Centre's global sub-daily data spanning 1/1/1973 - 31/12/2011. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19730101-20111231_v1-0-0-2011f.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1" } }, { "ob_id": 144, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 20040, "uuid": "4c44bc7beac5428aa1af2f3aca1a2055", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2013, v1.0.2.2013f", "abstract": "This is version 1.0.2.2013f of HadISD the Met Office Hadley Centre's global sub-daily data, extending v1.0.1.2012p to span 1/1/1973 - 31/12/2013. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19730101-20131231_v1-0-2-2013f.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 20039, "uuid": "64ee8b9d6e0048cfa903f1c21937b83a", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2012, v1.0.1.2012p", "abstract": "This is version 1.0.1.2012p of HadISD the Met Office Hadley Centre's global sub-daily data, extending v1.0.0.2011f to span 1/1/1973 - 31/12/2012. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19730101-20121231_v1-0-1-2012p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1" } }, { "ob_id": 146, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 20041, "uuid": "c1b593ad0a9945e18fa8d975908f19f0", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2015, v2.0.0.2015p", "abstract": "This is version 2.0.0.2015p of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v1.0.4.2015p to span 1931-2015 and includes an increase in the number of stations and an updated methodology. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v2-0-0-2015p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 19173, "uuid": "7b6993cbf7ec45f9ad01b86bed537e4c", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1973-2015, v1.0.4.2015p", "abstract": "This is version 1.0.4.2015p of HadISD (27 April 2015) the Met Office Hadley Centre's global sub-daily data, extending v1.0.3.2014f to span 1/1/1973 - 31/12/2015. \r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, their quality and completness cannot be guaranteed. Quality control flags and data values which have been removed in the quality control process are also provided along with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20151231_v1-0-4-2015p.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Climate of the Past\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 148, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 26024, "uuid": "e58fdade3a6c46bbaae7c53e948dd6d0", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): composite flux data for Icebreaker Oden", "abstract": "This dataset contains provides the final best estimates of fluxes, mean environmental variables and derived transfer coefficient estimates, along with asociated quality control flags, during the Icebreaker Oden voyage durning the Arctic Cloud Summer Expedition (ACSE) in summer 2014. These were calculated based on instrumentation data from the University of Leeds' Metek sonic anemometer, Licor LI-7500 gas analyzer and XSENS MTi-G-700 motion pack, plus mean surface meteorology data provided from the automatic weather station operated on board by the Department of Meteorology, Stockholm University (MISU).\r\n\r\nOther data from the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements.\r\n\r\nThe document \"ACSE_turbulent_fluxes_readme.txt\" in the archive contains fuller details of the flux calculations. The final data, prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds, contain:\r\n\r\n1) The final quality controlled best estimates of 20-min averaged dynamic fluxes, associated mean environmental variables (10m wind, etc), transfer coefficients, and quality control flags.\r\n\r\n2) The raw kinematic fluxes, etc that go into generating (1), along with the quality control variables used in generating the QC flags, and the QC flags.\r\n\r\n3) Other environmental variables (in some cases with duplicates from multiple different sensors) averaged onto the same time base as the flux estimates.\r\n\r\nThe authors note that in all cases a lot of work has been done on quality control and applying suitable corrections to raw measurements. In many cases other choices could have been made, and additional QC measures may need to be applied.\r\n\r\nMost of the work on the flux data processing has been done by John Prytherch, with additional input from Ian Brooks and Dominic Salisbury. Additional work on ancillary data was undertaken by other members of the ACSE science team." }, "objectObservation": { "ob_id": 25951, "uuid": "bfa8b9457e1a4cb6aa506316295564b2", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): turbulence wind and sonic temperature measurements from the University of Leeds Metek USA-100 sonic anemometer on board Icebreaker Oden", "abstract": "This dataset contains turbulent winds and sonic temperature measurements by the University of Leeds' Metek USA-100 sonic anemometer during the Arctic Cloud Summer Expedition (ACSE). The ACSE cruise took place in the Arctic during summer 2014. These data were obtained to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\nMeasurements were made at 20Hz from which 20-minute average fluxes were then derived.\r\n\r\nThe sonic anemometer was located on the foremast of the Icebreaker Oden ship at 20.58 m above the waterline. Data here includes the raw measurements and fully corrected turbulent winds (motion correction, flow distortion correction, etc), along with sonic temperature. For details of motion and flow distortion see the linked documentation. \r\n\r\nNote that while the Metek anemometer uses a left-handed reference frame, all measurements have been transformed to a right-handed frame here. The anemometer x-axis was rotated 30 deg anticlockwise from ship bow.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements." } }, { "ob_id": 149, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 26024, "uuid": "e58fdade3a6c46bbaae7c53e948dd6d0", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): composite flux data for Icebreaker Oden", "abstract": "This dataset contains provides the final best estimates of fluxes, mean environmental variables and derived transfer coefficient estimates, along with asociated quality control flags, during the Icebreaker Oden voyage durning the Arctic Cloud Summer Expedition (ACSE) in summer 2014. These were calculated based on instrumentation data from the University of Leeds' Metek sonic anemometer, Licor LI-7500 gas analyzer and XSENS MTi-G-700 motion pack, plus mean surface meteorology data provided from the automatic weather station operated on board by the Department of Meteorology, Stockholm University (MISU).\r\n\r\nOther data from the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements.\r\n\r\nThe document \"ACSE_turbulent_fluxes_readme.txt\" in the archive contains fuller details of the flux calculations. The final data, prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds, contain:\r\n\r\n1) The final quality controlled best estimates of 20-min averaged dynamic fluxes, associated mean environmental variables (10m wind, etc), transfer coefficients, and quality control flags.\r\n\r\n2) The raw kinematic fluxes, etc that go into generating (1), along with the quality control variables used in generating the QC flags, and the QC flags.\r\n\r\n3) Other environmental variables (in some cases with duplicates from multiple different sensors) averaged onto the same time base as the flux estimates.\r\n\r\nThe authors note that in all cases a lot of work has been done on quality control and applying suitable corrections to raw measurements. In many cases other choices could have been made, and additional QC measures may need to be applied.\r\n\r\nMost of the work on the flux data processing has been done by John Prytherch, with additional input from Ian Brooks and Dominic Salisbury. Additional work on ancillary data was undertaken by other members of the ACSE science team." }, "objectObservation": { "ob_id": 25950, "uuid": "a3e0f0848a144484bdea833ef01ec834", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): carbon dioxide and water vapour measurements from the University of Leeds LI-COR Li-7500 gas analyzer on board Icebreaker Oden", "abstract": "This dataset contains carbon dioxide and water vapour concentration measurements from the University of Leeds' LI-COR LI-7500 open path gas analyser mounted on board the Swedish Icebreaker Oden durning Arctic Cloud Summer Expedition (ACSE). ACSE took place in the Arctic during summer 2014. These measurements were used to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\nThe instrument's sensing head was located on the foremast of Icebreaker Oden, approximately 1 m forward of the sonic anemometer. Note the LiCOR LI-7500 CO2 data are generally not suitable for flux measurements at sea. Only the water vapour signal has been used for flux analysis.\r\n\r\nData times were truncated to match those from the sonic anemometer and the internal lag was corrected for.\r\n\r\nUsers should also note that the instrument's temperature and pressure measurements are made inside the interface box. Temperature is thus likely to be high due to solar heating of box, and pressure will be biased low (box is ~3 m below sensor) and may be subject to dynamic pressure fluctuations resulting from airflow around pressure inlet.\r\n\r\nMeasurements are made at 20 Hz frequency.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements." } }, { "ob_id": 150, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 26024, "uuid": "e58fdade3a6c46bbaae7c53e948dd6d0", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): composite flux data for Icebreaker Oden", "abstract": "This dataset contains provides the final best estimates of fluxes, mean environmental variables and derived transfer coefficient estimates, along with asociated quality control flags, during the Icebreaker Oden voyage durning the Arctic Cloud Summer Expedition (ACSE) in summer 2014. These were calculated based on instrumentation data from the University of Leeds' Metek sonic anemometer, Licor LI-7500 gas analyzer and XSENS MTi-G-700 motion pack, plus mean surface meteorology data provided from the automatic weather station operated on board by the Department of Meteorology, Stockholm University (MISU).\r\n\r\nOther data from the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements.\r\n\r\nThe document \"ACSE_turbulent_fluxes_readme.txt\" in the archive contains fuller details of the flux calculations. The final data, prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds, contain:\r\n\r\n1) The final quality controlled best estimates of 20-min averaged dynamic fluxes, associated mean environmental variables (10m wind, etc), transfer coefficients, and quality control flags.\r\n\r\n2) The raw kinematic fluxes, etc that go into generating (1), along with the quality control variables used in generating the QC flags, and the QC flags.\r\n\r\n3) Other environmental variables (in some cases with duplicates from multiple different sensors) averaged onto the same time base as the flux estimates.\r\n\r\nThe authors note that in all cases a lot of work has been done on quality control and applying suitable corrections to raw measurements. In many cases other choices could have been made, and additional QC measures may need to be applied.\r\n\r\nMost of the work on the flux data processing has been done by John Prytherch, with additional input from Ian Brooks and Dominic Salisbury. Additional work on ancillary data was undertaken by other members of the ACSE science team." }, "objectObservation": { "ob_id": 25955, "uuid": "f2f4675203d04e4eb269e230633d03db", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): high resolution ship motion data from the University of Leeds XSENS MTi-G-700 attitude and heading reference system on board Icebreaker Oden", "abstract": "This dataset contains high resolution attitude and motion measurements of the Icebreaker Oden ship's motion by the University of Leeds' XSENS MTi-G-700 attitude and heading reference system during the Arctic Cloud Summer Expedition (ACSE). The ACSE cruise took place in the Arctic during summer 2014. These data were obtained to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (Swedish-Russian-US Investigation) holdings - see online resources linked to this record.