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                    "abstract": "This dataset collection contains in situ atmospheric and aerosol measurements collected at Summit Station, Greenland.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project. Since 2010, the ICECAPS project has been monitoring cloud-atmosphere-energy interactions at Summit Station, in the centre of the Greenland Ice Sheet (GrIS), using a comprehensive suite of ground-based remote sensing instruments and twice daily radiosonde profiles. In 2018, the Aerosol Cloud Experiment (ACE) expansion of ICECAPS saw the addition of a new series of instruments to measure surface aerosol concentrations and turbulent heat fluxes over the ice sheet. Combined with the original ICECAPS instrumentation, the ACE instruments allow for the study of cloud-aerosol-energy interactions over the central GrIS.\r\n\r\nThis dataset collection contains the measurements collected as part of the ACE component of ICECAPS-ACE, which includes the following:\r\n1) Surface-temperature-profile: A near surface temperature profile from four temperature/ humidity sensors distributed on the 15 m tower at Summit.\r\n2) Surface-moisture-profile: A near surface moisture profile from four temperature/ humidity sensors distributed on the 15 m tower at Summit.\r\n3) Surface-winds-profile: A near surface wind profile from four sonic anemometers distributed on the 15 m tower at Summit.\r\n4) Snow-height: The distance to the snow surface from the lowest level of instruments on the 15 m tower at Summit, detected by a sonic-ranging sensor.\r\n5) Skin-temperature: The brightness temperature of the snow surface as detected by an infrared radiation thermometer.\r\n6) Aerosol-concentration: The concentration of condensation nuclei (> 5nm diameter) measured at the surface using a Condensation Particle Counter.\r\n7) Aerosol-size-distribution: The size-resolved concentration of surface aerosol particles between 0.25 and 6.5 um in diameter measured using an Optical Particle Counter.\r\n8) Flux-components: High resolution temperature, humidity and wind fluctuations that can be used to estimate turbulent fluxes using eddy covariance, located at two levels on the 15 m tower at Summit.\r\n9) Flux-estimates: Estimates of turbulent heat and momentum fluxes by applying the eddy covariance technique to flux-components.\r\n\r\nOther ICECAPS data are available here:\r\nhttps://psl.noaa.gov/arctic/observatories/summit/\r\n\r\nFrom August 2022 to August 2025, these measurements were supported by the ICECAPS-MELT project (Measurements along a Transect)."
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            "title": "High-resolution regional Met Office Unified Model (UM) climate model hindcast of the Antarctic Peninsula (1998-2017)",
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            "title": "FRANC: Ensemble member output from UK Met Office Unified Model runs supporting analysis of convective-scale perturbation growth across a spectrum of convective regimes",
            "abstract": "Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): Ensemble member output from Unified Model runs as described in Flack et al. (2018): Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405\r\n\r\nThe dataset contains ensemble run output from 36 hour long runs under different model set ups (see details below) for 6 case studies (see Flack et al. 2018 for greater detail). The case studies (and model output available in the dataset) chosen related to a spectrum of 'convective adjustment time scales', defined as the ratio between the convective available potential energy (CAPE) and its rate of release at the convective scale. 'control' run files contain  large scale rainfall rates and amounts whilst the 'control_multilevel' files contain various parameters on various levels, including mean sea level pressure, zonal, meridional and vertical wind components, specific humidity and temperature.\r\n\r\n- Case A: 20th April 2012, part of the Dynamical and Microphysical Evolution of Convective Storms (DYMECS) field experiment (Stein et al. 2015), showing typical conditions for scattered showers in the United Kingdom.\r\n- Case B: 12 August 2013, for a case where a surface low was situated over Scandinavia and the Azores high was beginning to build, leading to persistent northwesterly flow.\r\n- Case C: 23rd July 2013, relating to the fifth intensive observation period (IOP 5) of the Convective Precipitation Experiment (COPE; Leon et al. 2016). A low pressure system was centered to the west of the United Kingdom with several fronts ahead of the main center, which later decayed.\r\n- Case D: 2nd August 2013, covering IOP 10 of the COPE field campaign, with convection initiating at 1100 UTC. The synoptic situation shows a low pressure system centered to the west of Scotland, which led to southwesterly winds and a convergence line being set up along the North Cornish coastline (in southwest England).\r\n- Case E: 27th July 2013, covers the period of IOP 7 of the COPE field campaign where two mesoscale convective systems (MCS) influenced the U.K.’s weather throughout the forecast period.\r\n- Case F: 5th August 2013, was chosen for the complex situation for considering convective-scale perturbation grown and a second case driven by the boundary conditions as seen during IOP 12 of the COPE campaign\r\n\r\nA brief description of the model run IDs and model setup is given below.\r\n\r\nThe model used to create these ensembles is the Met Office Unified Model (MetUM). The United Kingdom Variable resolution (UKV) configuration is used, and so the data has a grid spacing of approximately 1.5 km. This was run at version 8.2 and run with the MetUM Graphical User Interface (GUI).\r\n\r\nrun ID: xkyib\r\n\r\nThis is the control experiment and everything is kept identical to the operational running of this configuration of the MetUM.\r\n\r\nrun ID: xldef\r\n\r\nHere the Gaussian potential temperature perturbations are added into the model. Full details of the perturbation method are described in Flack et al. (2018) Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405, however a brief overview is given below:\r\n\r\nA Gaussian distribution (defined using random numbers between +/- 1 at each grid point, with the seed determined by the time the model is ran) is created at every grid point in the domain. A superposition is created and rescaled to 0.1 K so as to be an appropriate amplitude for boundary layer noise. Each of the Gaussian distributions have a standard deviation of 9km so as to be added onto an appropriate scale for the model. The perturbations are added in at a model hybrid height of 261.6 m (approximately the 8th model level).",
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                    "title": "Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC) Project",
                    "abstract": "Brief periods of intense rainfall can lead to flash flooding with the potential to cause millions of pounds of damage to property, and to threaten lives. Accurate flood warnings even just a few hours ahead can allow preparations to be made to minimize damage. In order to improve the prediction of these events, more accurate forecasts of heavy rainfall are needed, which can then be used to inform flood prediction and warning systems. The UK Met Office is developing a new numerical weather prediction system with the goal of improving severe weather forecasts. This is a computer model that solves mathematical equations representing atmospheric motions and other physical processes such as cloud formation, with a horizontal grid spacing of 1.5km. This allows a more accurate representation of fine-scale features and explicit representation of storms, but the results are still dependent on the accuracy of the starting conditions or initial data describing the current state of atmospheric variables such as winds, pressure, temperature and humidity. Initial conditions are usually estimated using a sophisticated mathematical technique known as data assimilation that blends observations with model information, taking account of the uncertainties in the data. In this project, we propose fundamental research to reduce initial condition errors. The work will be carried out in a partnership between the Universities of Reading, Surrey and the Met Office. We plan to investigate ways of extracting the maximum information from weather radar observations of precipitation and moisture in the lower parts of the atmosphere. Although rainfall is usually well observed by weather radar, severe precipitation can cause the radar beam to lose energy, and thus the weaker returned signal may be misinterpreted, giving a lower rain-rate than in reality. We will develop algorithms to correct for this and other problems caused by severe rainfall. Recently, we have also developed techniques to infer humidity information about the lower atmosphere, and we plan to optimize the method and investigate the observation error characteristics, to prepare for this data to be assimilated by the Met Office. One of our goals is to use observations to provide information on the small scales without degrading the large scale weather patterns, which are themselves likely to be accurate. However, currently much of the small scale observational information is being lost by ignoring correlations between observation errors. We will develop a generic approach for treating observation correlations for a range of observation types. We will investigate mathematical methods that both capture the maximum amount of information contained in the observations, while still being practical for operational computations, which have to take place within a limited time frame. Another goal is to develop innovative ways of treating moist processes that are largely absent from present-day assimilation systems. We plan to design and test efficient and effective ways of assimilating moisture information that respect the intricate dynamical and physical relationships that operate in the atmosphere. If successful, such new approaches will allow better use of cloud and rain affected observations than at present. Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Further studies will be made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations.\r\n\r\nFor further details of the FRANC project please also see Dance et al. (2019) article in the online resources linked to from this record: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project.\r\n\r\nGrant ref: NE/K008900/1"
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                    "title": "Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): rain radar helical scan data, assimilation versus model residuals and ensemble member model output.",
                    "abstract": "The Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC) project undertook a series of studies to design and test efficient and effective ways of assimilating moisture information from observations that respect the intricate dynamical and physical relationships that operate in the atmosphere. The aim of this work was, if successful, that such new approaches would allow better use of cloud and rain affected observations than previously. Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Studies were also made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations. To these ends this collection contains data from two of the studies within this project plus helical scan data from the Met Office's Wardon Hill radar utilised by the project team.\r\n\r\nThe two datasets from the project team cover ensemble member output from runs of the Met Office's Unified Model conducted to support the project and Doppler radar radial wind observations and associated observation-minus-model residuals from the Met Office UKV 3D Var assimilation scheme. Please see the individual datasets for additional information.\r\n\r\nFor further details of the FRANC project please also see Dance et al. (2019) article in the online resources linked to from this record: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project."
