Procedure Computation List

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        {
            "ob_id": 23975,
            "uuid": "960b7f0b390848418c952b01c1d80cea",
            "title": "National Centers for Environmental Prediction (NCEP) running: experiment decadal1998 using the CFSv2-2011 model.",
            "abstract": "National Centers for Environmental Prediction (NCEP) running the decadal1998 experiment using the CFSv2-2011 model. See linked documentation for available information for each component.\n\nPlease note the following are not recorded within the Earth System Documentation (ES-DOC) site : decadal1998 experiment and simulation details.",
            "keywords": "CMIP5, WCRP, climate change, NOAA-NCEP, CFSv2-2011, decadal1998, atmos, land, landIce, ocean, seaIce, fx, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 23978,
            "uuid": "59673d8a1c2242d3bc0b8b8812e63e7e",
            "title": "National Centers for Environmental Prediction (NCEP) running: experiment decadal1981 using the CFSv2-2011 model.",
            "abstract": "National Centers for Environmental Prediction (NCEP) running the decadal1981 experiment using the CFSv2-2011 model. See linked documentation for available information for each component.\n\nPlease note the following are not recorded within the Earth System Documentation (ES-DOC) site : decadal1981 experiment and simulation details.",
            "keywords": "CMIP5, WCRP, climate change, NOAA-NCEP, CFSv2-2011, decadal1981, atmos, land, landIce, ocean, seaIce, fx, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 23981,
            "uuid": "05d65ebf6d9f4517b2e5b3aa2ca27849",
            "title": "National Centers for Environmental Prediction (NCEP) running: experiment decadal1983 using the CFSv2-2011 model.",
            "abstract": "National Centers for Environmental Prediction (NCEP) running the decadal1983 experiment using the CFSv2-2011 model. See linked documentation for available information for each component.\n\nPlease note the following are not recorded within the Earth System Documentation (ES-DOC) site : decadal1983 experiment and simulation details.",
            "keywords": "CMIP5, WCRP, climate change, NOAA-NCEP, CFSv2-2011, decadal1983, atmos, land, landIce, ocean, seaIce, fx, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 23984,
            "uuid": "827396232f3845d8a2ff5273e5b05943",
            "title": "National Centers for Environmental Prediction (NCEP) running: experiment decadal1985 using the CFSv2-2011 model.",
            "abstract": "National Centers for Environmental Prediction (NCEP) running the 10-year hindcast/prediction initialized in year 1985 (decadal1985) experiment using the CFSv2-2011 model. See linked documentation for available information for each component.\n\nPlease note the following are not recorded within the Earth System Documentation (ES-DOC) site : simulation details.",
            "keywords": "CMIP5, WCRP, climate change, NOAA-NCEP, CFSv2-2011, decadal1985, atmos, land, landIce, ocean, seaIce, fx, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24015,
            "uuid": "c326f15710244ce486892bfe5c1ff1ae",
            "title": "UKCP09 data computation",
            "abstract": "These UKCP09 data were generated using outputs from a Met-Office Hadley Centre (MOHC) regional climate model.\r\n\r\nSee the climate projections report for full details of the methodology.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24685,
            "uuid": "f5f5d72d28a54b648f27fb4ead63530b",
            "title": "UKCP09 data computation with POLCOMS model",
            "abstract": "These UKCP09 data were generated using outputs from a Met-Office Hadley Centre (MOHC) regional climate model simulation to drive the Atlantic Margin application shelf sea model of the Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) (now the National Oceanography Centre Liverpool; NOCL).\r\n\r\nThe POLCOMS shelf sea model is well established having been used operationally by the UK Met Office since 2002 and used in many scientific projects by NOCL (formerly POL) and the UK research community. It has been validated with observations from marine weather stations.\r\n\r\nSee the marine and coastal projections report for full details.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24686,
            "uuid": "49c23996ab514b0e8e936338ba2caca1",
            "title": "UKCP09 data computation for storm surge",
            "abstract": "The UKCP09 storm surge projections use the National Oceanography Centre storm surge model (POLCS3). This model is currently used to provide coastal forecasts of surge in the UK, as part of the UK Coastal Monitoring and Forecasting service, to support the issue of coastal flood warnings by the Environment Agency and other similar bodies.\r\n\r\nUse of POLCS3 has shown that the model is relatively reliable when driven by realistic weather information, and in the Thames Estuary 2100 Case studies it was shown to be capable of replicating observed extreme storm surges in the southern North Sea and Thames Estuary.\r\n\r\nSee the technical note on storm projections for full details.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24703,
            "uuid": "c96953ef56674498b66bfaf1fcf1f206",
            "title": "AGB-MEX",
            "abstract": "The map was generated by means of a combination of the probabilistic outputs from a Maximum Entropy (MaxEnt) algorithm. Approximately 16.000 field inventory plots CONAFOR, INFyS) were used in combination with SAR (JAXA, ALOS PALSAR) and optical data (NASA, MODIS VI), as well as a digital elevation model (NASA, SRTM). ",
            "keywords": "",
            "inputDescription": 1,
            "outputDescription": 2,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24711,
            "uuid": "b0462f6ec644428d8cafbe4f20242d5a",
            "title": "Weather at home - HadAM3P-HadRM3P",
            "abstract": "UK Metoffice PRECIS model, Global HadAM3P with embedded regional HadRM3P",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24916,
            "uuid": "4b244947c9d547e9bc6761e01b78117f",
            "title": "Poles Apart computation",
            "abstract": "The UK Met Office third generation Hadley Centre Global Environment Model (HadGEM3) run in atmosphere only mode (prescribed sea ice and sea surface temperatures) coupled to the United Kingdom Chemistry & Aerosols (UKCA) model. HadGEM3 was run using the GA4.0 and GL4.0 physics configurations (See Walters et al., 2014, Geo. Mod. Dev)",
            "keywords": "UKCA, HadGEM3",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24928,
            "uuid": "e768f449cbf84c3caba05ddc63c44989",
            "title": "MIPclouds Algorithm",
            "abstract": "The purpose of the Algorithm Theoretical Basis Document (ATBD) is to present the algorithm and technical\r\ndetails necessary to realise a processor for the retrieval of cloud properties form the Level 1b (L1B) data of\r\nESA instrument MIPAS on-board the ENVISAT satellite. The infrared spectra measured by MIPAS (main information\r\nof L1B) include sufficient information to retrieve various cloud parameter. Recent publications have\r\nalready shown the large potential of the dataset ([11], [12], [5], [6]). But so far no validated and consolidated\r\nMIPAS cloud product is available for the scientific community. Consequently the development of a cloud parameter\r\nprocessor for MIPAS and the application to the time series is highly desired.\r\nThe proposed MIPAS cloud processor follows the requirements of the statement of work of the ESA-ITT\r\nAO/1-5255/06/I-OL. Therefore the development and application of the processor is so far restricted to the first\r\n\r\nDetailed information on the data available at:measurement period of MIPAS from July 2002 to March 2004 (spectral high resolution (HR) mode). After\r\n2004 MIPAS is operating with slightly reduced resolution (RR-mode). A transfer of the prototype processor\r\nfor application of the RR mode has been taken into account during the algorithm development. It is desirable,\r\nthat this will be also considered in the realisation phase of the prototype processor.\r\nPrimary task of the software is to retrieve cloud properties from the MIPAS L1B spectra. This contains various\r\nitems of interest:\r\n• the detection of cloudy spectra in the L1B data\r\n• to classify various cloud types in the measurements (e.g. polar stratospheric clouds, liquids and ice\r\nclouds)\r\n• to retrieve cloud top information on height, temperature, pressure\r\n• to retrieve profile information on cloud parameters (e.g.. extinction, ice water path or integrated limb\r\npath quantities like volume/area density path)\r\n• information on microphysical parameter like effective radius of the particle size distribution (PSD) or\r\nVolume and Area densities.\r\nThe retrievability of the cloud parameters like the ones above have been investigated in a feasibility study of\r\nthe MIPclouds project\r\n\r\n1) Spang, R., Remedios, J. J., and Barkley, M., Colour Indices for the Detection and Differentiation of Cloud Types in Infra-red Limb Emission Spectra, Adv. Space Res., 33, pp.1041-1047, 2004.\r\n2) Spang, R., Arndt, K., Dudhia, A., Höpfner, M., Hoffmann, L., Hurley, J., Grainger, R. G., Griessbach, S., Poulsen, C., Remedios, J. J., Riese, M., Sembhi, H.,  Siddans, R., Waterfall, A., and Zehner, C.: Fast cloud parameter retrievals of MIPAS/Envisat, Atmos. Chem. Phys., 12, 7135-7164, doi:10.5194/acp-12-7135- 2012, 2012.\r\n 3) Sembhi, H., Remedios, J., Trent, T., Moore, D. P., Spang, R., Massie, S., and Vernier, J.-P.: MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP, Atmos. Meas. Tech., 5, 2537- 2553, doi:10.5194/amtd-5-2537-2012, 2012\r\n4) Hurley, J., Dudhia, A., and Grainger, R. G.: Retrieval of macrophysical cloud parameters from MIPAS: algorithm description, Atmos. Meas. Tech., 4, 683- 704, doi:10.5194/amt-4-20 683-2011, 2011.\"",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24929,
            "uuid": "1d93ea930ec24b92a408c92e41150c70",
            "title": "MIPclouds Algoritm",
            "abstract": "The purpose of the Algorithm Theoretical Basis Document (ATBD) is to present the algorithm and technical\r\ndetails necessary to realise a processor for the retrieval of cloud properties form the Level 1b (L1B) data of\r\nESA instrument MIPAS on-board the ENVISAT satellite. The infrared spectra measured by MIPAS (main information\r\nof L1B) include sufficient information to retrieve various cloud parameter. Recent publications have\r\nalready shown the large potential of the dataset ([11], [12], [5], [6]). But so far no validated and consolidated\r\nMIPAS cloud product is available for the scientific community. Consequently the development of a cloud parameter\r\nprocessor for MIPAS and the application to the time series is highly desired.\r\nThe proposed MIPAS cloud processor follows the requirements of the statement of work of the ESA-ITT\r\nAO/1-5255/06/I-OL. Therefore the development and application of the processor is so far restricted to the first\r\n\r\nDetailed information on the data available at:measurement period of MIPAS from July 2002 to March 2004 (spectral high resolution (HR) mode). After\r\n2004 MIPAS is operating with slightly reduced resolution (RR-mode). A transfer of the prototype processor\r\nfor application of the RR mode has been taken into account during the algorithm development. It is desirable,\r\nthat this will be also considered in the realisation phase of the prototype processor.\r\nPrimary task of the software is to retrieve cloud properties from the MIPAS L1B spectra. This contains various\r\nitems of interest:\r\n• the detection of cloudy spectra in the L1B data\r\n• to classify various cloud types in the measurements (e.g. polar stratospheric clouds, liquids and ice\r\nclouds)\r\n• to retrieve cloud top information on height, temperature, pressure\r\n• to retrieve profile information on cloud parameters (e.g.. extinction, ice water path or integrated limb\r\npath quantities like volume/area density path)\r\n• information on microphysical parameter like effective radius of the particle size distribution (PSD) or\r\nVolume and Area densities.\r\nThe retrievability of the cloud parameters like the ones above have been investigated in a feasibility study of\r\nthe MIPclouds project\r\n\r\n1) Spang, R., Remedios, J. J., and Barkley, M., Colour Indices for the Detection and Differentiation of Cloud Types in Infra-red Limb Emission Spectra, Adv. Space Res., 33, pp.1041-1047, 2004.\r\n2) Spang, R., Arndt, K., Dudhia, A., Höpfner, M., Hoffmann, L., Hurley, J., Grainger, R. G., Griessbach, S., Poulsen, C., Remedios, J. J., Riese, M., Sembhi, H.,  Siddans, R., Waterfall, A., and Zehner, C.: Fast cloud parameter retrievals of MIPAS/Envisat, Atmos. Chem. Phys., 12, 7135-7164, doi:10.5194/acp-12-7135- 2012, 2012.\r\n 3) Sembhi, H., Remedios, J., Trent, T., Moore, D. P., Spang, R., Massie, S., and Vernier, J.-P.: MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP, Atmos. Meas. Tech., 5, 2537- 2553, doi:10.5194/amtd-5-2537-2012, 2012\r\n4) Hurley, J., Dudhia, A., and Grainger, R. G.: Retrieval of macrophysical cloud parameters from MIPAS: algorithm description, Atmos. Meas. Tech., 4, 683- 704, doi:10.5194/amt-4-20 683-2011, 2011.\"",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24942,