\r\n\r\nMeasurements were made at 40Hz for inertial measurements and 4Hz for GPS measurements. Though the inertial measurements were used at 20Hz when merged with sonic anemometer, 20 minute final fluxes (see related data within the parent data collection).\r\n\r\nThe XSens MTi-G-700 measures 3-axis accelerations, rotation rates, and magnetic field components as well as GPS position. Internal algorithm calculate 3-axis velocity, tilt angles and heading. However, users of these data should note that it is often not possible to calibrate magnetic field for local platform induced distortions (soft iron and hard iron corrections) resulting in errors in magnetic field and calculated outputs. Additionally, the heading measurements were found to unreliable on the ship.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF)." } }, { "ob_id": 151, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 26024, "uuid": "e58fdade3a6c46bbaae7c53e948dd6d0", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): composite flux data for Icebreaker Oden", "abstract": "This dataset contains provides the final best estimates of fluxes, mean environmental variables and derived transfer coefficient estimates, along with asociated quality control flags, during the Icebreaker Oden voyage durning the Arctic Cloud Summer Expedition (ACSE) in summer 2014. These were calculated based on instrumentation data from the University of Leeds' Metek sonic anemometer, Licor LI-7500 gas analyzer and XSENS MTi-G-700 motion pack, plus mean surface meteorology data provided from the automatic weather station operated on board by the Department of Meteorology, Stockholm University (MISU).\r\n\r\nOther data from the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements.\r\n\r\nThe document \"ACSE_turbulent_fluxes_readme.txt\" in the archive contains fuller details of the flux calculations. The final data, prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds, contain:\r\n\r\n1) The final quality controlled best estimates of 20-min averaged dynamic fluxes, associated mean environmental variables (10m wind, etc), transfer coefficients, and quality control flags.\r\n\r\n2) The raw kinematic fluxes, etc that go into generating (1), along with the quality control variables used in generating the QC flags, and the QC flags.\r\n\r\n3) Other environmental variables (in some cases with duplicates from multiple different sensors) averaged onto the same time base as the flux estimates.\r\n\r\nThe authors note that in all cases a lot of work has been done on quality control and applying suitable corrections to raw measurements. In many cases other choices could have been made, and additional QC measures may need to be applied.\r\n\r\nMost of the work on the flux data processing has been done by John Prytherch, with additional input from Ian Brooks and Dominic Salisbury. Additional work on ancillary data was undertaken by other members of the ACSE science team." }, "objectObservation": { "ob_id": 25979, "uuid": "0af02f899ac14c218706295d3c6d1c4d", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): surface meteorology and radiation measurements from the Stockholm University's automatic weather station on board Icebreaker Oden", "abstract": "This dataset contains surface meteorological measurements including air temperature, relative humidity, surface irradiation and wind measurements from the Meteorologiska Institutionen Stockholms Universitet (MISU) weather station on board the Swedish Icebreaker Oden durning Arctic Cloud Summer Expedition (ACSE). ACSE took place in the Arctic during summer 2014. These measurements were used to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\nThese data came from an automatic weather station installed on the 7th deck of the Icebreaker Oden, approximately 25m above the surface, measuring at 1 Hz frequency. The system was operated by Joe Sedlar who also undertook data quality control and there are several flag variables for T/RH and radiation measurements documenting known data issues - notably when primary measurements have been replaced with those from other sensors, or corrections applied. This version of the dataset was then prepared for archiving with the Centre for Environmental Data Analysis by Ian Brooks, University of Leeds.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements." } }, { "ob_id": 152, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 26021, "uuid": "da84490d169246d381f59bccdfd143f1", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): composite ship motion data for Icebreaker Oden from on board ship navigation data and University of Leeds' XSens motion sensor package.", "abstract": "This dataset contains combined measurements of platform motion and final velocity and attitude corrections for turbulence measurements on the foremast of Icebreaker Oden utilising data from the ship's navigation unit alongside data from the University of Leeds' XSens motion and heading sensory package. These data include ship speed over group, course, heading etc, for Icebreaker Oden durning Arctic Cloud Summer Expedition (ACSE). ACSE took place in the Arctic during summer 2014. These measurements were used to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\nThe XSens motion pack was mounted at the base of the sonic anemometer, in the same reference frame (rotated 30 deg to port from bow). Rotation angles given in the dataset are with respect to the earth frame, with x-axis positive to east. \r\n\r\nCorrections combine high rate data from the Xsens package with low rate data from the ship navigation system (heading and speed) to derive the full earth-relative platform motion at 20Hz. The motion calculation follows Edson et al. (1988) and Prytherch et al. (2015) - see linked documentation.\r\n\r\nThese data are provided as supportive data for use with the other datasets within this collection, helping to account for ship movement during the expedition for later data analysis. These data were prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements." }, "objectObservation": { "ob_id": 25955, "uuid": "f2f4675203d04e4eb269e230633d03db", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): high resolution ship motion data from the University of Leeds XSENS MTi-G-700 attitude and heading reference system on board Icebreaker Oden", "abstract": "This dataset contains high resolution attitude and motion measurements of the Icebreaker Oden ship's motion by the University of Leeds' XSENS MTi-G-700 attitude and heading reference system during the Arctic Cloud Summer Expedition (ACSE). The ACSE cruise took place in the Arctic during summer 2014. These data were obtained to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (Swedish-Russian-US Investigation) holdings - see online resources linked to this record.\r\n\r\nMeasurements were made at 40Hz for inertial measurements and 4Hz for GPS measurements. Though the inertial measurements were used at 20Hz when merged with sonic anemometer, 20 minute final fluxes (see related data within the parent data collection).\r\n\r\nThe XSens MTi-G-700 measures 3-axis accelerations, rotation rates, and magnetic field components as well as GPS position. Internal algorithm calculate 3-axis velocity, tilt angles and heading. However, users of these data should note that it is often not possible to calibrate magnetic field for local platform induced distortions (soft iron and hard iron corrections) resulting in errors in magnetic field and calculated outputs. Additionally, the heading measurements were found to unreliable on the ship.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF)." } }, { "ob_id": 153, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 26021, "uuid": "da84490d169246d381f59bccdfd143f1", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): composite ship motion data for Icebreaker Oden from on board ship navigation data and University of Leeds' XSens motion sensor package.", "abstract": "This dataset contains combined measurements of platform motion and final velocity and attitude corrections for turbulence measurements on the foremast of Icebreaker Oden utilising data from the ship's navigation unit alongside data from the University of Leeds' XSens motion and heading sensory package. These data include ship speed over group, course, heading etc, for Icebreaker Oden durning Arctic Cloud Summer Expedition (ACSE). ACSE took place in the Arctic during summer 2014. These measurements were used to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\nThe XSens motion pack was mounted at the base of the sonic anemometer, in the same reference frame (rotated 30 deg to port from bow). Rotation angles given in the dataset are with respect to the earth frame, with x-axis positive to east. \r\n\r\nCorrections combine high rate data from the Xsens package with low rate data from the ship navigation system (heading and speed) to derive the full earth-relative platform motion at 20Hz. The motion calculation follows Edson et al. (1988) and Prytherch et al. (2015) - see linked documentation.\r\n\r\nThese data are provided as supportive data for use with the other datasets within this collection, helping to account for ship movement during the expedition for later data analysis. These data were prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements." }, "objectObservation": { "ob_id": 26010, "uuid": "d23f1cdf560f4ae185d1e008d1eef4b7", "short_code": "ob", "title": "Arctic Cloud Summer Expedition (ACSE): SPRS Icebreaker Oden ship navigation data", "abstract": "This dataset contains ship navigation data, including speed over group, course, heading etc, fomr the Swedish Polar Research Secretariat's (SPRS) Icebreaker Oden durning Arctic Cloud Summer Expedition (ACSE). ACSE took place in the Arctic during summer 2014. These measurements were used to complement a suite of other observations taken during the cruise. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the NOAA ACSE and The Bolin Centre for Climate Research SWERUS (SWEdish-Russian-US) holdings - see online resources linked to this record.\r\n\r\nThese data are provided as supportive data for use with the other datasets within this collection, helping to account for ship movement during the expedition for later data analysis. These data were prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Ian Brooks, University of Leeds.\r\n\r\nThe Arctic Cloud Summer Expedition (ACSE) was a collaboration between the University of Leeds, the University of Stockholm, and NOAA-CIRES. ACSE aimed to study the response of Arctic boundary layer cloud to changes in surface conditions in the Arctic Ocean as a working package of the larger Swedish-Russian-US Investigation of Climate, Cryosphere and Carbon interaction (SWERUS-C3) Expedition in Summer 2014. This expedition was a core component to the overall SWERUS-C3 programme and was supported by the Swedish Polar Research Secretariat.\r\n\r\nACSE took place during a 3-month cruise of the Swedish Icebreaker Oden from Tromso, Norway to Barrow, Alaska and back over the summer of 2014. During this cruise ACSE scientists measured surface turbulent exchange, boundary layer structure, and cloud properties. Many of the measurements used remote sensing approaches - radar, lidar, and microwave radiometers - to retrieve vertical profiles of the dynamic and microphysical properties of the lower atmosphere and cloud.