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            "abstract": "This dataset contains along-track sea level anomalies derived from satellite altimetry.   Altimeter along-track sea level measurements from the Jason-1, Jason -2 and Jason-3 satellite missions have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia).  These six time series cover the period from 15 January 2002 to 30 May 2018.\r\n\r\nThe product benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections.  \r\n\r\nThe main objective of this product is to provide accurate altimeter Sea Level Anomalies (SLA) time series as close to the coast as possible in order to assess whether the coastal sea level trends experienced at the coast are similar to the observed sea level trends in the open ocean and to determine the causes of the potential discrepancies.\r\n\r\nThe product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). During the project, the product will be extended in spatial coverage and with additional altimeter missions.  This version of the dataset is v1.1.  (DOI: 10.5270/esa-sl_cci-xtrack_ales_sla-200206_201805-v1.1-202005)",
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                "abstract": "The coastal sea level data products are based on a complete reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series at monthly interval, from 20 km offshore to the coast"
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                    "abstract": "This dataset collection contains various along-track sea level anomaly products derived from satellite altimetry by the ESA Sea Level Climate Change Initiative project.\r\n\r\nTwo datasets containing along-track sea level anomalies derived from satellite altimetry have been derived; one containing data from the JASON satellites (JASON-1, JSON-2, and JSON-3), and the other from the RA2 instrument on ENVISAT and the Altika instrument on SARAL satellite missions.\r\n\r\nThese have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia).  \r\n\r\nThe products benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections.  \r\n\r\nAdditionally a database of coastal sea level anomalies and associated trends from Jason satellite altimetry, derived from the JASON sea level anomaly product is included."
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                    "short_code": "coll",
                    "title": "ESA Sea Level Climate Change Initiative (Sea Level CCI) dataset collection",
                    "abstract": "As part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, the Sea Level CCI project has produced a set of gridded multi-satellite merged products relating to the Sea Level Essential Climate Variable (ECV).  These consist of a)  a time series of monthly gridded Sea Level Anomalies (SLA) and b) Oceanic Indicators describing the evolution of the sea level anomalies.\r\n\r\nSea surface heights are measured above (or below) some reference level by altimeter satellites, surface height being the difference between a satellites position in orbit with respect to an arbitrary reference surface (the Earth's centre or a rough approximation of the Earth's surface: the reference ellipsoid) and the satellite-to-surface range (calculated by measuring the time taken for the signal to make the round trip). Through sending a microwave pulse to the ocean's surface, the satellites measured the surface heights through measuring the time taken for the pulse to return. \r\n\r\nThe current version is v1.1, and covers the period January 1993 - December 2014, and has been derived from the main altimeter missions: ERS-1, ERS-2, Envisat, TOPEX/Poseidon, Jason-1, Jason-2 and Geosat-Follow-On.    A detailed description of the SL CCI project and the products can be found in Ablain et al., 2014, and further information is also provided in the Product User Guide. \r\n\r\nThe following DOI can be used to reference the product database (all products in the V1.1 release (as of December 2015)): DOI:10.5270/esa-sea_level_cci-1993_2014-v_1.1-201512.  \r\n\r\n When using or referring to the SL_cci products, please mention the associated DOI (see above and the individual datasets) and also use the following citation where a detailed description of the SL_cci project and products can be found:\r\n\r\nAblain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.\r\n\r\nFor further information on the Sea Level CCI products, and to register your interest with the CCI team please email: info-sealevel@esa-sealevel-cci.org"
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            "title": "HadEX3: Global land-surface climate extremes indices v3.0.0 (1901-2018)",
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                "title": "Composite process for the ESA Sea Level CCI Altimeter coastal sea level anomalies datasets based on XTRACK/ALES processing.",
                "abstract": "The coastal sea level data products are based on a complete reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series at monthly interval, from 20 km offshore to the coast"
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                    "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): Collection of datasets of altimeter along-track high resolution sea level anomalies and associated trends in some coastal regions, v1.1",
                    "abstract": "This dataset collection contains various along-track sea level anomaly products derived from satellite altimetry by the ESA Sea Level Climate Change Initiative project.\r\n\r\nTwo datasets containing along-track sea level anomalies derived from satellite altimetry have been derived; one containing data from the JASON satellites (JASON-1, JSON-2, and JSON-3), and the other from the RA2 instrument on ENVISAT and the Altika instrument on SARAL satellite missions.\r\n\r\nThese have been processed to produce high resolution (20 Hz, corresponding to an along-track distance of ~300m) sea level anomalies, in order to provide long-term homogeneous sea level time series as close to the coast as possible in six different coastal regions (North-East Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, South-East Asia and Australia).  \r\n\r\nThe products benefits from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections.  \r\n\r\nAdditionally a database of coastal sea level anomalies and associated trends from Jason satellite altimetry, derived from the JASON sea level anomaly product is included."
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                    "short_code": "coll",
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                    "abstract": "As part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, the Sea Level CCI project has produced a set of gridded multi-satellite merged products relating to the Sea Level Essential Climate Variable (ECV).  These consist of a)  a time series of monthly gridded Sea Level Anomalies (SLA) and b) Oceanic Indicators describing the evolution of the sea level anomalies.\r\n\r\nSea surface heights are measured above (or below) some reference level by altimeter satellites, surface height being the difference between a satellites position in orbit with respect to an arbitrary reference surface (the Earth's centre or a rough approximation of the Earth's surface: the reference ellipsoid) and the satellite-to-surface range (calculated by measuring the time taken for the signal to make the round trip). Through sending a microwave pulse to the ocean's surface, the satellites measured the surface heights through measuring the time taken for the pulse to return. \r\n\r\nThe current version is v1.1, and covers the period January 1993 - December 2014, and has been derived from the main altimeter missions: ERS-1, ERS-2, Envisat, TOPEX/Poseidon, Jason-1, Jason-2 and Geosat-Follow-On.    A detailed description of the SL CCI project and the products can be found in Ablain et al., 2014, and further information is also provided in the Product User Guide. \r\n\r\nThe following DOI can be used to reference the product database (all products in the V1.1 release (as of December 2015)): DOI:10.5270/esa-sea_level_cci-1993_2014-v_1.1-201512.  \r\n\r\n When using or referring to the SL_cci products, please mention the associated DOI (see above and the individual datasets) and also use the following citation where a detailed description of the SL_cci project and products can be found:\r\n\r\nAblain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.\r\n\r\nFor further information on the Sea Level CCI products, and to register your interest with the CCI team please email: info-sealevel@esa-sealevel-cci.org"
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            "title": "FRANC: Doppler radar radial wind observations and associated observation-minus-model residuals from the Met Office UKV 3D Var assimilation scheme",
            "abstract": "The dataset is provided to support the publication 'Diagnosing Observation Error Correlations for Doppler Radar Radial Winds in the Met Office UKV Model Using Observation-Minus-Background and Observation-Minus-Analysis Statistics' by Waller et al (2016). The dataset was created as part of the NERC Flooding from Intense Rainfall (FRANC) project in order to study the observation uncertainties associated with Doppler radar radial wind observations assimilated in to the Met Office UK variable resolution model. The dataset is processed output of the Met Office UKV 3D var assimilation scheme for June, July and August 2013 for four different experimental scenarios. Full details and equations are given in Waller et al (2016) but the four different experimental cases are summarised as follows:\r\n\r\n- Case 1: Control experiment using standard UKV settings in place in January 2014\r\n- Case 2: As Case 1, but with a different background error covariance matrix used in the data assimilation\r\n- Case 3: As Case 1, but with raw Doppler radial wind observations rather than superobservations\r\n- Case 4: As Case 3, but with an improved observation operator.\r\n\r\nFor each case the dataset consists of the radial wind observations assimilated at each assimilation cycle valid between 01/06/2013 and 31/08/2016 along with the associated observation-minus-background and observation-minus-analysis residuals. Each observation also has metadata that describes the location of the observation (both in latitude/longitude co-ordinates, and co-ordinates relative to the radar station) , the assimilation cycle at which it was assimilated and the observation error variance that the observation was assigned in the data assimilation scheme. \r\n\r\nThese data are published under the Open Government License  (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) © Crown Copyright, 2020, Met Office”.",
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                "abstract": "The datasets were created by assimilating observations from the Met Office network of C band radars into the Met Office variable-resolution convection permitting model (UKV) using version 8.2 of the unified model. The assimilation scheme is a limited-area version of the Met Office 3D variational assimilation scheme that uses an incremental approach. We note that four datasets are provided to correspond with four different experiments. Further details of the observation, model, data assimilation and four experimental cases are given in Waller et al. 2016.\r\n\r\nThe data were created by Joanne Waller and David Simonin."