            "uuid": "01c5c729c64046af967a201b9652437a",
            "title": "UKCP09 gridded land surface climate observations methodology",
            "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables — for example, for the temperature variables the number of stations rises from about 270 in 1914 to 600 in the mid-1990s, before falling to 450 in 2006.\r\n\r\nRegression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage. The methods used to generate the monthly and annual grids are described in more detail in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,041-1,054, which can be downloaded here: Generation of monthly gridded data sets for a range of climatic variables over the UK (PDF, 590 kB). The methods used to generate the daily grids are described in more detail in the report 'The generation of the daily gridded data sets of temperature and rainfall for the UK' (PDF, 2.27 MB).\r\n\r\nThe 5 × 5 km grids of the 1961-1990 baseline climate average available on these webpages are simply calculated by averaging or summing the 30 monthly or annual gridded data sets for each variable. They are not the same as the 1 × 1 km data sets presented on our UK Climate pages. The methods used to generate the 1 × 1 km data sets for 30-year average periods, which were used to normalise the station data prior to the generation of monthly and annual grids, are described in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,023-1,039, which can be downloaded here: Development of a new set of long-term averages for the UK\r\n(PDF, 1.08 MB)\r\n\r\nTo help users combine the 5 × 5 km baseline data sets with the UK Climate Projections, values of the 1961-1990 baseline climate average have also been generated for the 25 × 25 km grid boxes of the HadRM3 regional climate model and for administrative regions and river basins.\r\n\r\nEach 25 × 25 km grid box value is an average of the 5 × 5 km grid cell values that fall within it. Averages have been calculated for each month, season and the year as a whole (17 data sets). For the days of frost and days of rain variables the seasonal and annual averages are the total of the individual monthly averages. For the remaining variables the seasonal and annual averages are the mean of the monthly averages (allowing for differences in month length). To facilitate combining the baseline data with the UKCP09 climate projections, the 25 km baseline averages for rainfall have been expressed in units of millimetres per day (rather than total millimetres, as for the 5 km data sets).\r\n\r\nEach regional value is also an average of the 5 × 5 km grid cell values that fall within it. Monthly averages (12 values) have been calculated for each monthly variable and an annual average has been calculated for each annual variable. As with the 25 km data, the averages for rainfall have been expressed in units of millimetres per day.",
            "keywords": "UKCP09, gridded observations",
            "inputDescription": 3,
            "outputDescription": null,
            "softwareReference": null,
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        },
        {
            "ob_id": 24957,
            "uuid": "f66c26bcf7684ed29d14a88825884a19",
            "title": "BITMAP: Western Disturbance Tracks Algorithm",
            "abstract": "Tracks generated using a bespoke tracking algorithm, identifying and linking upper-tropospheric vortices (described fully in Hunt et al, 2018, QJRMS - see linked documentation to this record), using data derived from ERA-Interim reanalysis data and selected CMIP5 model runs (with some modifications such as the vorticity level used).\r\n\r\nIn essence the algorithm works by:\r\n\r\n1. locating all mid-tropospheric relative vorticity maxima;\r\n\r\n2. group multiple peaks by using a neighbourhood filter, then integrate to find the parent vortex centre;\r\n\r\n3. link potential candidates together across time steps to form tracks using a nearest-neighbour approach incorporating local wind speed;\r\n\r\n4. surviving tracks are filtered by duration (> 2 days) and location (must pass through [20-36.5N, 60-80E]).",
            "keywords": "BITMAP, India, Western disturbances, Vortices",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 24972,
            "uuid": "7176d16ce6164f70980a3a7fe6ac9414",
            "title": "Amazonica TOMCAT",
            "abstract": "Amazonica TOMCAT.\r\nTOMCAT is a Eulerian, offline three-dimensional (3-D) chemical transport model (CTM). For the simulations presented here, it was used with a horizontal resolution of 2.8° × 2.8° longitude by latitude, with 60 hybrid σ-p vertical levels up to 0.1 hPa. The model meteorology, including winds, temperature, and pressure data, is taken from 6-hourly ERA-Interim analyses provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) [Dee et al., 2011] and transformed onto the TOMCAT model grid using a model time step of 30 min. ",
            "keywords": "Amazonica, carbon, Amazon, TOMCAT",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25030,
            "uuid": "c940eb163bf743369ea9ce7a3584fbd8",
            "title": "The Organization of Tropical Rainfall: 4-km grid Met-UM simulations of tropical convection",
            "abstract": "Model was run on ARCHER, the UK supercomputer for academic use and comprises of model output from 11 runs of the Met Office Unified Model (MetUM) in idealised radiative-convective equilibrium mode. All runs have fixed constant sea surface temperature (SST) and doubly-periodic lateral boundary conditions. These runs were used in several papers on convective self-aggregation: principally in Holloway and Woolnough (2016, Journal of Advances in Modeling Earth Systems) but also in Holloway (2017, Journal of Advances in Modeling Earth Systems).\r\n\r\nAll runs use the \"\"New Dynamics\"\" dynamical core, MetUM version 7.5, as described in Holloway and Woolnough (2016). The simulations are run with 4-km horizontal grid spacing. They all have a horizontal domain size of 576 km X 576 km (or 144 X 144 grid points), with 70 vertical levels. They were all run for 40 days except for the two runs with lower SSTs (295 K and 290 K) which were run for only 20 days.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25032,
            "uuid": "f59ddf691aff484eadc2f0b98ec49635",
            "title": "The Organization of Tropical Rainfall: Realistic MetUM model output for convective aggregation studies",
            "abstract": "MetUM runs were performed on either HECToR or ARCHER, the UK supercomputers for academic use. All runs use the \"\"New Dynamics\"\" dynamical core, MetUM version 7.5, as described in Holloway (2017). The simulations are run with 4-km horizontal grid spacing. They all have a horizontal domain size of 20 degrees latitude X 20 degrees longitude (or 574 X 574 grid points, although the grid points in the outer 8 points on all sides, the \"\"rim\"\", should be discarded before analysis), with 70 vertical levels. All runs are initialised from operational analyses from the European Centre for Medium-Range Weather Forecasting (ECMWF) taken from actual cases. Lateral boundary conditions are comprised of 6-hourly ECMWF analyses, and the model is relaxed to these conditions in and near the outer rim as described in Holloway (2017). Sea surface temperatures (SST) are taken from the initial ECMWF analysis and are held constant in time for the 15 days (but are not constant in space). There are small land regions in four of the case studies which include an interactive land surface model.\r\n\r\nEach simulation was run for 15 days. The model output includes hourly model-level prognostic variables (temperature, specific humidity, pressure, wind components, liquid water, ice water) as well as some model-level increments to temperature and specific humidity. There are also many fields containing surface variables and fluxes (averaged over each hour or every 15 minutes). Note that the \"\"control\"\" simulations have slightly more available data than the other four runs in each of the five case studies.\r\n\r\n\r\nThe five cases each span 10°S–10°N latitude and have the following latitude ranges and date ranges:\r\n\r\n65°E–85°E 25 Jan 2009 to 8 Feb 2009 \r\n70°E–90°E 31 Jan 2009 to 14 Feb 2009 \r\n75°E–95°E 4 Apr 2010 to 18 Apr 2010 \r\n150°E–170°E 19 Nov 2008 to 3 Dec 2008 \r\n165°E–185°E 2 May 2009 to 16 May 2009\"",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25045,
            "uuid": "4eb66be638e04d759939a7af571f18ad",
            "title": "Weather@Home2 climate simulation environment uses the HadAM3P Atmosphere-only General Circulation Model (AGCM) with an embedded Regional Climate Model (RCM)",
            "abstract": "The Weather@Home2 climate simulation environment uses the HadAM3P Atmosphere-only General Circulation Model (AGCM) \nwith an embedded Regional Climate Model (RCM) variant, HadRM3P, both from the UK Met Office Hadley Centre. Unlike the original \nWeather@Home, Weather@Home2 uses a more sophisticated land-surface model, MOSES2.  \n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25056,
            "uuid": "07f4246e004a426aafe524aeda2147ed",
            "title": "High accuracy line intensity data for carbon dioxide",
            "abstract": "The Lodi-Tennyson method [33] for validating linelists on a purely theoretical basis relies on the use of accurate, ab initio transition intensity calculations require an accurate procedures for obtaining nuclear motion wavefunctions together with the use of at least two DMSs and two PESs. This is described in this paperE. Zak, J. Tennyson, O.L. Polyansky, L. Lodi, S.A. Tashkun and V.I. Perevalov, A room temperature CO2 line list with ab initio computed intensities, J. Quant. Spectrosc. Rad. Transf, 177, 31-42 (2016). doi  10.1016/j.jqsrt.2015.12.022 \r\n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25060,
            "uuid": "23c28b22413d427b923d11142e854fda",
            "title": "S-RIP reanalysis",