\r\n\r\nThe UK participation of ACSE was funded by the Natural Environment Research Council (NERC, grant: NE/K011820/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF). This dataset collection contains data mainy from the UK contribution with some additional data from other institutes also archived to complement the suite of meteorological measurements." } }, { "ob_id": 154, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25058, "uuid": "5f75fcb0c58740d99b07953797bc041e", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 50km grid spacing, version 2.1", "abstract": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing. This version of the product is v2.1, which is an extension of the version 2.0 Sea_Ice_cci dataset and has identical data until 2015-12-25.\r\n\r\nThis product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.\r\n\r\nA SIC CDR at 25km grid spacing is also available." }, "objectObservation": { "ob_id": 20367, "uuid": "8a45e78c4657438997617c726337dffc", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 50 km grid spacing, version 2.0", "abstract": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data (AMSR-E and AMSR-2). It is processed with an algorithm using coarse resolution (6 GHz and 37 GHz) imaging channels, and has been gridded at 50km grid spacing.\r\n\r\nThis product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea_Ice_CCI project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.\r\n\r\nA SIC CDR at 25km grid spacing is also available (doi: 10.5285/c61bfe88-873b-44d8-9b0e-6a0ee884ad95) and a 12.5km product is in preparation." } }, { "ob_id": 155, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25057, "uuid": "f17f146a31b14dfd960cde0874236ee5", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1", "abstract": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing. This version of the product is v2.1, which is an extension of the v2.0 Sea_Ice_cci data and has identical data until 2015-12-25.\r\n\r\nThis product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.\r\n\r\nA SIC CDR at 50 km grid spacing is also available." }, "objectObservation": { "ob_id": 20365, "uuid": "bc3f8a3cf94c4d0180a60b7e2bfd9c5b", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.0", "abstract": "The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data (AMSR-E and AMSR-2). It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing.\r\n\r\nThis product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.\r\n\r\nA SIC CDR at 50 km grid spacing is also available (doi:10.5285/70f611b0-ba82-48e6-9190-a62cf9f925f2) and a 12.5km product is in preparation." } }, { "ob_id": 156, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 26140, "uuid": "bf4fb57ac7f9461db27dab77c8c97cf2", "short_code": "ob", "title": "Forcing files for the ECMWF Integrated Forecasting System (IFS) Single Column Model (SCM) over Indian Ocean/Tropical Pacific derived from a 10-day high resolution simulation", "abstract": "This data set consisting of initial conditions, boundary conditions and forcing profiles for the Single Column Model (SCM) version of the European Centre for Medium-range Weather Forecasts (ECMWF) model, the Integrated Forecasting System (IFS). The IFS SCM is freely available through the OpenIFS project, on application to ECMWF for a licence. The data were produced and tested for IFS CY40R1, but will be suitable for earlier model cycles, and also for future versions assuming no new boundary fields are required by a later model. The data are archived as single time-stamp maps in netCDF files. If the data are extracted at any lat-lon location and the desired timestamps concatenated (e.g. using netCDF operators), the resultant file is in the correct format for input into the IFS SCM. \r\n\r\nThe data covers the Tropical Indian Ocean/Warm Pool domain spanning 20S-20N, 42-181E. The data are available every 15 minutes from 6 April 2009 0100 UTC for a period of ten days. The total number of grid points over which an SCM can be run is 480 in the longitudinal direction, and 142 latitudinally. With over 68,000 independent grid points available for evaluation of SCM simulations, robust statistics of bias can be estimated over a wide range of boundary and climatic conditions. \r\n \r\nThe initial conditions and forcing profiles were derived by coarse-graining high resolution (4 km) simulations produced as part of the NERC Cascade project, dataset ID xfhfc (also available on CEDA). The Cascade dataset is archived once an hour. The dataset was linearly interpolated in time to produce the 15-minute resolution required by the SCM. The resolution of the coarse-grained data corresponds to the IFS T639 reduced gaussian grid (approx 32 km). The boundary conditions are as used in the operational IFS at resolution T639. The coarse graining procedure by which the data were produced is detailed in Christensen, H. M., Dawson, A. and Holloway, C. E., 'Forcing Single Column Models using High-resolution Model Simulations', in review, Journal of Advances in Modeling Earth Systems (JAMES).\r\n \r\nFor full details of the parent Cascade simulation, see Holloway et al (2012). In brief, the simulations were produced using the limited-area setup of the MetUM version 7.1 (Davies et al, 2005). The model is semi-Lagrangian and non-hydrostatic. Initial conditions were specified from the ECMWF operational analysis. A 12 km parametrised convection run was first produced over a domain 1 degree larger in each direction, with lateral boundary conditions relaxed to the ECMWF operational analysis. The 4 km run was forced using lateral boundary conditions computed from the 12 km parametrised run, via a nudged rim of 8 model grid points. The model has 70 terrain-following hybrid levels in the vertical, with vertical resolution ranging from tens of metres in the boundary layer, to 250 m in the free troposphere, and with model top at 40 km. The time step was 30 s.\r\n \r\nThe Cascade dataset did not include archived soil variables, though surface sensible and latent heat fluxes were archived. When using the dataset, it is therefore recommended that the IFS land surface scheme be deactivated and the SCM forced using the surface fluxes instead. The first day of Cascade data exhibited evidence of spin-up. It is therefore recommended that the first day be discarded, and the data used from April 7 - April 16.\r\n \r\nThe software used to produce this dataset are freely available to interested users;\r\n 1. \"cg-cascade\"; NCL software to produce OpenIFS forcing fields from a high-resolution MetUM simulation and necessary ECMWF boundary files.\r\n https://github.com/aopp-pred/cg-cascade\r\n Furthermore, software to facilitate the use of this dataset are also available, consisting of;\r\n 2. \"scmtiles\"; Python software to deploy many independent SCMs over a domain. \r\n https://github.com/aopp-pred/scmtiles\r\n 3. \"openifs-scmtiles\"; Python software to deploy the OpenIFS SCM using scmtiles.\r\n https://github.com/aopp-pred/openifs-scmtiles\r\n " }, "objectObservation": { "ob_id": 14757, "uuid": "414a7c22a8e3486591b5e6eaf1a24377", "short_code": "ob", "title": "Cascade: Warm Pool 4km xfhfc model run data", "abstract": "Cascade was a NERC funded consortium project to study organized convection and scale interactions in the tropical atmosphere using large domain cloud system resolving model simulations. The xfhfc simulation was made using the Met Office Unified Model (UM) at 4km resolution over the domain 40E-183E, 22S-22N which encompasses the Indian Ocean West Pacific Warm Pool. Cascade Warm Pool simulations coincide with the Year of Tropical Convection.\r\n\r\nThis dataset contains Warm Pool 4km model measurements from xfhfc run. " } }, { "ob_id": 157, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 24712, "uuid": "c657ee46354d480b8cf668addf0b43f2", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 03.2", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2,. and SMOS satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v03.2 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2015-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 14403, "uuid": "663a557e848a4a9f8f0d205c6b3cb7f6", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 02.2", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v02.2 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2014-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" } }, { "ob_id": 160, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 24714, "uuid": "71e20f6a7d6e4ec392f9afcbce14eced", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 03.2", "abstract": "The Soil Moisture CCI 'Passive' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v03.2 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2015-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 14401, "uuid": "953545f5c4454952a26db065bbca004f", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 02.2", "abstract": "The Soil Moisture CCI 'Passive' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v02.2 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2014-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version or the paper by Wagner 2012, both available in the documentation section. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" } }, { "ob_id": 162, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 24715, "uuid": "7c7a38b2d2ce448b99194bff85a85248", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 03.2", "abstract": "The Soil Moisture CCI 'Combined' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these being respectively fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satelllite instruments. \r\n\r\nThe product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2015-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. An overview of all known errors associated with it is provided in the Comprehensive Error Characterization Report. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 14399, "uuid": "c89cb1c86f42456bb84e49ea06621c7e", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 02.2", "abstract": "The Soil Moisture CCI 'Combined' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these being respectively fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. \r\n\r\nThe v02.2 product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2014-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. An overview of all known errors associated with it is provided in the Comprehensive Error Characterization Report. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" } }, { "ob_id": 164, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 24841, "uuid": "c4f117ba38544e8a80338b6cf1000a91", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.2", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v03.2 Soil Moisture CCI data.\r\n\r\nThe \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 14397, "uuid": "33ac39755cad49e38e34b048678a67aa", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 02.2", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v02.2 Soil Moisture CCI data.\r\n\r\nFor further information on these and the references associated with them please see the Product Specification Document (PSD), a link to which is provided in the documentation section. The \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website or within the Product Specification Document." } }, { "ob_id": 165, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14399, "uuid": "c89cb1c86f42456bb84e49ea06621c7e", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 02.2", "abstract": "The Soil Moisture CCI 'Combined' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these being respectively fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. \r\n\r\nThe v02.2 product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2014-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. An overview of all known errors associated with it is provided in the Comprehensive Error Characterization Report. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" }, "objectObservation": { "ob_id": 12886, "uuid": "2cc1cbc906444322845e6a51916a1f03", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 02.1", "abstract": "The Combined Soil Moisture CCI dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these both being fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. \r\n\r\nThe v02.1 product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2013-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. An overview of all known errors associated with it is provided in the Comprehensive Error Characterization Report. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" } }, { "ob_id": 166, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26173, "uuid": "0f4570c780ba41b19a362e774509c883", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 03.3", "abstract": "The Soil Moisture CCI 'Combined' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these being respectively fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. \r\n\r\nThe v03.3 Combined product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2016-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 24715, "uuid": "7c7a38b2d2ce448b99194bff85a85248", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 03.2", "abstract": "The Soil Moisture CCI 'Combined' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these being respectively fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satelllite instruments. \r\n\r\nThe product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2015-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. An overview of all known errors associated with it is provided in the Comprehensive Error Characterization Report. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 167, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26232, "uuid": "7f320bf20d9e4c7994031c3b0a2170aa", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 03.3", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, and SMOS satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v03.3 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2016-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 24712, "uuid": "c657ee46354d480b8cf668addf0b43f2", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 03.2", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2,. and SMOS satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v03.2 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2015-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 168, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26234, "uuid": "f77cbcfbb6214448aebaa2119d829692", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 04.2", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, and SMOS satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v04.2 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2016-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 26232, "uuid": "7f320bf20d9e4c7994031c3b0a2170aa", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 03.3", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, and SMOS satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v03.3 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2016-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 170, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26237, "uuid": "5644c9ab30ef499ba1cc9ecfc6b6474c", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 03.3", "abstract": "The Soil Moisture CCI 'Passive' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v03.3 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2016-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 24714, "uuid": "71e20f6a7d6e4ec392f9afcbce14eced", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 03.2", "abstract": "The Soil Moisture CCI 'Passive' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v03.2 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2015-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 171, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26239, "uuid": "a4f9546935a644d3b3260b7f6a0a183f", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 04.2", "abstract": "The Soil Moisture CCI 'Passive' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v04.2 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2016-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 26237, "uuid": "5644c9ab30ef499ba1cc9ecfc6b6474c", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 03.3", "abstract": "The Soil Moisture CCI 'Passive' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v03.3 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2016-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 172, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26242, "uuid": "0869e3b34fa4465a911e2588396ff1ec", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 04.2", "abstract": "The Soil Moisture CCI 'Combined' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these being respectively fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. \r\n\r\nThe v04.2 Combined product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2016-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 26173, "uuid": "0f4570c780ba41b19a362e774509c883", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Combined' Product, Version 03.3", "abstract": "The Soil Moisture CCI 'Combined' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by merging the \"Active\" and \"Passive\" datasets which were created for the project, these being respectively fusions of scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. \r\n\r\nThe v03.3 Combined product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2016-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 173, "relationType": "IsIdenticalTo", "subjectObservation": { "ob_id": 26245, "uuid": "1d28a3d5d74d4439a2be8938dfb550f8", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.3", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v03.3 Soil Moisture CCI data.\r\n\r\nThe \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using the complete three references as follows:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 24841, "uuid": "c4f117ba38544e8a80338b6cf1000a91", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.2", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v03.2 Soil Moisture CCI data.\r\n\r\nThe \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 174, "relationType": "IsIdenticalTo", "subjectObservation": { "ob_id": 26247, "uuid": "55bff4add65d489e86c195edbae8f970", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 04.2", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v04.2 Soil Moisture CCI data.\r\n\r\nThe \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 26245, "uuid": "1d28a3d5d74d4439a2be8938dfb550f8", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.3", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v03.3 Soil Moisture CCI data.\r\n\r\nThe \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using the complete three references as follows:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 175, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14403, "uuid": "663a557e848a4a9f8f0d205c6b3cb7f6", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 02.2", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v02.2 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2014-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" }, "objectObservation": { "ob_id": 12878, "uuid": "515df65d974541df82740062974212aa", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Active' Product, Version 02.1", "abstract": "The Soil Moisture CCI 'Active' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. 'Passive' and 'Combined' products have also been created. The 'Passive' product is a fusion of radiometer data acquired by the SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. The 'Combined Product' is then a blended product based on the former two data sets.\r\n\r\nThe v02.1 Active product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2013-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version 2.0 or the paper by Wagner 2012, both available in the documentation section. An overview of all known errors of the dataset is provided in the Comprehensive Error Characterization Report. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" } }, { "ob_id": 176, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14401, "uuid": "953545f5c4454952a26db065bbca004f", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 02.2", "abstract": "The Soil Moisture CCI 'Passive' dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v02.2 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2014-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version or the paper by Wagner 2012, both available in the documentation section. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" }, "objectObservation": { "ob_id": 12881, "uuid": "a1ff285fb94c4c359269b1a12df957ec", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): 'Passive' Product, Version 02.1", "abstract": "The Soil Moisture CCI Passive dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) CCI project. The product has been created by fusing scatterometer and radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. 'Active' and 'Combined' products have also been created, the 'Active' product being a fusion of AMI-WS and ASCAT derived scatterometer and radiometer soil moisture products and the 'Combined Product' being a blended product based on the former two data sets. \r\n\r\nThe v02.1 Passive product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2013-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document version or the paper by Wagner 2012, both available in the documentation section. An overview of all known errors of the dataset is provided in the Comprehensive Error Characterization Report. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three following references:\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321" } }, { "ob_id": 177, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14397, "uuid": "33ac39755cad49e38e34b048678a67aa", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 02.2", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v02.2 Soil Moisture CCI data.\r\n\r\nFor further information on these and the references associated with them please see the Product Specification Document (PSD), a link to which is provided in the documentation section. The \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website or within the Product Specification Document." }, "objectObservation": { "ob_id": 12890, "uuid": "4eec83c4bbec4c8ea873188d90fc243f", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 02.1", "abstract": "Ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v02.1 Soil Moisture CCI data.\r\n\r\nFor further information on these and the references associated with them please see the Product Specification Document (PSD), a link to which is provided in linked documentation. The \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, and AMSR2 instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website or within the Product Specification Document." } }, { "ob_id": 178, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14759, "uuid": "20923d5a6a194340a94dcf4feda018a6", "short_code": "ob", "title": "Met Office surface measurements from Cardington, Bedfordshire Version 1 (2006 to 2017)", "abstract": "The Met Office's research unit based in Cardington, Bedfordshire, study boundary-layer meteorology and surface processes to help with the development of numerical weather prediction methods. Surface meteorological data and high resolution radiosonde data are collected from the Met Office's research site and elsewhere. \r\n\r\nThe dataset contains recorded surface meteorology and radiation measurements timed at 1, 10 and 30 minute intervals and measured by instruments mounted on 10, 25 and 50 metre masts." }, "objectObservation": { "ob_id": 2432, "uuid": "256800197acdc258344ca7421d218996", "short_code": "ob", "title": "Met Office surface measurements from Cardington, Bedfordshire Version 0 (2004 - 2009)", "abstract": "The Met Office's research unit based in Cardington, Bedfordshire, study boundary-layer meteorology and surface processes to help with the development of numerical weather prediction methods. Surface meteorological data and high resolution radiosonde data are collected from the Met Office's research site and elsewhere. \r\n\r\nThe dataset contains recorded surface meteorology and radiation measurements timed at 1, 10 and 30 minute intervals and measured by instruments mounted on 10, 25 and 50 metre masts." } }, { "ob_id": 179, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19880, "uuid": "76ad6afa787d4c469122f0b472a988c0", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 2.0.", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 2.