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                    "title": "Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC) Project",
                    "abstract": "Brief periods of intense rainfall can lead to flash flooding with the potential to cause millions of pounds of damage to property, and to threaten lives. Accurate flood warnings even just a few hours ahead can allow preparations to be made to minimize damage. In order to improve the prediction of these events, more accurate forecasts of heavy rainfall are needed, which can then be used to inform flood prediction and warning systems. The UK Met Office is developing a new numerical weather prediction system with the goal of improving severe weather forecasts. This is a computer model that solves mathematical equations representing atmospheric motions and other physical processes such as cloud formation, with a horizontal grid spacing of 1.5km. This allows a more accurate representation of fine-scale features and explicit representation of storms, but the results are still dependent on the accuracy of the starting conditions or initial data describing the current state of atmospheric variables such as winds, pressure, temperature and humidity. Initial conditions are usually estimated using a sophisticated mathematical technique known as data assimilation that blends observations with model information, taking account of the uncertainties in the data. In this project, we propose fundamental research to reduce initial condition errors. The work will be carried out in a partnership between the Universities of Reading, Surrey and the Met Office. We plan to investigate ways of extracting the maximum information from weather radar observations of precipitation and moisture in the lower parts of the atmosphere. Although rainfall is usually well observed by weather radar, severe precipitation can cause the radar beam to lose energy, and thus the weaker returned signal may be misinterpreted, giving a lower rain-rate than in reality. We will develop algorithms to correct for this and other problems caused by severe rainfall. Recently, we have also developed techniques to infer humidity information about the lower atmosphere, and we plan to optimize the method and investigate the observation error characteristics, to prepare for this data to be assimilated by the Met Office. One of our goals is to use observations to provide information on the small scales without degrading the large scale weather patterns, which are themselves likely to be accurate. However, currently much of the small scale observational information is being lost by ignoring correlations between observation errors. We will develop a generic approach for treating observation correlations for a range of observation types. We will investigate mathematical methods that both capture the maximum amount of information contained in the observations, while still being practical for operational computations, which have to take place within a limited time frame. Another goal is to develop innovative ways of treating moist processes that are largely absent from present-day assimilation systems. We plan to design and test efficient and effective ways of assimilating moisture information that respect the intricate dynamical and physical relationships that operate in the atmosphere. If successful, such new approaches will allow better use of cloud and rain affected observations than at present. Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Further studies will be made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations.\r\n\r\nFor further details of the FRANC project please also see Dance et al. (2019) article in the online resources linked to from this record: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project.\r\n\r\nGrant ref: NE/K008900/1"
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                    "title": "Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): rain radar helical scan data, assimilation versus model residuals and ensemble member model output.",
                    "abstract": "The Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC) project undertook a series of studies to design and test efficient and effective ways of assimilating moisture information from observations that respect the intricate dynamical and physical relationships that operate in the atmosphere. The aim of this work was, if successful, that such new approaches would allow better use of cloud and rain affected observations than previously. Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Studies were also made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations. To these ends this collection contains data from two of the studies within this project plus helical scan data from the Met Office's Wardon Hill radar utilised by the project team.\r\n\r\nThe two datasets from the project team cover ensemble member output from runs of the Met Office's Unified Model conducted to support the project and Doppler radar radial wind observations and associated observation-minus-model residuals from the Met Office UKV 3D Var assimilation scheme. Please see the individual datasets for additional information.\r\n\r\nFor further details of the FRANC project please also see Dance et al. (2019) article in the online resources linked to from this record: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project."
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            "abstract": "This dataset contains radar-derived measurements of cell-top height, size, 2 km reflectivity, and cell latitude and longitude from all convective cells between 14 May and 30 September 2016, where radar is available. The data was collected as part of the NERC/MoES Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) field campaign.\r\n\r\nThe seven sites analysed here represent four different Indian climate regions, allowing the study of the spatiotemporal development of convection during the 2016 monsoon season at high (1 km) resolution. Variation in these different cell statistics are found over timescales of variability such as the diurnal cycle, active-break periods, and monsoon progression.\r\n\r\nThe data were collected as part of the INCOMPASS field campaign May-July 2016, funded by Natural Environmental Research Council (NERC) (NE/L01386X/1). The aim of the project was to improve the skill of rainfall prediction in operational weather and climate models by way of better understanding and representation of interactions between the land surface, boundary layer, convection, the large-scale environment and monsoon variability on a range of scales.",
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                    "abstract": "The monsoon supplies the majority of water for agriculture and industry in South Asia, and is therefore critical to the well-being of a billion people. Active and break periods in the monsoon have a major influence on the success of farming, while year-to-year variations in the rainfall have economic consequences on an international scale. The growing population and developing economy mean that understanding and predicting the monsoon is therefore vital. Despite this, our capability to model the monsoon, and to make forecasts on scales from days to the season ahead is limited by large errors that develop quickly. The relatively poor performance of weather prediction models over India is due to a very strong and complex relationship between the land, ocean and atmosphere, which are linked by the process of convection, in the form of the rain-bringing cumulonimbus clouds. Forecast errors occur primarily because the convective clouds are not accurately linked to the large-scale circulation or to the surface conditions, and these errors persist to long time scales. Worldwide, weather and climate forecast models are gaining resolution, and yet the errors in monsoon rainfall are not diminishing. A lack of detailed observations of the land, ocean and atmospheric parts of the monsoon system, on a range of temporal and spatial scales, is preventing a more thorough understanding of processes in monsoon convective clouds and at the land surface, and their interaction with the large-scale circulation. \r\n\r\nThe project used a programme of new measurements over India and the adjacent oceans to advance monsoon forecasting capability in the Indo-UK community. The first detachment of the FAAM research aircraft to India, in combination with an intensive ground-based observation campaign, will gather new observations of the land surface, the boundary layer structure over land and ocean, and atmospheric profiles. We will institute a new long-term series of measurements of energy and water exchanges at the land surface. Research measurements from one monsoon season will be combined with long-term observations on the Indian operational networks. Observations will be focused on two transects: in the northern plains of India, covering a range of surface types from irrigated to rain-fed agriculture, and wet to dry climatic zones; and across the Western Ghats, with transitions from land to ocean and across orography. The observational analysis will represent a unique and unprecedented characterization of monsoon processes linking the land, ocean and atmospheric patterns which control the rainfall. Long-term measurements will allow the computation of statistical relationships between the various factors. \r\n\r\nThe observational analysis fed directly into improved forecasting at the Met Office and NCMRWF. The Met Office Unified Model, which is used for weather forecasting at both institutions, was set up in a range of different ways for the observational period. In particular, the project pioneered the test development of a new 100m-resolution atmospheric model, which greatly improved the representation of land-ocean-atmosphere interactions. Another priority was to improve land surface modelling in monsoon forecasts. By comparing the results of the very high resolution models on small domains with lower-resolution models representing the global weather patterns, it was possible to describe the key processes controlling monsoon rainfall, and to indicate how these need to be represented in different applications, such as weather predictions or climate predictions. Through model evaluation at a range of scales, the development of simple theoretical understanding of the rainfall processes, and working with groups responsible for operational model improvement, the project led directly to improvements in monsoon forecasts. \r\n\r\nObjectives: The grand objective of this project was to improve the skill of rainfall prediction in operational weather and climate models by way of better understanding and representation of interactions between the land surface, boundary layer, convection, the large-scale environment and monsoon variability on a range of scales.\r\n\r\nSpecific objectives:\r\n\r\n1a) To document and evaluate the characteristics of monsoon rainfall on sub-daily to intraseasonal time scales, as influenced by surface, thermodynamic and dynamic forcing, as monsoon air moves from the ocean inland and across the subcontinent.\r\n1b) To evaluate the representation of these rainfall processes in the Met Office Unified Model at a range of resolutions, and thereby to indicate the priorities for model development.\r\n\r\n2) Quantify land surface properties and fluxes, using in-situ and remote sensing measurements, as they interact with the monsoon on hourly to monthly time scales and from kilometre to continental spatial scales. \r\n\r\n3a) Quantify the role of the Indian land surface in the progression of the monsoon during the onset, and in monsoon variability, and relate it to the role of the ocean.\r\n3b) Evaluate the impact of improved land-surface representation on monsoon prediction and make recommendations for future land-atmosphere modelling strategy.\r\n\r\n4a) Evaluate the influence of local and short-term structures in convection and the boundary layer, on rainfall variability on intraseasonal and seasonal timescales, using observations, idealized models and a range of operational models. \r\n4b) Make recommendations for priorities in the parametrization of convective rainfall in the monsoon system."