            "abstract": "Three dimensional atmospheric fields were first downloaded from reanalysis data centers. Then, zonal-mean diagnostics were computed onto two distinct grids. The first is the grid originally provided by each data center. The second is a common 2.5 by 2.5 degrees grid onto which each data set is interpolated using bilinear interpolation. All diagnostics are performed using the same numerical methods for each reanalysis data set.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25089,
            "uuid": "997172def7dd45b6ad321ce66c5fafd0",
            "title": "Antarctic Mesoscale Prediction System (AMPS): WRF V3.3.1 with polar modifications",
            "abstract": "Weather Research and Forecasting model (WRF) V3.3.1 with polar modifications",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25303,
            "uuid": "3a969d40debe4e78818337529906a4f8",
            "title": "The CNRM-CERFACS team running: CCMI-1 experiment refC1 using the MOCAGE model.",
            "abstract": "The CNRM-CERFACS team running the CCMI-1 refC1 experiment using the MOCAGE model.The CNRM-CERFACS team consisted of the following agencies: Centre National de Recherches Meteorologiques (CNRM) and Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS). ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, CNRM-CERFACS, MOCAGE, refC1, atmos, hr, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25304,
            "uuid": "6f65d39c97df449f95c8e1b4eac38b8e",
            "title": "The CNRM-CERFACS team running: CCMI-1 experiment refC2 using the MOCAGE model.",
            "abstract": "The CNRM-CERFACS team running the CCMI-1 refC2 experiment using the MOCAGE model.The CNRM-CERFACS team consisted of the following agencies: Centre National de Recherches Meteorologiques (CNRM) and Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS).",
            "keywords": "CCMI-1, WCRP, climate, chemistry, CNRM-CERFACS, MOCAGE, refC2, atmos, hr, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25305,
            "uuid": "d649e2e06351464f8c7c5d8d62e7af41",
            "title": "The CNRM-CERFACS team running: CCMI-1 experiment senC1SDfEmis using the MOCAGE model.",
            "abstract": "The CNRM-CERFACS team running the CCMI-1 senC1SDfEmis experiment using the MOCAGE model.The CNRM-CERFACS team consisted of the following agencies: Centre National de Recherches Meteorologiques (CNRM) and Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS). ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, CNRM-CERFACS, MOCAGE, senC1SDfEmis, atmos, hr, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25306,
            "uuid": "8e8be862647943a6947fb9bbacaa8e04",
            "title": "The CNRM-CERFACS team running: CCMI-1 experiment refC1SD using the MOCAGE model.",
            "abstract": "The CNRM-CERFACS team running the CCMI-1 refC1SD experiment using the MOCAGE model.The CNRM-CERFACS team consisted of the following agencies: Centre National de Recherches Meteorologiques (CNRM) and Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS). ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, CNRM-CERFACS, MOCAGE, refC1SD, atmos, hr, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25310,
            "uuid": "e39ed907f7b34176b3fd258fa458b85f",
            "title": "The CNRM-CERFACS team running: CCMI-1 experiment refC1SD using the CNRM-CM5-3 model.",
            "abstract": "The CNRM-CERFACS team running the CCMI-1 refC1SD experiment using the CNRM-CM5-3 model.The CNRM-CERFACS team consisted of the following agencies: Centre National de Recherches Meteorologiques (CNRM) and Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS). ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, CNRM-CERFACS, CNRM-CM5-3, refC1SD, atmos, day, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25311,
            "uuid": "43d1841f23b445cf91019a76dff3ac0b",
            "title": "The CNRM-CERFACS team running: CCMI-1 experiment refC2 using the CNRM-CM5-3 model.",
            "abstract": "The CNRM-CERFACS team running the CCMI-1 refC2 experiment using the CNRM-CM5-3 model.The CNRM-CERFACS team consisted of the following agencies: Centre National de Recherches Meteorologiques (CNRM) and Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS). ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, CNRM-CERFACS, CNRM-CM5-3, refC2, atmos, day, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25312,
            "uuid": "b8f6f4fdc851440c8eefe7a377354bd1",
            "title": "The CNRM-CERFACS team running: CCMI-1 experiment refC1 using the CNRM-CM5-3 model.",
            "abstract": "The CNRM-CERFACS team running the CCMI-1 refC1 experiment using the CNRM-CM5-3 model.The CNRM-CERFACS team consisted of the following agencies: Centre National de Recherches Meteorologiques (CNRM) and Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS). ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, CNRM-CERFACS, CNRM-CM5-3, refC1, atmos, day, mon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25322,
            "uuid": "c59dd4a6adf6482e9bf43a6062af9f53",
            "title": "Goddard Space Flight Center (GSFC) running: CCMI-1 experiment refC1 using the GEOSCCM model.",
            "abstract": "Goddard Space Flight Center (GSFC) running the CCMI-1 refC1 experiment using the GEOSCCM model. ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, GSFC, GEOSCCM, refC1, atmos, day, fx, hr, mon, yr",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25326,
            "uuid": "b46e282ff71540019fe5fd3a41235391",
            "title": "Goddard Space Flight Center (GSFC) running: CCMI-1 experiment refC2 using the GEOSCCM model.",
            "abstract": "Goddard Space Flight Center (GSFC) running the CCMI-1 refC2 experiment using the GEOSCCM model. ",
            "keywords": "CCMI-1, WCRP, climate, chemistry, GSFC, GEOSCCM, refC1, atmos, day, fx, hr, mon, yr",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25331,
            "uuid": "e49c3e42244849d382e6c6ff0a0db2d8",
            "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Gravimetric Mass Balance Basin products",
            "abstract": "GRACE monthly solutions provided by TU Graz (ITSG-Grace2016)\r\nGRACE-derived time series of basin-averaged Antarctic ice mass changes with respect to the mass as of 2009-01-01 according to a linear, periodic (periods: 1 year, 1/2 year, 161 days) and quadratic model fitted to the monthly solutions in the period 2003-02 - 2013-12\r\ngia_model: IJ05_R2 (http://doi.org/10.1002/jgrb.50208)\r\n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25536,
            "uuid": "f19ecd7640a347b1b96418584a217164",
            "title": "MODIS Aqua  and Terra Satellite:  C6.1 Level 1B Computation",
            "abstract": "This ATBD documentation attached to the catalogues  describes how MODIS operates in space and provides the equations implemented by  the L1B software to generate the MODIS MOD02 (Terra) and MYD02 (Aqua) data products. It is a summary document that presents the formulae and error budgets used to transform MODIS DN  to  radiance  and  reflectance.  It  describes  the  current (Collection  6),  post -launch  MODIS  \r\ncalibration  process  and  supersedes  previous  ATBDs  [Barker  et  al.  (Version 1),  1994]  [MCST  \r\n(Version 2), May 1997] [MCST (Version 3), December 14, 2005]  Analysis of instrumental on-orbit  performance  by  MCST  and  investigation  of  L1B  products  by  the  Science  Team  have  resulted  in  several  L1B  software  updates  and  improvements.  This  ATBD  corresponds  to  the   Version  6.0  Terra  and  Aqua  software  releases. Prior  to  2006,  the  MCST  and  the  Science  Data  Support  Team  (SDST)  provided  software  deliveries  to  the  Goddard  Distributed  Archive  and   Analysis Center (GDAAC). Subsequently, the MODIS Adaptive Processing System (MODAPS)  assumed  the  data  production  role  formerly  under\r\ntaken  by  the  GDAAC.  Product  files  are  currently  distributed  using  the Level  1  and  Atmosphere  and  Archive  Distribution  System  \r\n\r\n\r\nThe MODIS Adaptive Processing System (MODAPS) is currently generating an improved Collection 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) & Level-3 (L3) Atmosphere Team products.  This decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data.  These L1B issues had a negative impact in varying degrees in downstream MODIS Atmosphere Level-2 (L2) and Level-3 (L3) products, which are more fully described below.  \r\n\r\nThe MODIS Atmosphere Team decided to use this reprocessing opportunity to implement some science improvements in their L2 and L3 products.  The documents below describe Collection 6.1 (061) changes to L1B as well as all impacted (or improved) L2 and L3 MODIS Atmosphere data.  Collection 6.1 reprocessing began in late summer 2017; began to be publically released beginning on 15 October 2017; and is expected to be completed by early summer 2018.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25825,
            "uuid": "d3bf8a04e3ec4f6eb0310181a927c941",
            "title": "The Organization of Tropical Rainfall: Observed convective aggregation data across the Tropics",
            "abstract": "Individual cloud layers identified over nearly 5 years of data (July 2006–April 2011) from two A-Train satellites, CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were collected and  Simple Convective Aggregation Index was applied.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25830,
            "uuid": "d8a5ea8766764d5e86b347e5772e73b3",
            "title": "Angström Fire Index Calculation Methodology used within the HELIX project",
            "abstract": "The The High-End cLimate Impacts and eXtremes (HELIX) project calculated fire indexes values at 1.5 and 2 degree resolution using the Angström Index, I, as given by:\r\n\r\nI = (R/20) + (27-T/10)\r\n\r\nWhere:\r\n\r\nR = Relative humidity (%)\r\n\r\nT = Air temperature (°C)\r\n",
            "keywords": "",
            "inputDescription": 4,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25831,
            "uuid": "1d9929b28e79491585373e69337cee65",
            "title": "McArthur Forest Fire Danger Index (FFDI) Calculation Methodology used within the HELIX project",
            "abstract": "The The High-End cLimate Impacts and eXtremes (HELIX) project calculated fire indexes values at 1.5 and 2 degree resolution the McArthur Forest Fire Danger Index (FFDI) (Noble et al, 1980) equation is as follows:\r\n\r\nFFDI = 1.25 * D * exp [ (T - H)/30.0 + 0.0234 * V]\r\n\r\n\r\n\r\nWhere:\r\n - D = drought factor, \r\n - T = Temperature (ºC), \r\n - H = humidity (%), and \r\n - V = wind speed (km hr-1). \r\n\r\nThe drought factor (D) is calculated as follows:\r\n\r\nD = (0.191 * ( I + 104) * (N + 1)^ 1.5) / (3.52 * (N+1)^1.5 + P -1)\r\n\r\n\r\n\r\nWhere :\r\n - P = precipitation (mm day-1),\r\n - N = number of days since last rain, and \r\n - I is based on Keetch-Byram drought index. \r\n\r\nThis represents the moisture in the upper soils layers that denotes flammability of organic matter (Keetch and Byram, 1968). The HELIX Project used a varying soil moisture to calculate the deficit compared to the field capacity at a depth of 1m.\r\n\r\n\r\nReferences:\r\n\r\nKeetch, John J.; Byram, George M. (1968). A Drought Index for Forest Fire Control. Res. Pap. SE-38. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. 35 p. https://www.srs.fs.usda.gov/pubs/rp/rp_se038.pdf\r\n\r\n\r\nNoble, I. (1980). McArthur's fire-danger meters expressed as equations. Australian Journal of Ecology, 5, 201-203. Re-published in July 2006 DOI: 10.1111/j.1442-9993.1980.tb01243.x",
            "keywords": "",
            "inputDescription": 4,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25856,
            "uuid": "9b400f465efa4f978e84dad046f0f7d9",
            "title": "MODIS Fire_CCI Burned Area Computation",