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). \r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, this dataset has been superseded. Later version of the data are now available." }, "objectObservation": { "ob_id": 13363, "uuid": "a70b70e7635342cdaa4451c17fd4bd87", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global ocean colour data products gridded on a sinusoidal projection (All Products), Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 1.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a daily time resolution.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)" } }, { "ob_id": 180, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19892, "uuid": "a897196a8e2b4c30ab8d22dbfe8f98c7", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 2.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data are also available as monthly climatologies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." }, "objectObservation": { "ob_id": 13333, "uuid": "0f29b5f7ca774bd590a2ade5f94e9ddb", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global ocean colour data products gridded on a geographic projection (All Products), Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 1.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a daily time resolution.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n" } }, { "ob_id": 181, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19889, "uuid": "3ba980b6cfba4bb48a5fe9c4efdeffe9", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 2.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites). Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." }, "objectObservation": { "ob_id": 13344, "uuid": "a526fdfb91954f1ab4360978e86f3b2b", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global chlorophyll-a data products gridded on a geographic projection, Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 1.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at a daily time resolution. Note, this dataset is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n" } }, { "ob_id": 182, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19888, "uuid": "a2cd1cefc5b84b86bbaa09bb3832e497", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 2.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, this the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." }, "objectObservation": { "ob_id": 13346, "uuid": "45278227a3804ab2a601fa2d3b1ec2fd", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 1.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a daily time resolution. Note, this dataset is also included in the 'All Products' dataset.\r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n" } }, { "ob_id": 183, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19887, "uuid": "49bcb6f29c824ae49e41d2d3656f11be", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 2.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 2.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." }, "objectObservation": { "ob_id": 13349, "uuid": "06e4e74e2cb24ec582cdce05e2ff1c87", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 1.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a daily time resolution. It is computed from the Ocean Colour CCI Version 1 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, this dataset is also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n" } }, { "ob_id": 184, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19886, "uuid": "aaf1d54282e94d5483356521f1b76434", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 2.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, this dataset has been superseded. Later version of the data are now available." }, "objectObservation": { "ob_id": 13351, "uuid": "70be18893edb498785e22bed288cfd54", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global remote sensing reflectance gridded on a geographic projection, Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 1.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a daily time resolution. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n" } }, { "ob_id": 185, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19885, "uuid": "8b087afe9d53471ea98ffa092867d289", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 2.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, this dataset has been superseded. Later version of the data are now available." }, "objectObservation": { "ob_id": 13353, "uuid": "a456b6c4c290453d8bb436e45e616f78", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global remote sensing reflectance gridded on a sinusoidal projection, Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 1.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a daily time resolution. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n" } }, { "ob_id": 186, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19884, "uuid": "b548475d0a5d4a2b8de40e7b1fa40d7a", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 2.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 2.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." }, "objectObservation": { "ob_id": 13355, "uuid": "5ce3092e823a403dad8122ff8ec93612", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 1.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a daily time resolution. It is computed from the Ocean Colour CCI Version 1 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, this dataset is also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n" } }, { "ob_id": 187, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19883, "uuid": "7852b8af4bda446ab12290b7b106cc3c", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 2.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 2.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." }, "objectObservation": { "ob_id": 13357, "uuid": "f7d1890865bb47b383a013cd9fede042", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean Colour CCI): Daily global chlorophyll-a data products gridded on a sinusoidal projection, Version 1.0", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 1.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a daily time resolution. Note, this dataset is also included in the 'All Products' dataset.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n" } }, { "ob_id": 189, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 24692, "uuid": "9ed2813d2eda4d958e92ab3ce1ab1fe6", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2", "abstract": "The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4.\r\n\r\nThe ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). \r\n\r\nFor further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR)." }, "objectObservation": { "ob_id": 14581, "uuid": "ccd65d303e7241f6b969b19b4be6a925", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Merged CH4 Level 2 Data Product (CH4_EMMA), version 1.0, generated with the EMMA algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG), the XCH4 EMMA product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced by applying the ensemble median algorithm EMMA to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). \r\n\r\nFor further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR) in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 193, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 25059, "uuid": "b241a7f536a244749662360bd7839312", "short_code": "ob", "title": "S-RIP: Zonal-mean dynamical variables of global atmospheric reanalyses on pressure levels", "abstract": "This dataset contains zonal-mean atmospheric diagnostics computed from reanalysis datasets on pressure levels. Primary variables include temperature, geopotential height, and the three-dimensional wind field. Advanced diagnostics include zonal covariance terms that can be used to compute, for instance, eddy kinetic energy and eddy fluxes. Terms from the primitive zonal-mean momentum equation and the transformed Eulerian momentum equation are also provided.\r\n\r\nThis dataset was produced to facilitate the comparison of reanalysis datasets for the collaborators of the SPARC- Reanalysis Intercomparison Project (S-RIP) project. The dataset is substantially smaller in size compared to the full three dimensional reanalysis fields and uses unified numerical methods. The dataset includes all global reanalyses available at the time of its development and will be extended to new reanalysis products in the future." }, "objectObservation": { "ob_id": 25384, "uuid": "70146c789eda4296a3c3ab6706931d56", "short_code": "ob", "title": "S-RIP: Zonal-mean heating rates of global atmospheric reanalyses on pressure levels", "abstract": "This dataset contains zonal-mean model-generated and diagnosed heating rates as potential temperature tendencies on pressure levels. The model-generated heating rates consist of total heating rates due to parameterized physics along with heating rates due to long-wave and short-wave radiative transfer, as generated during the model forecast step. The diagnosed heating rates are calculated from the zonal-mean atmospheric diagnostics (Zonal-mean reanalyses on pressure levels dataset) according to the zonal-mean thermodynamic equation. All heating rates are provided 6-hourly on identical horizontal and vertical grids as the dynamical variables included in Zonal-mean reanalyses on pressure levels dataset. However, the time axis of this dataset lags that of Zonal-mean reanalyses on pressure levels dataset by three hours.\r\n\r\nThis dataset was produced to facilitate the comparison of reanalysis datasets for the collaborators of the SPARC- Reanalysis Intercomparison Project (S-RIP). The dataset is substantially smaller in size compared to the full three dimensional reanalysis fields and uses unified numerical methods. The dataset includes all global reanalyses available at the time of its development and will be extended to new reanalysis products in the future." } }, { "ob_id": 194, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 26543, "uuid": "bdf2cf5a78554a73bf5e57a853e3bbc0", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Grounding Line Locations for the Ferringo, Pine Island, Thwaites, Smith, Kohler and Pope Glaciers, Antarctica, 1995-2017, v2.0 (CCI subset)", "abstract": "Grounding line locations (GLL) data for the Ferringo, Pine Island, Thwaites, Smith, Kohler and Pope Glaciers in Antarctica, produced by the ESA Antarctic Ice Sheet Climate Change Initiative (CCI) project. The grounding lines have been derived from satellite observations from the ERS-1/2 and Copernicus Sentinel-1 instruments, acquired in the period from 1995-2017.\r\n\r\nAn extended dataset of Grounding line locations for these Glaciers is available on the ENVEO CryoPortal (http://cryoportal.enveo.at/data/)" }, "objectObservation": { "ob_id": 24811, "uuid": "95809f64ed7f41329cadae3f41906b55", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Grounding Line Locations for the Ferringo, Pine Island, Thwaites, Smith, Kohler and Pope Glaciers, Antarctica, 1995-2017, v2.0", "abstract": "Grounding line locations (GLL) data for the Ferringo, Pine Island, Thwaites, Smith, Kohler and Pope Glaciers in Antarctica, produced by the ESA Antarctic Ice Sheet Climate Change Initiative (CCI) project. The grounding lines have been derived from satellite observations from the ERS-1/2 and Copernicus Sentinel-1 instruments, acquired in the period from 1995-2017." } }, { "ob_id": 196, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26434, "uuid": "58f00d8814064b79a0c49662ad3af537", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.1 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2020. This product supersedes the previously available MODIS v5.0 product. The v5.1 dataset was initially published for 2001-2017, and has later been periodically extended to include 2018 to 2022.