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                    "abstract": "Radiosonde and in-situ airborne observations by the FAAM BAE-146 aircraft for Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS)."
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            "title": "APHH: Ionic species data within PM2.5 measurements made at the Indira Gandhi Delhi Technical University for Women (IGDTUW) site during the pre and post monsoon periods for the DelhiFlux field campaign 2018",
            "abstract": "This dataset contains ionic data within PM2.5 measurements made during the  Pre- Monsoon (28/05/2018  08:30:00 - 05/06/2018  17:30:00) and Post-Monsoon periods (09/10/2018  14:54:00 - 0\r\n6/11/2018  10:35:00) of the APHH Delhi campaigns in 2018 at Indira Gandhi Delhi Technical University for Women (IGDTUW) site. Measurements were conducted by the University of York High Volume Sampler (Ecotech 3000, Australia) and University of York Dionex ICS-1100 Ion Chromatography System.\r\n\r\nThe data were collected as part of the DelhiFlux project part of Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme.",
            "creationDate": "2022-07-22T09:15:57.183554",
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                    "title": "Atmospheric Pollution & Human Health in a Developing Megacity (APHH)",
                    "abstract": "The Atmospheric Pollution & Human Health in a Developing Megacity (APHH) programme has two separate streams of activity looking at urban air pollution and its impact on Health in Chinese and Indian Megacities. The programme is a collaboration between NERC, the Medical Research Council (MRC) in the UK and the National Natural Science Foundation of China (NSFC) in China, and the Ministry of Earth Sciences (MoES) and Department of Biotechnology (DBT) in India."
                },
                {
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                    "abstract": "The project, part of the Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme, has four specific objectives:\r\n\r\nTo improve the emission factor database for key source types and compounds in Delhi through a combination of lab and field based emission factor measurements, using harmonised instrumentation.\r\nPrerequisite\r\n\r\nTo compile a state-of-the-art emission inventory for the greater Delhi area at a spatial resolution of (1.6 km)2, together with temporal profiles of the diurnal and seasonal variability.\r\nTo inform and evaluate this emission inventory through direct and independent emission flux measurements at the urban scale (~10 km2).\r\nTo apply atmospheric transport modelling to assess the performance of the emission inventory against concentration measurements and quantify the implications of the emissions improvements for air quality indicators.\r\n\r\nGrant Ref: NE/P016472/1"
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                    "short_code": "coll",
                    "title": "(APHH India) Megacity Delhi atmospheric emission quantification, assessment and impacts (DelhiFlux):  Atmospheric measurements",
                    "abstract": "This dataset collection contains atmospheric measurements from the APHH India) Megacity Delhi atmospheric emission quantification, assessment and impacts (DelhiFlux) project."
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                    "ob_id": 32625,
                    "uuid": "04f9006457e54c49b628913912827ad7",
                    "short_code": "coll",
                    "title": "(APHH India) Megacity Delhi atmospheric emission quantification, assessment and impacts (DelhiFlux): Air quality measurements",
                    "abstract": "This dataset collection contains air quality data from the Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme 'Megacity Delhi atmospheric emission quantification, assessment and impacts (DelhiFlux)'."
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            "ob_id": 31872,
            "uuid": "2cc63301f1854239aa61c70e58c61207",
            "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for selected validation sites, version 1.0",
            "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP.  The data covers the period from March 2017 to May 2018 and is provided for TCCON (Total Carbon Column Observing Network) validation sites only.  A full global dataset is in production.     For further information on the dataset, please see the linked documentation.\r\n\r\nThis data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).",
            "creationDate": "2022-07-22T09:15:57.183554",
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            "updateFrequency": "notPlanned",
            "dataLineage": "Data were processed by the ESA CCI GHG project team and supplied to CEDA as part of the ESA CCI Open Data Portal Project.      This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).",
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                "title": "Composite Process for the ESA CCI Greenhouse Gases product: Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, version 1.0",
                "abstract": "Composite Process for the ESA CCI Greenhouse Gases product: Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, version 1.0"
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                    "title": "ESA Greenhouse Gases Climate Change Initiative Project",
                    "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010."
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                    "ob_id": 30128,
                    "uuid": "7fe9f59731ab47b6a20e792e0cba4641",
                    "short_code": "coll",
                    "title": "National Centre for Earth Observation (NCEO) partnered datasets",
                    "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)."
                },
                {
                    "ob_id": 12808,
                    "uuid": "0508f3dd991144aa80346007a415fb07",
                    "short_code": "coll",
                    "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection",
                    "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs).    The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series.  One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section."
                }
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            "ob_id": 31878,
            "uuid": "7cc70ebf9ebc46d483fb4f17a984a978",
            "title": "Iceland Greenland seas Project (IGP): meteorological buoy measurements",
            "abstract": "This dataset contains meteorological, sea water temperature, surface ocean currents and wave height, direction and period measurements  from a Seawatch Wavescan meteorological buoy deployed in the northwest Iceland Sea for the Iceland Greenland seas Project (IGP). \r\n\r\nThis was an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Amospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1). \r\n\r\nThe Seawatch Wavescan meteorological buoy was deployed during the first leg of the NATO Research Vessel Alliance cruise, on 21 February 2018. Its position in the northwest Iceland Sea was strategically placed adjacent to a subsurface mooring in the Eggvin Offset. The dataset contains standard meteorological variables, surface ocean  currents and wave height, direction and period from the buoy.  Sea water temperature measurements at 8 m depth from the co-located mooring beneath the buoy are included  to replace failed sea surface temperature measurements from the buoy under the reasonable assumption that this was still within the surface ocean mixed layer in this region. Similarly, pressure measurements that failed for roughly half of the deployment are replaced by surface layer estimates from ECMWF's ERA5 reanalysis product interpolated to the position of the meteorological buoy, corroborated for the period the sensor was working. Otherwise the buoy worked well for 2.5 months, until it broke loose from its anchor and stopped recording on 6 May 2018 and was recovered soon after. Also provided in the dataset are bulk aerodynamic flux estimates generated using the COARE3.0a algorithm.",
            "creationDate": "2022-07-22T09:15:57.183554",
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            "dataLineage": "Data delivered to the Centre for Environmental Data Analysis (CEDA) for archiving.",
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            "publicationState": "published",
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            "dontHarvestFromProjects": false,
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            "resolution": "",
            "status": "completed",
            "dataPublishedTime": "2020-09-23T15:41:45",
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                "eastBoundLongitude": -16.1624,
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                "volume": 439533,
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                "fileFormat": "NetCDF"
            },
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                "startTime": "2018-02-04T12:04:44",
                "endTime": "2018-03-19T12:49:26"
            },
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                "ob_id": 3155,
                "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)",
                "passesTest": true,
                "resultTitle": "CEDA Data Quality Statement",
                "date": "2018-08-02"
            },
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                "short_code": "acq",
                "title": "Iceland Greenland seas Project (IGP): Meteorological Buoy Measurements",
                "abstract": "Iceland Greenland seas Project (IGP): Meteorological Buoy Measurements"
            },
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                    "licence": {
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            "projects": [
                {
                    "ob_id": 24899,
                    "uuid": "2780d047461c42f0a12534ccf42f487a",
                    "short_code": "proj",
                    "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)",
                    "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel)  were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes.   \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function.  In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild."