            "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned areas developed from satellite observations.\r\n\r\nThe Burned Area (BA) algorithm used for producing the final Fire_CCI BA product is a hybrid approach, combining information on active fires and temporal changes in relectance.  The algorithm is described in the Fire_CCI Algorithm Theoretical Basis Document (Lizundia et al. 2018).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25883,
            "uuid": "f13f1b5aee34484dbf969bc48c38769b",
            "title": "Merged SST and LST reconstructed anomaly averaged annually and between 90°S and 90°N",
            "abstract": "Global temperature anomalies calculated from a gridded dataset based on historical observations of sea surface temperature and land surface temperature as described in Smith and Reynolds (2005)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25887,
            "uuid": "6348d517a24549369766db3f43058d0b",
            "title": "Global Monthly Average of Effective sulphur dioxide (SO2) column amounts from the Infrared Atmospheric Sounding Interferometer (IASI)",
            "abstract": "The Walker et al. (2011, 2012) linear retrieval developed for the Infrared Atmospheric Sounding Interferometer (IASI) on the Metop satellites has been used to process the IASI Metop-A data to produce an effect SO2 column amount. The linear retrieval output for each orbit has then been averaged for each month to obtain an average effective SO2 column amount in Dobson Units (DU). In this application, the retrieval uses the ν3 absorption band (centred at 7.3 μm) and SO2 was assumed to be evenly distributed between the surface and 20 km.\r\n\r\n Literature: Taylor et al. (2018) Exploring the utility of IASI for monitoring volcanic SO2 emissions, in review at JGR: Atmospheres. Walker et al. (2011) An effective method for the detection of trace species demonstrated using the MetOp Infrared Atmospheric Sounding Interferometer, Atmospheric Measurement Techniques, 4: 1567-1580, doi:10.5194/amt-4-1567-2011. Walker et al. (2012) Improved detection of sulphur dioxide in volcanic plumes using satellite-based hyperspectral infrared ",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 25899,
            "uuid": "a50d9712b0e84ff08b0ed4169db239ed",
            "title": "Hadley Centre Global Environment Model version 3 (HadGEM3-AO)",
            "abstract": "We use the atmosphere-ocean coupled configuration of the Hadley Centre Global Environment\nModel version 3 (HadGEM3-AO) from the United Kingdom Met Office (Hewitt et al., 2011). Atmospheric chemistry is represented by the United Kingdom Chemistry and Aerosols\n(UKCA) model in an updated version of the detailed stratospheric chemistry configuration\n(Morgenstern et al., 2009; Nowack et al., 2015, 2016, 2017) which is coupled to the MetUM.\n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26030,
            "uuid": "f2bc3b1abf9a4adf8ed57b60fd833ba7",
            "title": "Hadley Centre Global Environmental Model version 1a (HadGEM1a) deployed on HPCx",
            "abstract": "This computation involved: Hadley Centre Global Environmental Model version 1a (HadGEM1a) deployed on HPCx.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/9539/?format=api"
            ]
        },
        {
            "ob_id": 26035,
            "uuid": "2db4843135244d7f886b2b0056c03e42",
            "title": "GlobTemperature Level-2 MODIS Land Surface Temperature (LST) algorithm",
            "abstract": "The GlobTemperature Level-2 MODIS Land Surface Temperature (LST) algorithm derives LST data from L1B calibrated radiances from the MODIS instruments on the Aqua and Terra Satellites.   It uses a generalized split-window (SW) approach to estimate Land Surface Temperature as a linear function of clear-sky TOA (top of atmosphere) brightness temperatures from MODIS bands 31 and 32 centred on 11 and 12 microns respectively.\r\n\r\nThe GlobTemperature MODIS product also provides a full breakdown of the pixel level uncertainty budget.  The uncertainty analysis takes into account the expected performance of the retrieval algorithm under varying surface and atmospheric conditions, with these uncertainties categorised into a 3-component model: random, locally correlated and systematic.   The locally correlated component is further split into surface and atmospheric conditions.",
            "keywords": "Land Surface Temperature, LST, University of Leicester",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26038,
            "uuid": "7a4e8ea849d147b3a63c6bc9c574b1f2",
            "title": "CRUTEM3 Global mean monthly temperature time series from the historical land-only surface temperature",
            "abstract": "The global temperature is calculated as the mean of the Northern and Southern Hemisphere time series (to stop the better sampled Northern Hemisphere from dominating the average).  Data represent temperature anomalies with respect to the 1961-1990 mean.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26042,
            "uuid": "4aec087c6d714484b2624b85d67b7d0d",
            "title": "Ellipsoid Model",
            "abstract": "Ellipsoid Model is a mathematical figure approximating the shape of the Earth, used as a reference frame for computations in geodesy, astronomy and the geosciences. Various different ellipsoids have been used as approximations.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26043,
            "uuid": "1d4eb12ed3d540bbb741e52439bed363",
            "title": "DEEP-C: Derived surface net downward energy and absorbed radiation",
            "abstract": "Using satellite observations, ERA-Interim reanalysis, and atmospheric simulations",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26046,
            "uuid": "c310df6bc12b4ff8a541c1577c8bc298",
            "title": "Calculation of the envelope field of Northern hemispheric upper tropospheric (300 hPa) quasi-stationary waves ( June 1979 to August 2015)",
            "abstract": "The data are calculated from the meridional wind in ERA-Interim. A lowpass filter of 15-days is applied and the daily climatology (also lowpass filtered) subtracted. The method of Zimin et al. 2003 with a latitude dependent wavenumber range is then applied. The description of this method and the discussion about the climatology of quasi-stationary waves (this dataset) and their connection to European temperature anomalies and extreme events can be found in the attached methodology statement.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26097,
            "uuid": "a688e159a7264bc4960577edcce8871e",
            "title": "Re-analysis (ERA-20C, ERA-Interim, NCEP-CFS) European winter extra-tropical cyclone tracks 1900/1979-2010",
            "abstract": "The data was downloaded from either the ECMWF MARS server (ERA-20C, ERA-Interim) or the NOAA server (NCEP-CFS). The tracking was then performed using the TRACK software (Hodges 1994, 1995, 1999), with the storm selection and filtering method, and the variables associated with the tracks discussed in the paper currently in preparation.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26113,
            "uuid": "a3ff0eb099e34980a0d694cd57100a7b",
            "title": "UKCP18 Probabilistic Climate Projections for the UK on a 25km grid",
            "abstract": "The probabilistic projections combine information from several collections of earth system climate models, including the HadCM3 family of Met Office Hadley Centre climate models, and earth system climate models from other climate centres contributing to CMIP5 (Coupled Model Intercomparison Project Phase 5) and the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013).\n\nThe leading sources of modelling uncertainty in the atmosphere, land surface, ocean, sulphur cycle, and terrestrial carbon cycle components of the climate system are sampled, and simulated responses are weighted by the likelihood for predicting a large multivariate set of climate observables. Observationally constrained uncertainty estimates for ranges of future climate are provided in the form of probability distribution functions or large sets of probabilistic realizations, for the RCP2.6, RCP4.5, RCP6.0, RCP8.5 and SRES-A1B emission-driven scenarios.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26118,
            "uuid": "09449d597ed64c119a1e150e4fd1e9fe",
            "title": "UKCP18 Global Projections",
            "abstract": "The data is provided at 60km resolution, the native resolution of the 15 Met Office Hadley Centre projections. \n\nThe CMIP5 projections have been regridded to this resolution. The Met Office Hadley Centre projections are all variants of the global coupled models HadGEM3-GC3.05, differing only in the choice of values assigned to 52 model parameters that control the strength of processes that affect the climate system and how it responds to anthropogenic emissions. The set of parameters to perturb was chosen by expert elicitation. A subset of these HadGEM3-3.05 projections were used to drive the UKCP18 Regional Projections. \n\nThe model is configured to run using a N216 Gaussian grid which has a 60km resolution over the UK.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26121,
            "uuid": "8d419dad0cbe41febb1f4d5ec137d82a",
            "title": "UKCP18 regional time-mean sea level projections for the 21st Century",
            "abstract": "The UKCP18 21st Century regional time-mean sea level projections are based on CMIP5 climate model simulations of global ocean thermal expansion and global average surface temperature (GST) change. Global contributions from ice sheet surface mass balance and glaciers are related to GST following methods described in IPCC AR5 WG1 Chapter 13 (Church et al, 2013). Global sea level contributions for dynamic ice discharge for Greenland and Antarctica are based on IPCC AR5 and Levermann et al (2014), respectively. Estimates of land water storage changes follow those reported in IPCC AR5. These global components are regionalised for the UK through use of: (i) sea level fingerprints for mass components provided by Aimee Slangen and Giorgio Spada; (ii) regression relationships around the UK and across CMIP5 models for the oceanographic component; (iii) an ensemble of estimates of the relative sea level change from glacial isostatic adjustment from the NERC BRITICE_CHRONO project. Following the approach of Church et al (2013), a 100,000 sample Monte Carlo is used to provide uncertainties for regional sea level change based on the 5th and 95th percentiles of the Monte Carlo sample. Please refer to UKCP18 documentation for detailed methods.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26123,
            "uuid": "8c04cc69ec674540bc44428e80b01ae0",
            "title": "UKCP18 exploratory extended regional time-mean sea level projections",
            "abstract": "The UKCP18 exploratory regional time-mean sea level projections are based on a two-layer energy balance model (e.g. Gregory et al, 2015) fit to individual CMIP5 climate models. This model framework is used to provide extended projections of global thermal expansion and global average surface temperature (GST) change to 2300. Global contributions from ice sheet surface mass balance and glaciers are related to GST following  methods described in IPCC AR5 WG1 Chapter 13 (Church et al, 2013). Global sea level contributions for dynamic ice discharge for Greenland and Antarctica are based on extrapolation of IPCC AR5 projections to 2100 and the modelling work of Levermann et al (2014), respectively. Estimates of land water storage changes follow those reported in IPCC AR5 for the 21st Century extrapolated to 2300. These global components are regionalised for the UK through use of: (i) sea level fingerprints for mass components provided by Aimee Slangen and Giorgio Spada; (ii) regression relationships around  the UK and across CMIP5 models for the oceanographic component; (iii) an ensemble of estimates of the relative sea level change from glacial isostatic adjustment from the NERC BRITICE_CHRONO project. Please refer to UKCP18 documentation for detailed methods.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26134,