\r\n\r\nThe Fire_cci v5.1 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v2.0.7 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation." }, "objectObservation": { "ob_id": 25112, "uuid": "9c666602b89e468493e1c907a4de62ff", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.0 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2016. This is the first time that MODIS 250m resolution images are used for global burned area (BA) mapping.\r\n\r\nThe Fire_cci v5.0 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v1.6.1 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation. \r\n\r\nPlease note, a new version of this product (v5.1) is now available." } }, { "ob_id": 197, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19674, "uuid": "3a3503a06f69429e8a4827592e23787e", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Burned Area Pixel Product Version 4.1", "abstract": "The ESA Fire Climate Change Initiative (CCI) dataset consists of maps of global burned areas for years 2005 to 2011, developed from satellite observations. The products are distributed as 6 continental tiles and are based upon spectral information from the Medium Resolution Imaging Spectrometer (MERIS), on board the ESA ENVISAT satellite and thermal information from the MODIS active fires product.\r\n\r\nThe Pixel product includes maps at 0.00277778-degree (approx. 300m) resolution. Burned area (BA) information is included in 3 layers: date of BA detection, the confidence level (a probability value estimating the confidence that a pixel is actually burned), and the land cover information as defined in the Land Cover CCI v1.6.1 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation." }, "objectObservation": { "ob_id": 12535, "uuid": "56224b6755a843298af463827e9832ae", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire CCI): Burned Area Pixel Product Version 3.1", "abstract": "The ESA Fire Climate Change Initiative (CCI) dataset consists of maps of global burned areas for years 2006 to 2008, developed from satellite observations. The products are distributed as 6 continental tiles and are based upon thermal information from MODIS active fires product and spectral information from the Medium Resolution Imaging Spectrometer (MERIS), on board the ENVISAT ESA satellite.\r\nThe Pixel product includes maps in raster format, at 300m resolution. Burned area (BA) information is included in 3 layers: date of BA detection, the land cover of the burned pixel and the confidence level, a probability value estimating the confidence that a pixel is actually burned.\r\n\r\nAll files are in standard zip compression format, each yearly compressed file holding a set of monthly compressed files. Files are in Geotiff format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carree projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire CCI product user guide in linked documentation." } }, { "ob_id": 198, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19672, "uuid": "fa493d62c2af4c5cb8e6e3c340cdbf0d", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Burned Area Grid Product Version 4.1", "abstract": "The ESA Fire Climate Change Initiative (CCI) dataset collection consists of maps of global burned areas for years 2005 to 2011, developed from satellite observations. The products are based upon spectral information from the Medium Resolution Imaging Spectrometer (MERIS), on board the ESA ENVISAT satellite, and thermal information from the MODIS active fires product.\r\n\r\nThe Grid product is derived from the Pixel product by summarising its burned area information into a regular grid covering the Earth for 15-day periods with 0.25 degree resolution. Information on burned area is included in 22 individual layers: sum of burned area, standard error, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land Cover CCI v1.6.1 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation." }, "objectObservation": { "ob_id": 12543, "uuid": "9821980dc18047f09b9113d44fc2c20b", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire CCI): Burned Area Grid Product Version 3.1", "abstract": "The ESA Fire Climate Change Initiative (CCI) dataset collection consists of maps of global burned areas for years 2006 to 2008, developed from satellite observations. The products are based upon thermal information from the MODIS active fires product and spectral information from the Medium Resolution Imaging Spectrometer (MERIS), on board the ENVISAT ESA satellite.\r\n\r\nThe Grid product is derived from the Pixel product by summarizing its burned area information into a regular grid covering the Earth for 15-day periods with 0.5 degree resolution. Information on burned area is included in 22 individual layers: sum of burned area, standard error, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Globcover (2005) product. For further information on the product and its format see the Fire CCI product user guide in linked documentation." } }, { "ob_id": 199, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25112, "uuid": "9c666602b89e468493e1c907a4de62ff", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.0 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2016. This is the first time that MODIS 250m resolution images are used for global burned area (BA) mapping.\r\n\r\nThe Fire_cci v5.0 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v1.6.1 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation. \r\n\r\nPlease note, a new version of this product (v5.1) is now available." }, "objectObservation": { "ob_id": 19674, "uuid": "3a3503a06f69429e8a4827592e23787e", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Burned Area Pixel Product Version 4.1", "abstract": "The ESA Fire Climate Change Initiative (CCI) dataset consists of maps of global burned areas for years 2005 to 2011, developed from satellite observations. The products are distributed as 6 continental tiles and are based upon spectral information from the Medium Resolution Imaging Spectrometer (MERIS), on board the ESA ENVISAT satellite and thermal information from the MODIS active fires product.\r\n\r\nThe Pixel product includes maps at 0.00277778-degree (approx. 300m) resolution. Burned area (BA) information is included in 3 layers: date of BA detection, the confidence level (a probability value estimating the confidence that a pixel is actually burned), and the land cover information as defined in the Land Cover CCI v1.6.1 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation." } }, { "ob_id": 200, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25111, "uuid": "f1c9c7aa210d4564bd61ed1a81d51130", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area developed from satellite observations. The MODIS Fire_cci v5.0 grid products described here are derived from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001 to 2016. This is the first time that MODIS 250m resolution images are used for global burned area (BA) mapping.\r\n\r\nThis dataset is a gridded product, derived from the MODIS Fire_cci v5.0 pixel product by summarising its burned area information into a regular grid covering the Earth for 15-day periods with 0.25 degree resolution. Information on burned area is included in 23 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land_Cover_cci v1.6.1 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation.\r\n\r\nPlease note, a new version of this dataset (v5.1) is now available." }, "objectObservation": { "ob_id": 19672, "uuid": "fa493d62c2af4c5cb8e6e3c340cdbf0d", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Burned Area Grid Product Version 4.1", "abstract": "The ESA Fire Climate Change Initiative (CCI) dataset collection consists of maps of global burned areas for years 2005 to 2011, developed from satellite observations. The products are based upon spectral information from the Medium Resolution Imaging Spectrometer (MERIS), on board the ESA ENVISAT satellite, and thermal information from the MODIS active fires product.\r\n\r\nThe Grid product is derived from the Pixel product by summarising its burned area information into a regular grid covering the Earth for 15-day periods with 0.25 degree resolution. Information on burned area is included in 22 individual layers: sum of burned area, standard error, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land Cover CCI v1.6.1 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation." } }, { "ob_id": 201, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26712, "uuid": "b8cbc75bfaa1414fa4431cff170a9e99", "short_code": "ob", "title": "High accuracy room temperature line lists for the isotopologues of carbon dioxide", "abstract": "This dataset contains room temperature spectral line lists for 13 isotopologues of CO2, which have been calculated as part of the NERC (Natural Environment Research Council) funded 'High accuracy line intensity for carbon dioxide' project. These high accuracy line lists were derived for use in remote sensing of CO2 in the atmosphere. The dataset has been calculated from a theoretical model using the AMES potential energy surface and an accurate ab initio dipole moment surface. Data is provided in HITRAN format." }, "objectObservation": { "ob_id": 12292, "uuid": "6d2cbe9f93404c5e9152f4f92ea6cbdb", "short_code": "ob", "title": "High accuracy line intensity data for carbon dioxide", "abstract": "High accuracy line intensity for carbon dioxide project was NERC (Natural Environment Research Council) funded. The aim of the project was to provide an accurate theoretical solution to the problem of CO2 line intensities based on the application of high accuracy, first principles quantum mechanical calculations for the intensities and experimental data for the line positions.\r\n\r\nThis dataset contains measurements of high accuracy line intensity for carbon dioxide." } }, { "ob_id": 202, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19855, "uuid": "fba8969ef8224c4cac3cbaca149aef8f", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Calving Front Locations, v2.0", "abstract": "The data set provides calving front locations of 28 major outlet glaciers of the Greenland Ice Sheet using ERS and ENVISAT and Sentinel-1 SAR data. A selected number of the glaciers have been sampled seasonally, whilst the rest are sampled annually.\r\n\r\nThe Calving Front Location (CFL) of outlet glaciers from ice sheets is a basic parameter for ice dynamic modelling, for computing the mass fluxes at the calving gate, and for mapping glacier area change. From the ice velocity at the calving front and the time sequence of Calving Front Locations the iceberg calving rate can be computed which is of relevance for estimating the export of ice mass to the ocean.\r\n\r\nThe CFL product is a collection of ESRI shapefile in latitude and longitude, WGS84 projection." }, "objectObservation": { "ob_id": 14262, "uuid": "70eae17a47e64c8597defcb0ed155dea", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Calving Front Locations, v1.1", "abstract": "The data set provides calving front locations of major outlet glaciers of the Greenland Ice Sheet from SAR data from various sensors, produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project. Version 1.1 of the dataset has been updated to include information from Sentinel 1 data.