                }
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                    "short_code": "coll",
                    "title": "In situ observations of air-sea interaction processes from the Iceland Greenland seas Project  (IGP)",
                    "abstract": "This collection contains a range of in situ observations of meteorological and air-sea interaction processes from a range of instruments on several platforms  (buoy, ship , radiosonde) from the Iceland Greenland seas Project  (IGP). \r\n\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US and Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)"
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            "uuid": "8d85f664fc614ba0a28af3a2d7ef4533",
            "title": "MIDAS Open: UK hourly weather observation data, v202007",
            "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2019.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2019, historical data recovery has added temperature and weather data for Bude (1937-1958), Teignmouth (1912-1930), and Eskdalemuir (1915-1948).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data.",
            "creationDate": "2022-07-22T09:15:57.183554",
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            "latestDataUpdateTime": "2020-10-30T16:42:38",
            "updateFrequency": "notPlanned",
            "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.",
            "removedDataReason": "",
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            "doiPublishedTime": "2020-10-21T10:27:56",
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                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 29454605314,
                "numberOfFiles": 49789,
                "fileFormat": "Data are BADC-CSV formatted."
            },
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                "ob_id": 8736,
                "startTime": "1875-01-01T09:00:00",
                "endTime": "2019-12-31T23:59:59"
            },
            "resultQuality": {
                "ob_id": 300,
                "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.",
                "passesTest": true,
                "resultTitle": "CEDA Data Quality Statement",
                "date": "2014-09-11"
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                    "abstract": "This Dataset Collection contains a number of different versions of the HadUK-Grid dataset, each of which present a set of gridded climate variables extending from the present back to the 19th Century. The primary purpose of these data are to facilitate monitoring of the UK climate and research into climate variability, climate change, impacts and adaptation. The Met Office uses these data for operational monitoring of the UK's climate.\r\n\r\nThe data have been interpolated from meteorological station data onto a uniform grid at 1km by 1km resolution to provide complete and consistent coverage across the UK. The 1km data set has been regridded to different resolutions and regional averages to create a collection allowing for comparison to data from UKCP18 climate projections.\r\n\r\nA new version of HadUK-Grid is released each year. The latest version is v1.3.1.ceda, released in June 2025 and containing data up to the end of 2024. A summary of previous releases can be found below. Provisional data for more recent months can be found on the Met Office web site https://www.metoffice.gov.uk/hadobs/hadukgrid/.\r\n\r\nEach version comprises eight Datasets - gridded data at 1, 5, 12, 25 and 60 km resolution, plus three sets of area averages (UK countries, admin regions and river basins).\r\n\r\nThe earliest year of data varies by variable and has changed as more data are digitised. Currently the start years are:\r\n1836 (monthly rainfall)\r\n1884 (monthly max/mean/min air temperature)\r\n1891 (daily rainfall)\r\n1910 (monthly sunshine)\r\n1931 (daily max/min air temperature)\r\n1961 (monthly days of ground frost, relative humidity, mean sea level pressure and vapour pressure)\r\n1969 (monthly mean wind speed)\r\n1971 (monthly days of lying snow)\r\n\r\nThe grids are provided at daily (max/min air temperature and rainfall only), monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods.\r\n\r\nThe latest release has been created by the Met Office funded by the UK Department for Science, Innovation and Technology (DSIT).\r\n\r\nPrevious versions were created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project.\r\n\r\nFor all versions, the data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\".\r\n\r\nThe data are provided under Open Government Licence v3 (see each dataset for links to licence and associated citations to use).\r\n\r\nList of dataset versions (latest first) and key differences (each release also extends the dataset by one year):\r\n\r\nv1.3.1.ceda (1836-2024) - Daily temperature extended back to 1931 (from 1960). Historical data recovery has improved daily rainfall over Scotland for 1922-1945.\r\nv1.3.0.ceda (1836-2023) - Historical data recovery has improved daily rainfall over Scotland for 1945-1960.\r\nv1.2.0.ceda (1836-2022) - Monthly sunshine extended back to 1910 (from 1919). Incorporation of Rainfall Rescue v2.\r\nv1.1.0.0 (1836-2021) - Addition of climate averages for 1991-2020. Rainfall Rescue v1 dataset incorporated into the monthly rainfall grids which are extended back to 1836 (from 1862).\r\nv1.0.3.0 (1862-2020)\r\nv1.0.2.1 (1862-2019) - Monthly sunshine extended back to 1919 (from 1929). Historical data recovery has also improved monthly rainfall 1862-1910, daily rainfall 1891-1910 and monthly temperature 1900-1909. Correction to the grid definition for 12 km grid product to match the UKCP18 climate model products.\r\nv1.0.1.0 (1862-2018) - Addition of 5km data.\r\nv1.0.0.0 (1862-2017) - Initial release.\r\n\r\nSee the change log file for each version for further details.\r\n\r\nNote: The introduction of the '.ceda' suffix was done to highlight that CEDA is the source of these data files compared to other potential sources (e.g. the UKCP User Interface https://ukclimateprojections-ui.metoffice.gov.uk/ui/home) The data values are the same - it is the way the data are packaged that may differ between sources.\r\n\r\nEach version following the initial release is accompanied by change log files. These list new files in the version compared with the previous version plus summary totals of the number of files that remained the same, modified and removed. Links to these change logs are available in the 'Details/Docs' section of each dataset. Additionally, a summary change log file is provided which gives an overview of all changes to the data sources and processing methods since the initial release. This summary can be found in the 'Details/Docs' section below or via the individual datasets.\r\n\r\nThis collection supersedes the UKCP09 Dataset Collection and contains all datasets within the major version 1 release (i.e. v1.#.#.#). See Hollis et al. (2019; linked documentation) for details on the version numbering utilised."
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            "title": "NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)",
            "abstract": "The National Centre for Earth Observation (NCEO): Monthly global Particulate Organic Carbon (POC) dataset contains POC concentrations gridded on both sinusoidal (SIN) and geographic (GEO) grid projections at 4 km spatial resolution for 1997-2020. The POC dataset has been produced using the Ocean Colour Climate Change Initiative Remote Sensing Reflectance (Rrs) products, Version 4.2. The dataset includes the Rrs at 443 nm and 555 nm with pixel-by-pixel uncertainty estimates for each wavelength.\r\n\r\nFor more details on the algorithm and its validation, please see papers by Stramski et al. (2008) and Evers-King et al. (2017). Please note that the validation of the POC algorithm is a continuing process. To increase the accuracy of POC algorithms, further in situ POC data need to be collected with high spatial and temporal resolution.",
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                "abstract": "The monthly global oceanic POC data for 1997-2020 was produced using the Ocean Colour Climate Change Initiative remote sensing reflectance product, Version 4.2.  The POC data has been calculated using the Stramski et al. (2008) empirical POC band ratio algorithms. References can be found in the documentation section."
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                    "title": "ESA Biological Pump and Carbon Export Processes (BICEP) Project",
                    "abstract": "The ESA Biological Pump and Carbon Export Processes (BICEP) project is an ESA project led by the Plymouth Marine Laboratory.   The objective of the  BICEP project is to further advance our capacity to better characterise the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system,  from a synergetic use of space data, in-situ measurements and model outputs.\r\n\r\nThe development of the BICEP datasets was also supported the Simons Foundation grant 'Computational Biogeochemical Modeling of Marine Ecosystems' (CBIOMES, number 549947)."