            "uuid": "deb97019774242e09e0627aee7c5edc6",
            "title": "INVICAT",
            "abstract": "INVICAT inverse model, developed at the University of Leeds, Leeds, UK.  INVICAT is based on the TOMCAT chemical transport model, which was developed by M. P. Chipperfield, also now at the University \r\nof Leeds.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26138,
            "uuid": "e8fe9f46c3e24a85afd29dde48856fd1",
            "title": "MetUM version 7.1",
            "abstract": "The parent simulation was produced with the limited area version of MetUM version 7.1. This simulation was processed to produce IFS CY40R1 SCM input fields using the 'cg-cascade' software available at https://github.com/aopp-pred/cg-cascade. ",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26141,
            "uuid": "6e6bf8ba72b9495398b444e10d68b6e8",
            "title": "UKCP18 Probabilistic Climate Projections for the UK Administrative Regions",
            "abstract": "The probabilistic projections combine information from several collections of earth system climate models, including the HadCM3 family of Met Office Hadley Centre climate models, and earth system climate models from other climate centres contributing to CMIP5 (Coupled Model Intercomparison Project Phase 5) and the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013).\n\nThe leading sources of modelling uncertainty in the atmosphere, land surface, ocean, sulphur cycle, and terrestrial carbon cycle components of the climate system are sampled, and simulated responses are weighted by the likelihood for predicting a large multivariate set of climate observables. Observationally constrained uncertainty estimates for ranges of future climate are provided in the form of probability distribution functions or large sets of probabilistic realizations, for the RCP2.6, RCP4.5, RCP6.0, RCP8.5 and SRES-A1B emission-driven scenarios. \n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26142,
            "uuid": "51660b405e02488f99eb782f42066d56",
            "title": "UKCP18 Probabilistic Climate Projections for UK River Basins",
            "abstract": "The probabilistic projections combine information from several collections of earth system climate models, including the HadCM3 family of Met Office Hadley Centre climate models, and earth system climate models from other climate centres contributing to CMIP5 (Coupled Model Intercomparison Project Phase 5) and the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013).\n\nThe leading sources of modelling uncertainty in the atmosphere, land surface, ocean, sulphur cycle, and terrestrial carbon cycle components of the climate system are sampled, and simulated responses are weighted by the likelihood for predicting a large multivariate set of climate observables. Observationally constrained uncertainty estimates for ranges of future climate are provided in the form of probability distribution functions or large sets of probabilistic realizations, for the RCP2.6, RCP4.5, RCP6.0, RCP8.5 and SRES-A1B emission-driven scenarios. \n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26143,
            "uuid": "6dd83819d5954787815b4e2c849b07ad",
            "title": "UKCP18 Probabilistic Climate Projections for UK Countries",
            "abstract": "The probabilistic projections combine information from several collections of earth system climate models, including the HadCM3 family of Met Office Hadley Centre climate models, and earth system climate models from other climate centres contributing to CMIP5 (Coupled Model Intercomparison Project Phase 5) and the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013).\n\nThe leading sources of modelling uncertainty in the atmosphere, land surface, ocean, sulphur cycle, and terrestrial carbon cycle components of the climate system are sampled, and simulated responses are weighted by the likelihood for predicting a large multivariate set of climate observables. Observationally constrained uncertainty estimates for ranges of future climate are provided in the form of probability distribution functions or large sets of probabilistic realizations, for the RCP2.6, RCP4.5, RCP6.0, RCP8.5 and SRES-A1B emission-driven scenarios. \n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26145,
            "uuid": "c53f486d8034465ca923104d81933db3",
            "title": "UKCP18 Historical Simulations of Gridded Sea Surface Elevation in UK Waters",
            "abstract": "The historical sea surface elevation simulations for UK waters are driven with data from five CMIP5 models: \nEC-EARTH, HadGEM2-ES, MPI-ESM-LR, IPSL-CM5A-MR, CNRM-CM5,\nThe driving models were themselves forced with the historical CMIP5 forcings.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26147,
            "uuid": "9032579f10874f809874165cca38bde5",
            "title": "UKCP18 Future Simulations of Gridded Sea Surface Elevation in UK Waters",
            "abstract": "The future sea surface elevation simulations for UK waters are driven with data from five CMIP5 models: \nEC-EARTH, HadGEM2-ES, MPI-ESM-LR, IPSL-CM5A-MR, CNRM-CM5,\nThe driving models were themselves forced with the high forcing CMIP5 scenario RCP8.5.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26149,
            "uuid": "4e1ad382198b4c4d84c512385d7a4e81",
            "title": "UKCP18 Short Event Case Studies of Historical and Future Sea Surface Elevation Around the UK",
            "abstract": "The POLCS3 storm surge model was used to produce a simulation of three historical storm surge events, with the mean sea level artificially increased by zero, 0.5, 1, 2,or 3 metres. The data show the sea surface elevation (tide-only and tide-plus-surge) relative to the imposed mean  sea level.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26151,
            "uuid": "31bd70cdd7fa4083ab41237c7ec87460",
            "title": "UKCP18 Simulated Impact of Mean Sea Level Change on Tidal Characteristics Around the UK",
            "abstract": "The POLCS3 storm surge model was used to produce a simulation of the tides around the UK with mean sea level artificially increased by zero, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 3, 4.5, 5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5 or 10 metres. The data show the sea surface elevation relative to the imposed mean sea level. No atmospheric forcing is included: the simulation is tide only (no storm surge).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26153,
            "uuid": "c21af5504e98474e9e4ed8f481013f98",
            "title": "UKCP18 Time-series Analyses of Sea Surface Elevation Around the UK",
            "abstract": "The POLCS3 storm surge model was driven by climate model simulations of the 21st century under RCP8.5. Five different climate models were used. Each has first been downscaled by the  SMHI regional atmospheric model RCA4. A statistical methid was used to identify trends in the most extreme storm surges. The data show these trends for various sites around the UK coastline.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26194,
            "uuid": "6b60cbcaafbf4d3ea66b54731ab58c33",
            "title": "UKCP18 Climate Realisations for UK Administrative Regions from Global Projections",
            "abstract": "Climate model simulations made using the HadGEM3 model. The uncertainties in the projections of future climate are represented by the spread of the perturbed multi-model ensemble.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26197,
            "uuid": "7b264a303df34df3bc2b3fe0bdee0d6a",
            "title": "UKCP18 Climate Realisations for UK Country Regions from Global Runs",
            "abstract": "Climate model simulations made using the HadGEM3 model. The uncertainties in the projections of future climate are represented by the spread of the perturbed multi-model ensemble.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26200,
            "uuid": "7f684d84d7584be4a42cc01d577c9541",
            "title": "UKCP18 Climate Realisations for UK River Basin Regions from Global Runs",
            "abstract": "Climate model simulations made using the HadGEM3 model. The uncertainties in the projections of future climate are represented by the spread of the perturbed multi-model ensemble.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26203,
            "uuid": "f220246ca1fa4e1eae3636bc883b6cb7",
            "title": "UKCP18 Climate Realisations for UK on OSGB grid from Global Runs",
            "abstract": "Climate model simulations made using the HadGEM3 model. The uncertainties in the projections of future climate are represented by the spread of the perturbed multi-model ensemble.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26206,
            "uuid": "21d72da014ee44db9325e8f44bbf5fb7",
            "title": "UKCP18 Climate Realisations for UK Administrative Regions from Europe Regional Climate Model Realisations",
            "abstract": "The climate model projections are all variants of the limited-area atmosphere-only version of the Met Office Hadley Centre Global Environmental model (HadGEM3). They provide downscaled projections for the UK or Europe, driven by an ensemble of 60km Hadley Centre global coupled models HadGEM3-GC3.05.\n\nThis dataset consists of 12 projections from the 12km HadREM3-GA705 model. The model spans Europe and is driven by the 60km HadGEM3-GC3.05 global coupled model (GCM) perturbed-physics ensemble, with perturbations applied to the 12km RCM consistent with the driving GCM. All models are configurations of the Unified Model.\n\nThere are discontinuities in the data on 1st Dec 2020 and 1st Dec 2060, due to the simulations being conducted as three separate time slices.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26209,
            "uuid": "b035d8ddc37c4c1aa9f77bf8c63de356",
            "title": "UKCP18 Climate Realisations for UK Country Regions from Europe Regional Climate Model Realisations",
            "abstract": "The climate model projections are all variants of the limited-area atmosphere-only version of the Met Office Hadley Centre Global Environmental model (HadGEM3). They provide downscaled projections for the UK or Europe, driven by an ensemble of 60km Hadley Centre global coupled models HadGEM3-GC3.05.\n\nThis dataset consists of 12 projections from the 12km HadREM3-GA705 model. The model spans Europe and is driven by the 60km HadGEM3-GC3.05 global coupled model (GCM) perturbed-physics ensemble, with perturbations applied to the 12km RCM consistent with the driving GCM. All models are configurations of the Unified Model.\n\nThere are discontinuities in the data on 1st Dec 2020 and 1st Dec 2060, due to the simulations being conducted as three separate time slices.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26212,
            "uuid": "f56a9d41e69147738b7d1e97ee934801",
            "title": "UKCP18 Climate Realisations for UK River Basin Regions from Europe Regional Climate Model Realisations",
            "abstract": "The climate model projections are all variants of the limited-area atmosphere-only version of the Met Office Hadley Centre Global Environmental model (HadGEM3). They provide downscaled projections for the UK or Europe, driven by an ensemble of 60km Hadley Centre global coupled models HadGEM3-GC3.05.\n\nThis dataset consists of 12 projections from the 12km HadREM3-GA705 model. The model spans Europe and is driven by the 60km HadGEM3-GC3.05 global coupled model (GCM) perturbed-physics ensemble, with perturbations applied to the 12km RCM consistent with the driving GCM. All models are configurations of the Unified Model.\n\nThere are discontinuities in the data on 1st Dec 2020 and 1st Dec 2060, due to the simulations being conducted as three separate time slices.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26215,