\r\n\r\nThe Calving Front Location (CFL) of outlet glaciers from ice sheets is a basic parameter for ice dynamic modelling, for computing the mass fluxes at the calving gate, and for mapping glacier area change. From the ice velocity at the calving front and the time sequence of Calving Front Locations the iceberg calving rate can be computed which is of relevance for estimating the export of ice mass to the ocean.\r\n\r\nThe calving front location has been derived by manual delineation based on SAR or optical satellite data. The CFL product is a collection of ESRI shapefile in latitude and longitude, on WGS84 projection. The basic data are vector line files (not polygons)." } }, { "ob_id": 203, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26642, "uuid": "41e9783d4caa447b99f653c065805579", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change from Cryosat-2, v2.2", "abstract": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2017.\r\n \r\nThe surface elevation change data are provided as 2-year means (2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, and 2016-2017), and five-year means are also provided (2011-2015, 2012-2016, 2013-2017), along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.\r\n\r\nThe algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012" }, "objectObservation": { "ob_id": 19985, "uuid": "a9f4876560234ded84ac87eb9d4853c6", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change from Cryosat-2, v2.0", "abstract": "This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2015.\r\n \r\nThe surface elevation change data are provided as 2-year means (2011-2012, 2012-2013, 2013-2014 and 2014-2015), and a five-year mean is also provided (2011-2015), along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.\r\n\r\nThe algorithm used to devive the product is described in the paper “Implications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetry” by S.B. Simonsen and L.S. Sørensen, which has been submitted to Remote Sensing of the Environment." } }, { "ob_id": 204, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19854, "uuid": "2d43e43de3484810ae24cfdc13eab263", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Grounding Line Locations v1.2", "abstract": "This dataset contains grounding lines for 5 North Greenland glaciers, derived from generated from ERS -1/-2 SAR Tandem and 3 days data sets. This addition includes the grounding line for the Petermann glacier from Sentinel-1A. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria. \r\n\r\nThe grounding line is the separation point between the floating and grounded parts of the glacier. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product.\r\n\r\nRemote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone)." }, "objectObservation": { "ob_id": 14263, "uuid": "f558dea21c664d51b407fe59fe321e2c", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Grounding Line Locations, v1.1", "abstract": "This dataset contains grounding lines for 5 North Greenland glaciers, derived from SAR Interferometery data from the ERS-1 and -2 satellites. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria.\r\n\r\nThe grounding line separates the floating part of a glacier from the grounded part. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product. \r\n\r\nRemote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone)." } }, { "ob_id": 205, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26644, "uuid": "ea7a4cbe7b83450bb7a00bf3761c40d7", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Grounding Line Locations from SAR Interferometry, v1.3", "abstract": "This dataset contains grounding lines for 5 North Greenland glaciers, derived from generated from ERS -1/-2 and Sentinel-1 SAR (Synthetic Aperture Radar) interferometry. This version of the dataset (v1.3) has been extended with grounding lines for 2017. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria. \r\n\r\nThe grounding line is the separation point between the floating and grounded parts of the glacier. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product.\r\n\r\nRemote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone)." }, "objectObservation": { "ob_id": 19854, "uuid": "2d43e43de3484810ae24cfdc13eab263", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Grounding Line Locations v1.2", "abstract": "This dataset contains grounding lines for 5 North Greenland glaciers, derived from generated from ERS -1/-2 SAR Tandem and 3 days data sets. This addition includes the grounding line for the Petermann glacier from Sentinel-1A. Data was produced as part of the ESA Greenland Ice Sheets Climate Change Initiative (CCI) project by ENVEO, Austria. \r\n\r\nThe grounding line is the separation point between the floating and grounded parts of the glacier. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and for establishing realistic scenarios for the response to climate change. The grounding line location product is derived from InSAR data by mapping the tidal flexure and is generated for a selection of the few glaciers in Greenland, which have a floating tongue. In general, the true location of the grounding line is unknown, and therefore validation is difficult for this product.\r\n\r\nRemote sensing observations do not provide direct measurement on the transition from floating to grounding ice (the grounding line). The satellite data deliver observations on ice surface features (e.g. tidal deformation by InSAR, spatial changes in texture and shading in optical images) that are indirect indicators for estimating the position of the grounding line. Due to the plasticity of ice these indicators spread out over a zone upstream and downstream of the grounding line, the tidal flexure zone (also called grounding zone)." } }, { "ob_id": 206, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26778, "uuid": "b017235a8e544d6fbad21387ebfbf0d8", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by TU Dresden, v1.2", "abstract": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by TU Dresden. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to August 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.2) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016)\r\n\r\nThe mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.\r\n\r\nMass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. \r\n\r\nBasin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. This GMB product has been produced by TU Dresden for comparison with the existing GMB product derived by DTU Space.\r\n\r\nPlease cite the dataset as follows: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065" }, "objectObservation": { "ob_id": 20115, "uuid": "0c724d2a018c48cab18e1a14f0fee6df", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by TU Dresden, v1.0", "abstract": "This dataset provides the Gravitational Mass Balance (GMB) product derived from the GRACE satellite instrument, by TU Dresden. The data consists of two products, a mass change time series for the Greenland Ice Sheet and individual basins, and mass trend grids for 5-year periods. \r\n\r\nThe mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.\r\n\r\nMass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set." } }, { "ob_id": 207, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26784, "uuid": "35ea8189e75e4b6f95e7c86812080ecb", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v1.4", "abstract": "This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2017; and mass trend grids for different 5-year periods between 2003 and 2017. This version (1.4) is derived from GRACE monthly solutions provided by TU Graz (ITSG-Grace 2016), apart from August 2016 time series which is computed using the CRS-R05 solution.\r\n\r\nThe mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.\r\n\r\nMass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. \r\n\r\nBasin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. \r\n\r\nCitation: \r\nBarletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013." }, "objectObservation": { "ob_id": 19856, "uuid": "2587d4a63a1f4928880008d7d7770552", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU-Space, v1.0", "abstract": "This dataset provides the Gravitational Mass Balance (GMB) product derived from the GRACE satellite instrument, by DTU-Space. The data consists of two products, a mass change time series for the Greenland Ice Sheet and individual basins, and mass trend grids for 5-year periods. \r\n\r\nThe mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.\r\n\r\nMass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set." } }, { "ob_id": 208, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19868, "uuid": "82e4ede59fe746ba810009d9a30e0153", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2014-2015, v1.0", "abstract": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2014-2015, derived from Sentinel-1 SAR data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThe data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities." }, "objectObservation": { "ob_id": 14264, "uuid": "7ae7e43347d34308960c19313367e59c", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map 2015, v1.0", "abstract": "This dataset is part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project, and provides components of the ice velocity and the magnitude of the velocity for the Greenland Ice Sheet.\r\n\r\nThe dataset is derived from Interferometric Wide Swath SAR data from the Sentinel-1 satellite, acquired in the period from the 1st November 2014 to the 1st December 2015. The ocean mask is based on the GIMP Ocean mask (Version 2.0; Howat et al. 2014), but calving fronts of marine terminating glaciers have been updated using Landsat-8 data acquired from May to August 2015." } }, { "ob_id": 209, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26647, "uuid": "302f379334e84664bd3409d08eca6565", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2015-2016, v1.2", "abstract": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2015-2016, derived from Sentinel-1 SAR data acquired from 01/10/2015 to 31/10/2016, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. \r\n\r\nThe ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH)." }, "objectObservation": { "ob_id": 19870, "uuid": "369f9e483e0d4646a144d37f4f88f9fe", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map Winter 2015-2016, v1.0", "abstract": "This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2015-2016, derived from Sentinel-1 SAR data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. \r\n\r\nThe data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities." } }, { "ob_id": 210, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26641, "uuid": "a0d9764a3068439b997c42928ef739d2", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2", "abstract": "This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010. It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThe dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage. It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity. \r\n\r\nThe second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.\r\n\r\nThe data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.\r\nThe horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.