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                    "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments."
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            "title": "Monthly mean climate data from a transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 1950 to 2015",
            "abstract": "This dataset comprises monthly mean data from a global, transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) from 1950 to 2015. WACCM-X is a global atmosphere model covering altitudes from the surface up to ~500 km, i.e. including the troposphere, stratosphere, mesosphere and thermosphere. \r\n\r\nWACCM-X version 2.0 (Liu et al., 2018) was used, part of the Community Earth System Model (CESM) release 2.1.0 made available by the US National Center for Atmospheric Research. The model was run in free-running mode with a horizontal resolution of 1.9° latitude  2.5° longitude (giving 96 latitude points and 144 longitude points) and 126 vertical levels. Further description of the model and simulation setup is provided by Cnossen (2020) and references therein. A large number of variables are included on standard monthly mean output files on the model grid, while selected variables are also offered interpolated to a constant height grid or vertically integrated in height (details below). Zonal mean and global mean output files are included as well.\r\n\r\nThe following data file types are included:\r\n1)Monthly mean output on the full grid for the full set of variables; [DFT] = ''\r\n2)Zonal mean monthly mean output for the full set of variables; [DFT] = _zm\r\n3)Global mean monthly mean output for the full set of variables; [DFT] = _gm\r\n4)Height-interpolated/-integrated output on the full grid for selected variables; [DFT] = _ht\r\n\r\nA cos(latitude) weighting was used when calculating the global means.\r\n\r\nData were interpolated to a set of constant heights (61 levels in total) using the Z3GM variable (for variables output on midpoints, with \"lev\" as the vertical coordinate) or the Z3GMI variable (for variables output on interfaces, with \"ilev\" as the vertical coordinate) stored on the original output files (type 1 above). Interpolation was done separately for each longitude, latitude and time. \r\n\r\nMass density (DEN [g/cm3]) was calculated from the M_dens, N2_vmr, O2, and O variables on the original data files before interpolation to constant height levels. \r\n\r\nThe Joule heating power QJ [W/m3] was calculated using Q_J=_P B^2 [(u_i-u_n )^2+(v_i-v_n )^2+(w_i-w_n )^2] with P = Pedersen conductivity [S], B = geomagnetic field strength [T], ui, vi, and wi = zonal, meridional, and vertical ion velocities [m/s] and un, vn, and wn = neutral wind velocities [m/s]. QJ was integrated vertically in height (using a 2.5 km height grid spacing rather than the 61 levels on output file type 4) to give the JHH variable on the type 4 data files. The QJOULE variable also given is the Joule heating rate [K/s] at each of the 61 height levels.\r\n\r\nAll data are provided as monthly mean files with one time record per file, giving 792 files for each data file type for the period 1950-2015 (66 years).",
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                    "title": "Impacts of climate change in the troposphere, stratosphere and mesosphere on the thermosphere and ionosphere",
                    "abstract": "The data were produced as part of a Natural Environment Research Council (NERC) Independent Research Fellowship (NE/R015651/1) awarded to Ingrid Cnossen, entitled 'Impacts of climate change in the troposphere, stratosphere and mesosphere on the thermosphere and ionosphere'. \r\n\r\nThe project as a whole aims to quantify the importance of man-made climate change in the lower and middle atmosphere in causing long-term changes in the upper atmosphere, both in the past (1950s-2010s) and projected into the future (2050s) according to established emission scenarios. Computer simulations with WACCM-X, a state-of-the-art, global, 3-dimensional climate model, extending from the surface up to ~500 km altitude, are being used to do this. Results from these simulations will be compared to observed long-term changes in the upper atmosphere (e.g., in temperature, density) and to contributions made by other known factors. These include the increase in greenhouse gas concentration within the upper atmosphere itself, which has a cooling effect, and changes in the Earth's magnetic field, which cause more complicated patterns of long-term change. Interactions of changes in the Earth's magnetic field and changes in atmospheric tides due to climate change will also be investigated. This will focus at least initially again on the period of the 1950s to 2050s, but this may be broadened to a larger timespan from 850 to the present-day. \r\n\r\nThe current data set covers the period 1950-2015 and comes from the first long-term transient simulation with WACCM-X. The data set was used by Cnossen (2020) to quantify past climate change in the upper atmosphere, using the same multi-linear regression analysis technique that is often used to extract trends from observational data sets. The data set is also highly suitable for comparisons with observed long-term trends in the upper atmosphere, as the timeframes covered by observational data sets can be matched by selecting the relevant time window from the simulation data and all known drivers of climate change are included.  NE/R015651/1"
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                    "abstract": "Brief periods of intense rainfall can lead to flash flooding with the potential to cause millions of pounds of damage to property, and to threaten lives. Accurate flood warnings even just a few hours ahead can allow preparations to be made to minimize damage. In order to improve the prediction of these events, more accurate forecasts of heavy rainfall are needed, which can then be used to inform flood prediction and warning systems. The UK Met Office is developing a new numerical weather prediction system with the goal of improving severe weather forecasts. This is a computer model that solves mathematical equations representing atmospheric motions and other physical processes such as cloud formation, with a horizontal grid spacing of 1.5km. This allows a more accurate representation of fine-scale features and explicit representation of storms, but the results are still dependent on the accuracy of the starting conditions or initial data describing the current state of atmospheric variables such as winds, pressure, temperature and humidity. Initial conditions are usually estimated using a sophisticated mathematical technique known as data assimilation that blends observations with model information, taking account of the uncertainties in the data. In this project, we propose fundamental research to reduce initial condition errors. The work will be carried out in a partnership between the Universities of Reading, Surrey and the Met Office. We plan to investigate ways of extracting the maximum information from weather radar observations of precipitation and moisture in the lower parts of the atmosphere. Although rainfall is usually well observed by weather radar, severe precipitation can cause the radar beam to lose energy, and thus the weaker returned signal may be misinterpreted, giving a lower rain-rate than in reality. We will develop algorithms to correct for this and other problems caused by severe rainfall. Recently, we have also developed techniques to infer humidity information about the lower atmosphere, and we plan to optimize the method and investigate the observation error characteristics, to prepare for this data to be assimilated by the Met Office. One of our goals is to use observations to provide information on the small scales without degrading the large scale weather patterns, which are themselves likely to be accurate. However, currently much of the small scale observational information is being lost by ignoring correlations between observation errors. We will develop a generic approach for treating observation correlations for a range of observation types. We will investigate mathematical methods that both capture the maximum amount of information contained in the observations, while still being practical for operational computations, which have to take place within a limited time frame. Another goal is to develop innovative ways of treating moist processes that are largely absent from present-day assimilation systems. We plan to design and test efficient and effective ways of assimilating moisture information that respect the intricate dynamical and physical relationships that operate in the atmosphere. If successful, such new approaches will allow better use of cloud and rain affected observations than at present. Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Further studies will be made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations.\r\n\r\nFor further details of the FRANC project please also see Dance et al. (2019) article in the online resources linked to from this record: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project.\r\n\r\nGrant ref: NE/K008900/1"
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                    "abstract": "A collection of products from rain radars operated by the Met Office and other European agencies for the UK and Europe. This collection includes rain composite plots and data for the UK and Europe, plus single site radar data including rain rate data, single and dual-polar data products. \r\n\r\nThese were produced by the Met Office's Nimrod system. Nimrod is a fully automated system for weather analysis and nowcasting based around a network of C-band rainfall radars. This dataset has the fine-resolution analyses of rain rate for the UK and Europe.\r\n\r\nThe UK has a network of C-band rainfall radars and data form these are processed by the Met Office NIMROD system. Four or five radar scans at different elevations at each site are processed to give the best possible estimate of rainfall at the ground. The main quality checking method is routine evaluation using rain gauges as ground truth.\r\n\r\nThe BADC holds the analyses of rainfall rate at a time resolution of 5 or 15 minutes. Data are available from late 2002. Images are available for the UK as well as a further image including neighbouring European countries from 1999. Data files are available on a 1 km and 5 km Cartesian grid. Single radar site data are available for 2 and 5 km Cartesian grids for various UK radar sites.\r\n\r\nThe value of radar-based data from the Nimrod system has been highlighted repeatedly. For example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events. The 2 km data files reach to 100 km from the radar, the 5km files to 250 km.\r\n\r\nDetailed radar site location are given in the Met Office Weather Radar Factsheet.\r\n\r\nTime resolution is 5 or 15 minutes depending on the product.\r\n\r\nVarious scripts have been made available under the software directory to aid use of these data, including within GIS applications."