            "uuid": "607de5aa7bc74246a25ffe4dae5ec619",
            "title": "UKCP18 Climate Realisations for UK on OSGB grid from Europe Regional Climate Model Realisations",
            "abstract": "The climate model projections are all variants of the limited-area atmosphere-only version of the Met Office Hadley Centre Global Environmental model (HadGEM3). They provide downscaled projections for the UK or Europe, driven by an ensemble of 60km Hadley Centre global coupled models HadGEM3-GC3.05.\n\nThis dataset consists of 12 projections from the 12km HadREM3-GA705 model. The model spans Europe and is driven by the 60km HadGEM3-GC3.05 global coupled model (GCM) perturbed-physics ensemble, with perturbations applied to the 12km RCM consistent with the driving GCM. All models are configurations of the Unified Model.\n\nThe regional climate model data has been interpolated using a conservative regridding scheme to the Ordnance Survey's British National grid.\n\nThere are discontinuities in the data on 1st Dec 2020 and 1st Dec 2060, due to the simulations being conducted as three separate time slices.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26220,
            "uuid": "eea5f87c81a84857a2950268713c5881",
            "title": "Lisflood-FP v5.9, subgrid compset",
            "abstract": "LISFLOOD-FP is a two-dimensional hydrodynamic model specifically designed to simulate floodplain inundation in a computationally efficient manner over complex topography. It is capable of simulating grids up to 106 cells for dynamic flood events and can take advantage of new sources of terrain information from remote sensing techniques such as airborne laser altimetry and satellite interferometric radar.\r\n\r\nThe model predicts water depths in each grid cell at each time step, and hence can simulate the dynamic propagation of flood waves over fluvial, coastal and estuarine floodplains. It is a non-commercial, research code developed as part of an effort to improve our fundamental understanding of flood hydraulics, flood inundation prediction and flood risk assessment.\r\n\r\nIn this project the COSMO-Skymed Synthetic Aperture Radar (CSK-SAR) were acquired processed and transformed into Water Level Observations (WLOs) by crossing with LiDAR Digital Terrain Model. Environment Agency (EA) rain gauges were used to estimate precipitation and MORECS as potential evapotranspiration to generate the forcings into a catchment-scale rainfall-runoff hydrologic model (topHSPF) to generate simulated runoff forecast, to be used as forcing for the coupled Lisflood-FP v5.9 inundation model. EA water level gauges were used for validation. CSK-SAR based WLO were assimilated into ensemble simulations with Lisflood-FP v5.9 generated with perturbed physics (friction parameters, bathymetric errors) and runoff inputs from the above mentioned hydrologic model. \r\n",
            "keywords": "DEMON, Synthetic Aperture Radar, Flood forecast, Assimilation, Ensemble Kalman Filte",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26229,
            "uuid": "d0dba8684d3f466db3a7144ca6852e2c",
            "title": "ACITES: Python code used to produce the NetCDF files",
            "abstract": "Python code used to produce the NetCDF files in the htap_data, land_surface_data and measurement_data direc",
            "keywords": "ACITES, python",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26250,
            "uuid": "13df5ddb2ca5479eb3697507763f6d3b",
            "title": "METOP IMS RAL computation process",
            "abstract": "The RAL Infrared Microwave Sounder (IMS) retrieval scheme jointly retrieves height-resolved temperature, water vapour, and ozone, as well as surface spectral emissivity, effective cloud fraction, effective cloud ice fraction, and effective cloud top height, from three instruments on board the MetOp platform: the Infrared Atmospheric Sounding Interferometer (IASI), the Advanced Microwave Sounding Unit (AMSU), and the Microwave Humidity Sounder (MHS). The IMS scheme is a modified version of the EUMETSAT 1DVar IASI L2 retrieval, resulting from the study “Optimal Estimation Method retrievals with IASI, AMSU and MHS measurements” (R. Siddans et al., 2015). The IMS scheme differs from the EUMETSAT L2 scheme through the addition of microwave channels from AMSU and MHS to the measurement vector, and the addition of surface spectral emissivity and cloud to the state vector. There are also differences in the prior assumptions made by the retrieval and RTTOV coefficients used.\r\n\r\nThe IMS scheme measurement vector comprises the same subset of 139 IASI channels as the EUMETSAT L2 scheme (700-1900 cm-1), all (functioning) AMSU channels, and all MHS channels. The forward model used is RTTOV (Matricari, 2009) with v9 coefficients to allow the modelling of spatial and seasonal variation and trends in greenhouse gases.\r\n\r\nThe IMS retrieval state vector is expressed in terms of PC weights of a climatological global zonal mean covariance matrix, with 28 PC weights for the atmospheric temperature profile, 18 for the water vapour profile, 10 for the ozone profile and 20 for surface spectral emissivity. In the output files, profiles are reported on the 101 level RTTOV fixed pressure grid. \r\n\r\nA climatological zonal mean prior, scaled to match NWP, is used for temperature, water vapour and ozone, and is interpolated linearly to the latitude of each measurement. \r\n\r\nFor the retrieval of surface spectral emissivity, PCs were computed from the global covariance of the RTTOV implementation of the University of Wisconsin global infrared land surface emissivity database (Borbas and Ruston, 2010) and RTTOV microwave spectral emissivity models. Prior covariances for surface spectral emissivity are defined for the first 6 patterns using the combined RTTOV infrared and microwave atlases. Spectral correlations between the infrared and microwave emissivities are included (derived from the RTTOV representation of the spatial covariance of infrared and microwave emissivity).\r\n\r\nInitial cloud screening of IASI scenes is performed by implementing a threshold on the brightness temperature difference between observations and cloud-free simulations (based on ECMWF) at 11 microns. Scenes containing partial, thin or very-low cloud are processed by the retrieval and characterised by the retrieved cloud variables.\r\n\r\nsee documentations for additional details\r\n",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26446,
            "uuid": "117c8b62457a4b2d999a55884f5df22e",
            "title": "Level 2 Carbon Monoxide (CO) total column processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
            "abstract": "This computation involves the Level 2 processing algorithm applied to raw TROPOspheric Monitoring Instrument (TROPOMI) data. The retrieval algorithm requires several input fields:\r\n• The measured Earth radiance and solar irradiance spectra including noise estimate, solar and viewing\r\ngeometry, and information of geo-location.\r\n• ECMWF temperature, water vapor, and pressure profiles, and geo-potential height.\r\n• An estimate of the CH4 field using a chemistry transport model, e.g. Transport Model 5 (TM5, [RD33]).\r\n• An estimate of the CO column from a chemistry transport model (e.g. TM5).\r\nThe retrieval is performed in two steps: first, as part of the SWIR preprocessing module, the vertically integrated amount of methane is retrieved from a dedicated fit window of the SWIR band between 2315 and 2324 nm using a non-scattering radiative transfer model. The extent of lightpath shortening and enhancement due to atmospheric scattering by clouds and aerosols can be indicated by comparing the retrieved CH4 column with a priori knowledge. If the difference ∆CH4 exceeds a certain threshold, observations are strongly contaminated by clouds and are rejected. In a second step, the SICOR full physics retrieval approach is used to infer CO columns from the adjacent spectral window, 2324-2338 nm. Here, the methane absorption features are used to infer information on atmospheric scattering by clouds and aerosols, which passed the cloud filter, together with the atmospheric CO and H2O abundances, surface albedo, and spectral calibration of the reflectance spectrum. The scattering layer has a triangular height distribution of fixed geometrical thickness, and its optical depth and height are parameters to be retrieved. This step of the retrieval relies on accurate a priori knowledge of CH4 which will be provided within an accuracy of ±3 % by a dedicated methane forecast using the TM5 atmospheric transport model. The atmospheric scattering is described by a two-stream radiative transfer model. Finally, the retrieval product consists of a CO column estimate including a column averaging kernel and a random error estimate. For more information on the processing algorithm please look at the ATBD document on the TROPOMI CO webpage.",
            "keywords": "Sentinel, ESA, VIIRS",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26457,
            "uuid": "fe199a094ee04ba2a6c5ac6ac11b879b",
            "title": "Level 2 Ozone (O3) total column processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
            "abstract": "This computation involves the Level 2 processing algorithm applied to raw TROPOspheric Monitoring Instrument (TROPOMI) data. The one-step OFFL algorithm (S5P_TO3_GODFIT) comprises a non-linear least-squares inversion based on the direct comparison of simulated and measured backscattered\r\nradiances. Simulated radiances are computed (again for a multiple scattering atmosphere) at all nominal wavelengths in the UV fitting window, along with corresponding analytically derived weighting functions for total ozone, albedo and effective temperature. The algorithm also contains closure fitting coefficients and a semi-empirical correction for Ring interference. Although more accurate, the S5P_TO3_GODFIT algorithm involves many more RT simulations than S5P_TO3_DOAS, and is, therefore, slower by an order of magnitude approximately. This is currently the main driver for the selection of S5P_TO3_DOAS for the\r\nNRTI product. For more information on the processing of the O3 product please refer to the ATBD document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26460,
            "uuid": "b79b6f6063ee408cb5a93ec7b34923da",
            "title": "Level 2 Nitrogen Dioxide (NO2) total column processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
            "abstract": "This computation involves the Level 2 processing algorithm applied to raw TROPOspheric Monitoring Instrument (TROPOMI) data. The TROPOMI NO2 processing system is based on the DOMINO and QA4ECV processing systems, with improvements related to specific TROPOMI aspects and new scientific insights. The basis for the processing at KNMI is a retrieval-assimilation-modeling system that uses the 3-dimensional global TM5 chemistry transport model as an essential element. The retrieval consists of a three-step procedure, performed on each measured Level-1b spectrum:\r\n1. the retrieval of a total NO2 slant column density (Ns) from the Level-1b radiance and irradiance spectra\r\nmeasured by TROPOMI using a DOAS (Differential Optical Absorption Spectroscopy) method,\r\n2. the separation of the Ns into a stratospheric and a tropospheric part on the basis of\r\ninformation coming from a data assimilation system, and\r\n3. the conversion of the tropospheric slant column density into a tropospheric vertical column density and of the stratospheric slant column density into a stratospheric vertical column density, by applying an appropriate AMF based on a look-up table of altitude-dependent AMFs and actual, daily information on the vertical distribution of NO2 from the TM5-MP model on a 1-degree by 1-degree grid. The altitude-dependent AMF depends on the satellite geometry, terrain height, cloud fraction and height and surface albedo.\r\nThese steps are described in detail in the ATBD document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26491,