\r\n\r\nThe product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH)." }, "objectObservation": { "ob_id": 19863, "uuid": "dcda86e1d52f44aaafcffb77b47ba1bb", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Isbrae glacier, 2002-2010, v1.1 (June 2016 release)", "abstract": "This dataset contains a time series of ice velocities for the Jakobshavn Isbrae Glacier in Greenland between 2002-2010, which has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThis dataset consists of a time series of Ice velocity maps which have been generated from Image Swath mode images from the ASAR instrument on the ENVISAT satellite, with a 35 day repeat cycle. The data are supplied on a 500m polar stereographic grid. \r\n\r\nPlease note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product." } }, { "ob_id": 211, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19863, "uuid": "dcda86e1d52f44aaafcffb77b47ba1bb", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Isbrae glacier, 2002-2010, v1.1 (June 2016 release)", "abstract": "This dataset contains a time series of ice velocities for the Jakobshavn Isbrae Glacier in Greenland between 2002-2010, which has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThis dataset consists of a time series of Ice velocity maps which have been generated from Image Swath mode images from the ASAR instrument on the ENVISAT satellite, with a 35 day repeat cycle. The data are supplied on a 500m polar stereographic grid. \r\n\r\nPlease note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product." }, "objectObservation": { "ob_id": 14265, "uuid": "378bd099a9b74c498e2975a1e156dd12", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshaven region for 2002-2010, v1.1 (April 2016 release)", "abstract": "This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland between 2002 and 2010. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThis dataset consists of a time series of ice velocity maps which have been generated from IS mode images from the ASAR instrument on the ENVISAT satellite, with a 35 day repeat cycle, and are supplied on a 500m polar stereographic grid. The ice velocity product contain the horizontal components, vN and vE, of the total velocity vector, which is derived from radar measurements assuming surface parallel flow. The used digital elevation model of the surface is also supplied. The North and East velocities at any grid points are given in a local geographic north-east coordinates system (and not in the used grid map projection system)." } }, { "ob_id": 212, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 19864, "uuid": "ec38bfab8ae64c8a8b9a8072c2765b9a", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time series for the Upernavik region, 1992-2010, v1.1 (June 2016 release)", "abstract": "This dataset contains a time series of ice velocities for the Upernavik region in Greenland between 1992-2010, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThe data consists of an ice velocity time series derived from intensity-tracking of ERS-1/2, ASAR and PALSAR data acquired between 02-01-1992 and 22-08-2010. It provides components of the ice velocity and the magnitude of the velocity.\r\n\r\nThe data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that the previous versions of this product provided the horizontal velocities as true East and North velocities.\r\n\r\nBoth a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by GEUS. For further information please see the Product User Guide (v2.0).\r\n\r\nPlease note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product." }, "objectObservation": { "ob_id": 14298, "uuid": "ad51086ffcc348debd392559b3205f11", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Upernavik region for 1992 - 2010, v1.1 (April 2016 release)", "abstract": "This dataset contains a time series of ice velocities for the Upernavik glacier in Greenland between 1992 and 2010. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThis dataset consists of a time series of Ice velocity maps which have been generated from SAR data from the ERS-1 and ERS-2, ENVISAT and the ALOS satellites. The data are supplied on a 500m polar stereographic grid. The ice velocity product contain the horizontal components, vN and vE, of the total velocity vector, which is derived from radar measurements assuming surface parallel flow. The used digital elevation model of the surface is also supplied. The North and East velocities at any grid points are given in a local geographic north-east coordinates system (and not in the used grid map projection system)." } }, { "ob_id": 213, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26640, "uuid": "8d475d7d92894765ad1ddda16de0e610", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik glacier from ERS-1, ERS-2, Envisat and PALSAR data for 1992-2010, v1.2", "abstract": "This dataset contains a time series of ice velocities for the Upernavik glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat and PALSAR data aquired between 02/01/1992 and 22/08/2010. The data provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThe data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs used have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. \r\n\r\nThe product was generated by GEUS (Geological Survey of Denmark and Greenland)." }, "objectObservation": { "ob_id": 19864, "uuid": "ec38bfab8ae64c8a8b9a8072c2765b9a", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time series for the Upernavik region, 1992-2010, v1.1 (June 2016 release)", "abstract": "This dataset contains a time series of ice velocities for the Upernavik region in Greenland between 1992-2010, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nThe data consists of an ice velocity time series derived from intensity-tracking of ERS-1/2, ASAR and PALSAR data acquired between 02-01-1992 and 22-08-2010. It provides components of the ice velocity and the magnitude of the velocity.\r\n\r\nThe data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NOTHING(y) directions of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that the previous versions of this product provided the horizontal velocities as true East and North velocities.\r\n\r\nBoth a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by GEUS. For further information please see the Product User Guide (v2.0).\r\n\r\nPlease note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product." } }, { "ob_id": 214, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26635, "uuid": "925e3f0e807243e2936cc492f5207af6", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Kangerlussuaq Glacier for 2015-2017 from Sentinel-1, v1.1", "abstract": "This dataset contains a time series of ice velocity maps for the Kangerlussuag Glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19987, "uuid": "7687e5d628f1496cbe6c2622642842b2", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of the Kangerlussuaq Glacier for 2015-2016 from Sentinel-1, v1.0", "abstract": "This dataset contains a time series of ice velocity maps for the Kangerlussuag Glacier in Greenland derived from Sentinel-1 SAR data acquired between January 2015 and June 2016. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } }, { "ob_id": 215, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26636, "uuid": "e1c0c34e0cc942898b3626efd1dcc095", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier for 2014-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the Jakobshavn glacier in Greenland, generated from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired from October 2014 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19986, "uuid": "d58ffcfd861a4f03bad97f77c505814b", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of the Jakobshavn Isbrae for 2014-2016 from Sentinel-1 data, v1.0", "abstract": "This dataset contains a time series of ice velocities for the Jakobshavn Isbrae glacier in Greenland, generated from Sentinel-1 SAR data acquired from 11/10/2014 and 02/06/2016. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } }, { "ob_id": 216, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26632, "uuid": "ef5c6596cae548c6aea9dea181c7624c", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Upernavik Glacier for 2014-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the Upernavik Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between October 2014 and March 2017. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19866, "uuid": "ea4fefb0e791495fb80d6f571f055a6b", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of the Upernavik Isstroem for 2014-2016 from Sentinel-1 data, v1.0", "abstract": "This dataset contains a time series of ice velocities for the Upernavik Isstroem glacier in Greenland, derived from Sentinel-1 SAR data acquired between October 2014 and June 2016. This dataset has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } }, { "ob_id": 217, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26639, "uuid": "41e2300068b44fa190f24272dc08dcd0", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the 79-Fjord Glacier for 2015-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the 79-Fjord Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19859, "uuid": "3566cdffebf04ce784ad0fdcf094834a", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of 79-Fjord Glacier for 2015-2016 from Sentinel-1 data, v1.0", "abstract": "This dataset contains a time series of ice velocities for the 79-Fjord Glacier in Greenland, derived from Sentinel-1 SAR data acquired between 25/1/2015 and 8/6/2016. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } }, { "ob_id": 218, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26634, "uuid": "81332b9a10f14bda8a1a83b6463bb6de", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann Glacier for 2015-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the Petermann Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 22/1/2015-19/3/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19860, "uuid": "b989a4bf8d534973918470118c0d96fe", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of the Petermann Glacier for 2015-2016 from Sentinel-1 data, v1.0", "abstract": "This dataset contains a time series of ice velocities for the Petermann Glacier in Greenland, derived from Sentinel-1 SAR data acquired between 23/1/2015-10/6/2016. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } }, { "ob_id": 219, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26631, "uuid": "e3dbdc32f7b6476e949d52d8d3990205", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae Glacier for 2015-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the Zachariae glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19858, "uuid": "bf6bfa8a6ae74b27b6e5497ea3d45307", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of the Zachariae Isstroem for 2015-2016 from Sentinel-1 data, v1.0", "abstract": "This dataset contains a time series of ice velocities for the Zachariae Isstroem glacier in Greenland, derived from Sentinel-1 SAR data acquired between 25/1/2015 and 8/6/2016. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } }, { "ob_id": 220, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26633, "uuid": "1dd4c30a78d84e628cd8097bae3148fd", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Storstroemmen Glacier for 2015-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the Storstromemmen glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between 24/1/2015 and 22/03/2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19857, "uuid": "7bb9375151cf44e7993664c10d8d3da5", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of the Storstroemmen Glacier for 2015-2016 from Sentinel-1 data, v1.0", "abstract": "This dataset contains a time series of ice velocities for the Storstromemmen glacier in Greenland, derived from Sentinel-1 SAR data acquired between 26/1/2015 and 8/6/2016. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } }, { "ob_id": 221, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26638, "uuid": "5c9935b8b8854baeb7a256446293c03b", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen Glacier for 2015-2017 from Sentinel-1 data, v1.1", "abstract": "This dataset contains a time series of ice velocities for the Hagen glacier in Greenland derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." }, "objectObservation": { "ob_id": 19865, "uuid": "e3a4bd4d857742c2922993722253f52b", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity Time Series of the Hagen Brae for 2015-2016 from Sentinel-1 data, v1.0", "abstract": "This dataset contains a time series of ice velocities for the Hagen Brae glacier in Greenland derived from Sentinel-1 SAR data acquired between 22/1/2015 and 1/6/2016. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.\r\n\r\nData files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid." } } ] }