                },
                {
                    "ob_id": 31970,
                    "uuid": "333bf4303034426a857515a768387e4f",
                    "short_code": "coll",
                    "title": "Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): rain radar helical scan data, assimilation versus model residuals and ensemble member model output.",
                    "abstract": "The Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC) project undertook a series of studies to design and test efficient and effective ways of assimilating moisture information from observations that respect the intricate dynamical and physical relationships that operate in the atmosphere. The aim of this work was, if successful, that such new approaches would allow better use of cloud and rain affected observations than previously. Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Studies were also made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations. To these ends this collection contains data from two of the studies within this project plus helical scan data from the Met Office's Wardon Hill radar utilised by the project team.\r\n\r\nThe two datasets from the project team cover ensemble member output from runs of the Met Office's Unified Model conducted to support the project and Doppler radar radial wind observations and associated observation-minus-model residuals from the Met Office UKV 3D Var assimilation scheme. Please see the individual datasets for additional information.\r\n\r\nFor further details of the FRANC project please also see Dance et al. (2019) article in the online resources linked to from this record: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project."
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            "title": "CRU JRA v2.1: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2019.",
            "abstract": "The CRU JRA V2.1 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2019.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.04 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001",
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            "dataLineage": "The CRU JRA data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. This is the first formal release and was provided to CEDA in October 2020.",
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                "abstract": "The CRU JRA (Japanese reanalysis) data is a replacement to the CRU NCEP dataset, CRU JRA data follows the style of Nicolas Viovy's original dataset rather than that which is available from UCAR.\r\n\r\nThe CRU JRA dataset is based on the JRA-55 reanalysis dataset and aligned where appropriate with the CRU TS dataset version 3.26 (1901-2017).\r\n\r\nAll JRA variables are regridded from their native TL319 Gaussian grid to the CRU regular 0.5° x 0.5° grid, using the g2fsh spherical harmonics routine from NCL (NCAR Command Language), based on the 'Spherepack' code. The exception is precipitation, which is regridded using ESMF 'nearest neighbour': all other algorithms tried exhibited unwanted artifacts.\r\n\r\nThe JRA-55 reanalysis dataset starts in 1958. The years 1901-1957 are constructed using randomly-selected years between 1958 and 1967. Where alignment with CRU TS occurs, the relevant CRU TS data is used.\r\n\r\nOf the ten variables listed above, the last four do not have analogs in the CRU TS dataset. These are simply regridded, masked for land only, and output as CRUJRA. The other six are aligned with CRU TS as follows:\r\n\r\nTMP is aligned with CRU TS TMP. A monthly mean for the JRA data is\r\ncalculated and compared with the equivalent CRU TS mean. The difference\r\nbetween the means is added to every JRA value.\r\n\r\n---\r\n\r\nTMAX and TMIN are aligned with CRUJRA TMP and CRU TS DTR. Firstly, at\r\neach time step, the TMAX-TMP-TMIN triplets are checked and adjusted so\r\nthat TMAX is always >= TMP, and TMIN is always <= TMP. This triplet\r\nalignment is prioritised above DTR alignment. Secondly, monthly JRA DTR\r\nis calculated by first establishing the daily maxima and minima (max and\r\nmin of the subdaily values in TMAX and TMIN respectively), then monthly\r\nmaxima and minima, (means of the daily DTR values), giving JRA monthly\r\nDTR. This is compared with CRU TS DTR and the fractional difference\r\n(factor) calculated as (CRU TS DTR) / (JRA monthly DTR). This factor is\r\nthen used to adjust the DTR of each pair of subdaily TMAX and TMIN\r\nvalues, though not if the triplet alignment would be broken.\r\n\r\n---\r\n\r\nPRE is aligned with CRU TS PRE and WET (rain day counts). Firstly, the\r\nmonthly total precipitation is calculated for JRA and compared to CRU TS\r\nPRE; an adjustment factor is acquired (crupre/jrapre) and all values\r\nadjusted. Precipitation amounts are now aligned at a monthly level, and\r\nthis alignment is prioritised above WET alignment. Secondly, the number\r\nof rain days is calculated for JRA: a day is declared wet if the total\r\nprecipitation is equal to, or exceeds, 0.1mm (the same threshold as CRU\r\nTS WET). If JRA has more wet days than CRU TS, then the driest of those\r\nare reduced to a random amount below 0.1 (an adjustment factor is\r\ncalculated and applied to each time step, to preserve the subdaily\r\ndistribution). If JRA has fewer wet days than CRU TS, then sufficient\r\ndry days are set to a random amount equal to or closely above 0.1mm,\r\nagain using an adjustment factor to preserve the subdaily distribution. \r\nWhere wet day alignment threatens precipitation alignment, the process\r\nis abandoned and the cell/month reverts to the previously-aligned\r\nprecip version. Exception handling is very complicated and cannot be\r\nsummarised here.\r\n\r\n---\r\n\r\nSPFH is aligned with CRU TS VAP. VAP is converted to SPFH, and JRA mean\r\nmonthly SPFH is calculated. The fractional difference (factor) is\r\ncalculated as (CRU TS SPFH) / (JRA monthly SPFH), this factor is then\r\napplied to the JRA subdaily humidity values.\r\n\r\n---\r\n\r\nDSWRF is aligned with CRU TS CLD. CLD is converted to shortwave\r\nradiation, and JRA mean monthly DSWRF is calculated. The fractional\r\ndifference (factor) is calculated as (CRU TS SWR) / (JRA monthly DSWRF),\r\nthis factor is then applied to the JRA subdaily radiation values.\r\n\r\n---\r\n\r\nWhere appropriate, CRUJRA values are kept within physically-appropriate\r\nconstraints (such as negative precipitation), which could result from\r\nregridding as well as adjustments."
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                    "title": "CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data.",
                    "abstract": "This is a collection of the University of East Anglia Climatic Research Unit (CRU) Japanese Reanalysis (JRA) data. The CRU JRA data are 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models.\r\n\r\nThe dataset is constructed by combining data from the Japanese Reanalysis data produced by the Japanese Meteorological Agency (JMA) and adjusted where possible to align with the CRU TS data (these 'ten meteorological variables' are not the same ten available from CRU TS).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRUNCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRUNCEP dataset rather than that which is available from UCAR."
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            "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci):   Permafrost extent for the Northern Hemisphere, v2.0",
            "abstract": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.  \r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year.",
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                "explanation": "Data are as provided by the Permafrost CCI project.  For further quality information see the permafrost CCI website and linked documentation.",
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                "abstract": "The  Permafrost CCI project is  establishing Earth Observation (EO) based products for the permafrost Essential Climate Variable (ECV) spanning the last two decades. Since ground temperature  and  thaw  depth  cannot be  directly  observed  from  space-borne  sensors,  a  variety  of satellite  and  reanalysis  data  are  combined  in  a  ground  thermal  model.  The  algorithm  uses  remotely sensed  data  sets  of  Land  Surface  Temperature  (MODIS  LST/  ESA  LST  CCI)  and  landcover  (ESA LandcoverCCI)  to  drive  the  transient  permafrost  model  CryoGrid  2,  which  yields  thaw  depth  and ground  temperature  at  various  depths,  while  ground  temperature  forms  the  basis  for  permafrost fraction.\r\n\r\nInput data: MODIS Land surface temperature is used as the main input for the L4 production for 2003-2018 data. Sensors of auxiliary data are listed in the meta data.\r\nDownscaled and bias corrected ERA reanalyses data based on statistics of the overlap period between ERA reanalysis and MODIS LST are used for data before 2003. Sensors of auxiliary data are listed in the meta data."
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                    "abstract": "The Permafrost Climate Change Initiatve Project (Permafrost_cci) is part of the European Space Agency's Climate Change Initiative Programme.   The ultimate objective of Permafrost_cci is to develop and deliver permafrost maps of Essential Climate Variable products, primarily derived from satellite measurements."