            "uuid": "4192e2f0403348c4b94f28e65c56f7c7",
            "title": "Level 2 Cloud processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
            "abstract": "This computation involves the Level 2 processing algorithm applied to raw TROPOspheric Monitoring Instrument (TROPOMI) data.\r\nOptical Cloud Recognition Algorithm (OCRA) is the S5P_CLOUD_OCRA heritage. In OCRA, optical sensor measurements are divided into two components: a cloud-free background and a remainder expressing the influence of clouds. OCRA was first developed for GOME in the late 1990s, when enough data from the three sub-pixel broad-band PMDs (Polarization Measurement Devices) had accumulated to allow for the construction of the global cloud-free composite which is the key element in the algorithm. Over the course of the 16-year GOME record, the\r\nalgorithm was refined and the cloud-free composite adjusted as more data became available. OCRA has also been applied to SCIAMACHY and GOME-2. Initial cloud-free composites for these sensors were based on GOME data before dedicated measurements became available from SCIAMACHY and GOME-2. For S5P_CLOUD_OCRA, the initial cloud-free composite will be based on GOME-2 and OMI (see section 5.2). Retrieval of Cloud Information using Neural Networks (ROCINN) is the S5P_CLOUD_ROCINN heritage. ROCINN is based on the comparison of measured and simulated satellite sun-normalized radiances in and near the O2 A-band, and it uses a neural network algorithm to retrieve cloud-top height and cloud-top albedo. ROCINN uses the cloud fraction input from OCRA as one starting point. Early versions of ROCINN used a transmittance model to compute simulated radiances, but the latest versions are based on the use of the VLIDORT radiative transfer scattering model.\r\nFor GOME and GOME-2, ROCINN Version 2.0 is the current operational algorithm in the GDP [GOME Data Processor]. This version is based on the assumption that clouds are simply Lambertian reflecting surfaces so the two main retrieval products are the cloud-top height and the cloud-top albedo itself. This is the “clouds-as-reflecting-boundaries” (CRB) model; see for example [van Roozendael et al., 2006] for GOME and [Loyola et al., 2011] for GOME 2.\r\nAlthough ROCINN 2.0 is the heritage algorithm, there is an important point of departure for S5P. For TROPOMI/S5P, ROCINN Version 3.0 was initially used, which is based on a more realistic treatment of clouds as optically uniform layers of light-scattering particles (water droplets). This is the “clouds-as-layers\" (CAL) model – here, the two main retrieval products are the cloud-top height and the cloud optical thickness. Details of this algorithm prototype may be found in [Schuessler et al., 2014]. Although the CAL model will be the default for S5P, it has been requested that the CRB method should also be retained as an option. CAL is the preferred method for the relatively small TROPOMI/S5P ground pixels (5.5 x 3.5\r\nkm2). The CRB approach works best with large pixels such as those from GOME (footprint 320 x 40 km2). [Schuessler et al., 2014] has shown that for the smaller GOME-2 pixels, CAL retrieval produces more reliable cloud information than that from CRB, not only with regard to the accuracy of the cloud parameters themselves but also with regard to the effect of cloud parameter uncertainties on total ozone accuracy. In OCRA, the intensity is regarded as a linear function of the radiometric cloud cover, and in\r\nROCINN, TOA radiances for partially cloudy scenarios are computed using a linearly weighted mean of the clear-sky and fully-cloudy calculations, the weighting factor being the cloud fraction. In the context of this IPA model, the two algorithms are consistent. With the notably smaller pixel size that comes with higher spatial resolution, 3-D cloud radiative effects will become an important consideration in error budgeting for the cloud algorithms. For more information on the processing chain please see the ATBD document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26500,
            "uuid": "867132d84abe4fe6b2e4cccb16647985",
            "title": "Level 2 UV Aerosol Index processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
            "abstract": "This computation involves the Level 2 processing algorithm applied to raw TROPOspheric Monitoring Instrument (TROPOMI) data. MORE INFORMATION TO FOLLOW",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26510,
            "uuid": "bebcc2d6c59f4167a406c87e08106122",
            "title": "TRMM Multi-Satellite Precipitation Analysis",
            "abstract": "The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26513,
            "uuid": "0f0233013769441d92de1e084cb950eb",
            "title": "Integrated Multi-satellitE Retrievals for GPM (IMERG)",
            "abstract": "The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the Day-1 multi-satellite precipitation product. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2014 version of the Goddard Profiling Algorithm (GPROF2014), then gridded, intercalibrated to the GPM Combined Instrument product, and combined into half-hourly 10x10 km fields.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26519,
            "uuid": "179a8c9a1cd2402cb3d753071b0606a7",
            "title": "Level 2 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) version 3",
            "abstract": "Level-2 consists of geo-located geophysical products derived from Level-1. Level-2 Ocean (OCN) products for wind, wave and currents applications may contain the following geophysical components derived from the SAR data:\r\n- Ocean Wind field (OWI)\r\n- Ocean Swell spectra (OSW)\r\n- Surface Radial Velocity (RVL)\r\nThe availability of components depends on the acquisition mode. The OSW component cannot be generated from IW and EW mode, since individual looks with sufficient time separation are required. The obtained inter look time separation within one burst is too short due to the progressive scanning (i.e. short dwell time).\r\n\r\nThe metadata referring to OWI are derived from an internally processed GRD product. The metadata referring to RVL (and OSW, for SM and WV mode) are derived from an internally processed SLC product.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab.",
            "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26528,
            "uuid": "accf62d297264f2e94d6299be7ba7333",
            "title": "Climate Prediction Center morphing method (CMORPH)",
            "abstract": "CMORPH (CPC MORPHing technique) produces global precipitation analyses at very high spatial and temporal resolution. This technique uses precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively, and whose features are transported via spatial propagation information that is obtained entirely from geostationary satellite IR data.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26529,
            "uuid": "370e24b52236461592e91cf6628c08b6",
            "title": "Climate Hazards Group Infrared Precipitation with Stations (CHIRPS)",
            "abstract": "CHIRPS is the product of a two part process. First, IR Precipitation (IRP) pentad rainfall estimates are created from satellite data by calculating the percentage of time during the pentad that the IR observations indicate cold cloud tops (<235° K), and converting that value into millimeters of precipitation by means of previously determined local regression with TRMM 3B42 precipitation pentads. The IRP pentads are then expressed as percent of normal by dividing the values by their long-term (1981–2012) IRP means. These unitless values represent variations in time from the long-term mean (below normal, normal, or above normal rainfall). The percent of normal IRP pentad is then multiplied by the corresponding CHPClim pentad to produce an unbiased gridded estimate, with units of millimeters per pentad, called the Climate Hazards Group IR Precipitation (CHIRP). In the second part of the process, stations are blended with the CHIRP data to produce the final product, CHIRPS",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26541,
            "uuid": "1b075ab7d69b41ee981cc832d6306a44",
            "title": "Level 1B processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
            "abstract": "This computation involves the Level 1b processing algorithm applied to raw TROPOspheric Monitoring Instrument (TROPOMI) data.\r\n\r\nThe Earth radiance measurements form the bulk of the measurements. Apart from the optical properties of the instrument, there is some flexibility in the electronics that determine the Earth's radiance ground pixel size. The co-addition period determines the ground pixel size in the along-track direction. Row binning (which is possible for UVN-DEMs only) determines the ground pixel size across track. The parameter space is limited, however, as choosing a smaller ground pixel size will increase the data rate and will decrease the signal-to-noise ratio for the individual ground pixels. The data rate is limited by both internal interfaces within the instrument as well as by the platform’s on-board storage and down-link capabilities. \r\nFor the Earth's radiance measurements, the co-addition period can be set to either 1080ms or 840ms. This\r\neffectively results in a ground pixel size of approximately 7km or 5.5km along-track. The co-addition period is set in the instrument configuration, initially, the nominal operations phase was started with 1080ms. For the SWIR-DEM, which contains a CMOS detector, row binning is not supported. This means that, effectively, the binning factor is 1 for the SWIR bands (Band 7 and Band 8), resulting in a ground pixel size across-track between 7km at the center and 34km at the edges of the across-track field of view. The ground pixel size varies across-track since the spatial dispersion (degrees/pixel) is constant, resulting in a ground pixel size that becomes larger towards the edges of the across-track field of view due to the Earth’s curvature.\r\nApart from the binning factor and the co-addition period, the remaining configuration parameters for\r\nthe Earth radiance measurements, including exposure time and gains, have been optimized during in-flight\r\ncommissioning for the best signal-to-noise ratio while minimizing the saturation of the detector or electronics. This optimization was based on scenes with the highest radiance levels, typically clouded scenes. Since the highest radiance level changes as a function of latitude, a total of five different settings for different latitude zones are created. For bands 4 and 6 saturation, it has not been possible to exclude saturation completely due to instrument limitations.\r\n\r\nFor more information please see the ATBD document linked in the docs tab.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26555,
            "uuid": "b8ec9aef5b104c2f84c5192663af342e",
            "title": "NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry.",
            "abstract": "Simulations were undertaken with the NEMO-HadOCC model ocean at 1 degree with HadOCC biogeochemistry.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26560,