                }
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                    "abstract": "This collection of data forms the Permafrost Climate Research Data Package (CRDP v1), which comprises the Version 2.0 Permafrost data products from the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project.     Data products include Ground Temperature, Active Layer Thickness and Permafrost Extent for the Northern Hemisphere (north of 30°) for the period 1997-2018. They are derived from a thermal model driven and constrained by satellite data. Gridded products are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures, as well as the maximum depth of seasonal thaw, which corresponds to the active layer thickness."
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            "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year.",
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                    "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)."
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            "title": "UKCP Global (60km) - European Circulation Indices",
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            "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1",
            "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.\r\n\r\nThe dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in NetCDF files, and it includes 4 layers: sum of burned area, standard error, fraction of burnable area and fraction of observed area. For further information on the product and its format see the Product User Guide.",
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                    "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change."
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                    "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid covering 1901-2018. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Indices are available on an annual, and for some a monthly, basis. Some indices use a reference period to calculate thresholds, and for these, we provide versions using 1961-90 and 1981-2010.\r\n\r\nThe indices are available in NetCDF files, with one index per file and separate files for annual and monthly values, as well as the different reference periods if appropriate. The codes used to create the dataset are available online, and a wide number of analysis plots are on the dataset homepage. For a detailed description of the methods behind the dataset, please see the paper in Details/Docs."
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            "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Obs4MIPS monthly-averaged sea surface temperature data, v2.1",
            "abstract": "This dataset contains monthly 1 degree averages of sea surface temperature data in Obs4MIPS format, from the European Space Agency (ESA)'s Climate Change Initiatve (CCI) Sea Surface Temperature (SST) v2.1 analysis.\r\n\r\nThe data covers the period from 1981-2017, with the data from 1981 to 2016 coming from the Sea Surface Temperature (SST) project of the ESA CCI project. The data for 2017 were generated using the same approach but under funding from the Copernicus Climate Change Service (C3S).\r\n\r\nThis particular product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x",
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            "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving in the context of the ESA CCI Open Data Portal project and the National Centre for Earth Observation (NCEO).  The data for 2017 were generated using the same approach but under funding from the Copernicus Climate Change Service (C3S).",
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            },
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                "short_code": "comp",
                "title": "Derivation of the ESA CCI SST v2.1 Obs4MIPS format data",
                "abstract": "The Obs4MIPs dataset produced from the ESA Climate Change Initiatve Sea Surface Temperature v2.1 analysis, has been obtained from post-processing of >4 trillion individual skin sea surface\r\ntemperature (SST-skin) retrievals from infra-red imagery of sensors on Earth observing\r\nsatellites. \r\n\r\nThe main post-processing steps are:\r\n 1) the addition to the SST-skin of adjustments to daily-mean SST at a nominal depth of\r\n20 cm, resulting in an estimate of “SST-depth” \r\n2) the uncertainty-weighted combination of SST-depth values from all the sensors to\r\nmake a daily-gap-free SST analysis at 0.05 degree latitude-longitude resolution\r\n3)  the averaging and uncertainty propagation from the daily SST analysis to monthly 1 degree\r\ndata\r\n\r\nIn the processing, ERA-Interim fields are used as ancillary information with minimized\r\ninfluence on the SST outcome. Consistent fields of sea ice fraction and sea fraction are also\r\nprovided. The sea ice fraction is obtained from the Ocean and Sea-Ice Satellite Application\r\nFacility [and the sea fraction accounts for ice from the same product and also the\r\npresence or not of land in the cell, determined from a surface classification dataset.\r\n\r\nFor a high-level description of the processing steps see Merchant et al, 2019"
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                    "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme,  It aims to accurately mapping the surface temperature of the global oceans  using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST."
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                    "short_code": "coll",
                    "title": "National Centre for Earth Observation (NCEO) partnered datasets",
                    "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)."
                },
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                    "ob_id": 11005,
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                    "short_code": "coll",
                    "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)",
                    "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper:  Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page."
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            "title": "HadCRUT.5.0.0.0: Ensemble near-surface temperature anomaly grids and time series",
            "abstract": "HadCRUT5 (Met Office Hadley Centre/Climatic Research Unit global surface temperature anomalies, version 5) is a gridded dataset of global historical near-surface air temperature anomalies since the year 1850. It has been developed and maintained by the Met Office Hadley Centre and University of East Anglia Climatic Research Unit. Air temperature information over land is derived from CRUTEM5 monthly average meteorological station temperature series, an expanded compilation of station series with revised quality control methods. Temperatures over ocean are derived from the HadSST4 sea-surface temperature dataset, including revised assessments of instrumental biases. Temperature data are presented as monthly average near-surface temperature anomalies, relative to the 1961-1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018, with derived global and hemispheric time series.\r\n\r\nTwo variants of the dataset are provided. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200‐member ensemble accompanied by additional uncertainty information.\r\n\r\nMonthly updates to HadCRUT5 are available from the Met Office Hadobs website (see documentation links).",
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            "abstract": "CRUTEM (Climatic Research Unit TEMperature) is a gridded dataset of global historical near-surface air temperature anomalies over land at a monthly timescale. It is a collaborative product of the Climatic Research Unit at the University of East Anglia, the Met Office Hadley Centre and the National Centre for Atmospheric Science. CRUTEM also contributes the land air temperature station data to the global (land and ocean) temperature dataset called HadCRUT.\r\n  \r\nCRUTEM5 is the fifth major version of the dataset, covering the time period from 1850, with a spatial resolution of 5° latitude by 5° longitude and a monthly-mean time resolution. The gridded temperature anomaly fields are based on a compilation of monthly-mean temperature observational records from weather stations. This compilation contains 10639 station records, but only 7983 records had the necessary coverage to be used for producing the gridded dataset. Anomalies are differences from average conditions in the 1961-1990 period. Hemispheric and global mean time series of land air temperature anomalies are also provided.",
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                "abstract": "For details about the construction of the CRUTEM dataset, please refer to these papers:\r\nOsborn, T.J. and Jones, P.D., 2014: The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth System Science Data 6, 61-68, doi:10.5194/essd-6-61-2014\r\nOsborn TJ, Jones PD, Lister DH, Morice CP, Simpson IR, Winn J, Hogan E and Harris IC (2020) Land surface air temperature variations across the globe updated to 2019: the CRUTEM5 dataset. Journal of Geophysical Research."
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            "title": "Cape Verde Atmospheric Observatory: High-precision long-term atmospheric measurements of greenhouse gases (CO, CO2, N2O and CH4 ) using Off-Axis Integrated-Cavity Output Spectroscopy  (OA-ICOS) (2011 onwards).",
            "abstract": "Data from observations made at the Cape Verde Atmospheric Observatory (CVAO) which exists to advance understanding of climatically significant interactions between the atmosphere and ocean and to provide a regional focal point and long-term data. \r\n\r\nThe observatory is based on Calhau Island of São Vicente, Cape Verde at 16.848N, 24.871W, in the tropical Eastern North Atlantic Ocean, a region which is data poor but plays a key role in atmosphere-ocean interactions of climate-related and biogeochemical parameters including greenhouse gases. It is an open-ocean site that is representative of a region likely to be sensitive to future climate change, and is minimally influenced by local effects and intermittent continental pollution. \r\n\r\nSince November 2011, real-time N2O (Nitrous Oxide) and CO (Carbon Monoxide) concentrations have been simultaneously and continuously measured using an Off-Axis Integrated-Cavity Output Spectroscopy (OA-ICOS) analyser (Los Gatos Inc).  In November 2012, a Greenhouse Gas Analyser (GGA) using the same fundamental measuring technique was added and placed in series to measure CO2 (Carbon Dioxide) and CH4 (Methane) concentrations. Both devices are configured to sample at a frequency of 1Hz and both have the precision and accuracy to conform to measurement recommendations as defined by Global Atmosphere Watch (GAW).  \r\n\r\nThe dataset contains hourly measurements of CO, CO2, N2O and CH4.  Data were collected by collaboration between the University of Exeter and the Max Planck Institute for Biogeochemistry.",
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