            "uuid": "8a1587e2f3034eab8ef78e6b8ea4c5b8",
            "title": "HadCM3B coupled climate model",
            "abstract": "The Hadley Centre Climate Model 3 Bristol (HadCM3B) is a coupled climate model consisting of a 3D dynamical atmosphere26 and ocean27 component. HadCM3B is a version of the more commonly known HadCM3 that has been developed at the University of Bristol",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26607,
            "uuid": "17e38b85b0c546799f21bf640b5c219c",
            "title": "Along Track Scanning Radiometers combined Surface Temperature (AAST) dataset (v2.1) algorithm",
            "abstract": "The AAST Combined Surface Temperature dataset (CST_L3S_v2.1) has been produced by the University of Leicester.   The algorithm combines products produced by the following projects :  Ice (and snow) Surface Temperatures (ISTs) and Land Surface Temperatures (LSTs) used to produce this dataset are sourced from the GlobTemperature Level 2 LST V2.1 product;  Sea Surface Temperature (SSTs) are from the ATSR SST L2P V3.0 product.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26619,
            "uuid": "115628bd9118415ab47a4b41aa6dc0a6",
            "title": "ESA Fire_cci Burned Area SFD algorithm",
            "abstract": "The Burned Area (BA) algorithm used for producing the Fire_CCI Burned Area product is described in the Fire_CCI Algoirthm Theoretical Basis Document (Bastarrika and Roteta, 2018), available at https://www.esa-fire-cci.org/documents",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26670,
            "uuid": "392a945cbb0a434791530a90eaebcd90",
            "title": "WRF version 3.8 model",
            "abstract": "Modified WRF version 3.8 model deployed on the British Antarctic Survey computer",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26680,
            "uuid": "04595e17816a4455af8fe6ad596cbf8f",
            "title": "Terrestrial Laser Scanner raw data computation process",
            "abstract": "The terrestrial laser scanner data was proceesing using software created by Andrew Burt UCL project colleagues detailed belowed",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26692,
            "uuid": "a7162bc8f4004dd294c9bb51c4d46a81",
            "title": "UKCP18 Projected Future Extreme Still Water Level at Selected Tide Gauge Locations",
            "abstract": "Projected mean sea level change (c/f MS4.8 or MS4.06 need to check  with  Matt)  was added to best estimates of present day return levels of extreme still water level to give projected future still water return levels",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26711,
            "uuid": "f01f1585e50b44d8b10640db0d79dabd",
            "title": "Unified Model version 6.6 (HadGEM2) run in AMIP configuration for an anthropogenic-only aerosol experiment for the SAPRISE (South Asian PRecIpitation: A SEamless assessment) project.",
            "abstract": "Unified Model version 6.6 (HadGEM2) run in AMIP (atmosphere only) configuration for an anthropogenic-only aerosol experiment for the SAPRISE (South Asian PRecIpitation: A SEamless assessment) project.\r\nThe model is forced with anthropogenic-only aerosols i.e. sulphur dioxide, black carbon and biomass burning aerosols. \r\nEach ensemble member is initialised from different phases of multi-decadal variation to account for the model/atmosphere internal variability.",
            "keywords": "SAPRISE, South Asia, Monsoon, Rainfall, Precipitation, Anthropogenic Aerosols, HadGEM2, UM 6.6, Historical, AMIP",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26725,
            "uuid": "01a9f5caedbc4a92af70b390a3a21b5e",
            "title": "Theoretical calculation of line intensties for isotopologues of CO2",
            "abstract": "Spectral line lists for CO2 have been calculated from a theoretical model using the AMES potential energy surface and an accurate ab initio dipole moment surface.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": 27382,
            "identifier_set": []
        },
        {
            "ob_id": 26727,
            "uuid": "0f5670b1168c4ba5a7b8c9da6b29c5a6",
            "title": "The ESA CCI Greenland Ice Sheets Cryosat2 Surface Elevation Change product",
            "abstract": "The algorithms used to derive the product are explained in detail in Simonsen, S. B. and Sørensen, L. S. (2017) ‘Implications of changing scattering properties on Greenland ice sheet volume change from Cryosat-2 altimetry’, Remote Sensing of Environment. Elsevier Inc., 190, pp. 207–216. doi: 10.1016/j.rse.2016.12.012.. \r\n\r\nThe approach used corresponds to Least Squares Method (LSM) as described in the paper, in which the slope within each grid cell is accounted for by subtraction of the GIMP DEM; the data are corrected for both backscatter and leading edge width; and the LSM is solved at 1 km grid resolution (2 km search radius) and averaged in the post-processing to 5 km grid resolution and with a correlation length of 10/20 km.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26736,
            "uuid": "c5f0267d809340a58cae305cd3dd8678",
            "title": "ESA CCI Greenland Ice Sheet Surface Elevation Change from SARAL-Altika",
            "abstract": "This new experimental product of surface elevation change is based on data from the AltiKa-instrument onboard the France (CNES)/Indian (ISRO) SARAL satellite (Verron et al. 2015). The AktiKa altimeter utilizes Ka-band radar signals, which have less penetration in the upper snow. However, the surface slope and roughness has an imprint in the derived signal and the new product is only available for the flatter central parts of the Greenland ice sheet.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26787,
            "uuid": "7b2b77861e3344ae83499ee9a98f8baa",
            "title": "Ice Velocity derived by the ESA Greenland Ice Sheets Climate Change Initiative project.",
            "abstract": "Ice velocities for the Greenland Ice Sheet have been derived by the ESA CCI Greenland Ice Sheet project.  Further details are available from http://esa-icesheets-greenland-cci.org/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26793,
            "uuid": "0c1e15ab90ee475f849230f842020fc0",
            "title": "UKCP18 Derived Climate Projections for the RCP 2.6 scenario on a 60km grid of the UK for 1900-2100",
            "abstract": "This dataset was produced using an emulation technique. The technique is based on applying the time shift methodology to the UKCP18 Global Climate Projections, along with some other statistical techniques, to produce time series of climate variables for the UK region only. Each realisation provides an example of climate variability in a changing climate. The dataset covers the time period of 1900-2100 and comprises of monthly time series of near surface air temperature, precipitation rate, relative humidity, near surface wind, northward near surface wind speed, eastward near surface wind speed and net surface short wave flux. Daily time series of mean surface temperature and precipitation also exists",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26796,
            "uuid": "37b19e34e2bc4a8c8fd5e0f700c76e69",
            "title": "UKCP18 Derived Climate Projections for a 2°C global warming level on a 60km grid of the UK",
            "abstract": "This dataset was produced using an emulation technique. The technique is based on applying the time shift methodology to the UKCP18 Global Climate Projections, along with some other statistical techniques, to produce time series of climate variables for the UK region only. Each realisation provides an example of climate variability in a changing climate. The dataset cover the time period of 50 years and comprises of monthly time series of near surface air temperature, precipitation rate, relative humidity, near surface wind, northward near surface wind speed, eastward near surface wind speed and net surface short wave flux. Daily time series of near surface temperature and precipitation also exist",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26799,
            "uuid": "3027e59cec544a6db2c372ee7f1317c3",
            "title": "UKCP18 Derived Climate Projections for a 4°C global warming level on a 60km grid of the UK",
            "abstract": "This dataset was produced using an emulation technique. The technique is based on applying the time shift methodology to the UKCP18 Global Climate Projections, along with some other statistical techniques, to produce time series of climate variables for the UK region only. Each realisation provides an example of climate variability in a changing climate. The dataset cover the time period of 50 years and comprises of monthly time series of near surface air temperature, precipitation rate, relative humidity, near surface wind, northward near surface wind speed, eastward near surface wind speed and net surface short wave flux. Daily time series of near surface temperature and precipitation also exist.  Since not all realisations in the UKCP18 Global Climate Projections reach a global warming of 4°C above pre-industrial levels, there are less than 28 realisations in this dataset.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26804,
            "uuid": "0e31667819b3481484fc28d8f1d613a5",
            "title": "Optical Ice velocity derived from intensity tracking of Sentinel 2 data",
            "abstract": "Optical Ice Velocity time series and seasonality products have been derived from intensity-tracking of Sentinel 2 data as part of the ESA Climate Change Initiative (CCI) Greenland Ice Sheet project.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26818,
            "uuid": "e1a0c71adc7a4bf19c239f99b24134f9",
            "title": "Ice velocity time series derived from intensity tracking of ERS-1, ERS-2 and ENVISAT data for the Greenland Ice Sheet CCI project.",
            "abstract": "Ice Velocity time series have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data for the ESA Climate Change Initiative (CCI) Greenland Ice Sheet project.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 26870,
            "uuid": "b1b352825f5548a8bf0639afe335f5ae",
            "title": "HadUK-Grid gridded climate observations methodology",
            "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables - for example, for the temperature variables the number of stations rises from about 270 in 1910s to 600 in the mid-1990s, before falling to 450 in 2006. Regression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage.\r\n\r\nThe methods used to generate the grids are described in more detail in a paper published by Hollis et al. (2019) https://doi.org/10.1002/gdj3.78 (see linked documentation on this record).\r\n\r\nTo help users combine the observational data sets with the UKCP18 climate projections, the 1km x 1km grid is averaged to grids at resolutions to match those of the climate projections. Each 5 x 5 km, 12 x 12 km, 25 x 25 km or 60 x 60 km grid box value is an average of the all the 1 × 1 km grid cell values that fall within it. A set of regional values for UK administrative regions, river basins and countries are calculated as the average of all 1 × 1 km grid cell values that fall within the defined geography.",
            "keywords": "gridded observations",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": [
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12118/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12119/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12120/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12121/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12122/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12123/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/12117/?format=api"
            ]
        }
    ]
}