ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

Release 1 of the Fundamental Climate Data Record (FCDR) of the Microwave Humidity Sounder - 1 (MWHS-1) onboard the Chinese polar orbiting satellites FY-3A and FY-3B. The FCDR provides recalibrated brightness temperatures with detailed uncertainty estimates and error correlation information. The FCDR was generated for the Copernicus Climate Change Service (C3S) and as further elaborated in the Product User Guide, the FCDR files are provided in netCDF4 format.

Release 1 of the Fundamental Climate Data Record (FCDR) of the Microwave Humidity Sounder consists of FCDRS from Advanced Microwave Humidity Sounder-B (AMSU-B) onboard National Atmospheric Administration (NOAA) satellited N15, N16, and N17. The FCDR provides recalibrated brightness temperatures with detailed uncertainty estimates and error correlation information. The FCDR was generated in the EU Horizon2020 Fidelity and uncertainty in climate data records from Earth Observations (FIDUCEO) project and as further elaborated in the Product User Guide, the FCDR files are provided in FIDUCEO EASY netCDF4 formats.

The NCEP/NCAR Reanalysis Project is an effort to reanalyze historical data using state-of-the-art models. The reanalysis is done at NCEP (formerly NMC) using T62 (209 km) global spectral model of 28 vertical levels. This is the same model used in the assimilation system, as implemented in the NCEP operational system in December 1994. The model has 5 levels in the boundary layer and about 7 levels above 100 hPa. The lowest model level is about 5 hPa from the surface, and the top level is at about 3 hPa. This vertical structure was chosen so that the boundary layer is reasonably well resolved and the stratospheric analysis at 10 hPa isnot much affected by the top boundary conditions. The details of the model dynamics and physics are described in Development Division (1988), Kanamitsu (1989), and Kanamitsu et al(1991). The model includes parameterizations of all major physical processes, i.e., convection, large scale precipitation, shallow convection, gravity wave drag, radiation with diurnal cycle and interaction with clouds, boundary layer physics, an interactive surface hydrology, and vertical and horizontal diffusion processes. See https://climatedataguide.ucar.edu/climate-data/reanalysis for further documentation. The data sets consist of multi-level, multi-code sigma-level fields.

The NCEP/NCAR Reanalysis Project is an effort to reanalyze historical data using state-of-the-art models. The reanalysis is done at NCEP (formerly NMC) using T62 (209 km) global spectral model of 28 vertical levels. This is the same model used in the assimilation system, as implemented in the NCEP operational system in December 1994. The model has 5 levels in the boundary layer and about 7 levels above 100 hPa. The lowest model level is about 5 hPa from the surface, and the top level is at about 3 hPa. This vertical structure was chosen so that the boundary layer is reasonably well resolved and the stratospheric analysis at 10 hPa isnot much affected by the top boundary conditions. The details of the model dynamics and physics are described in Development Division (1988), Kanamitsu (1989), and Kanamitsu et al(1991). The model includes parameterizations of all major physical processes, i.e., convection, large scale precipitation, shallow convection, gravity wave drag, radiation with diurnal cycle and interaction with clouds, boundary layer physics, an interactive surface hydrology, and vertical and horizontal diffusion processes. See https://climatedataguide.ucar.edu/climate-data/reanalysis for further documentation. The data sets consist of single-level, single-code pressure-level fields on gaussian grid.

Release 1 of the Fundamental Climate Data Record (FCDR) of the Advanced Technology Microwave Sounder (ATMS) onboard Soumi NPP satellite. The FCDR provides recalibrated brightness temperatures with detailed uncertainty estimates and error correlation information. The FCDR was generated for the Copernicus Climate Change Service (C3S) and as further elaborated in the Product User Guide, the FCDR files are provided in netCDF4 format.

Release 2 of the Fundamental Climate Data Record (FCDR) of the Microwave Humidity Sounder (MHS) onboard EUEMTSAT satellites MetopA and MetopB. The FCDR provides recalibrated brightness temperatures with detailed uncertainty estimates and error correlation information. The FCDR was generated for the Copernicus Climate Change Service (C3S) and as further elaborated in the Product User Guide, the FCDR files are provided in netCDF4 format.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Release 1 of the Fundamental Climate Data Record (FCDR) of the Microwave Humidity Sounder (MHS) onboard NOAA satellites N18 and N19 and EUMETSAT satellites Metop-A and Metop-B. The FCDR provides recalibrated brightness temperatures with detailed uncertainty estimates and error correlation information. The FCDR was generated in the EU Horizon2020 Fidelity and uncertainty in climate data records from Earth Observations (FIDUCEO) project and as further elaborated in the Product User Guide, the FCDR files are provided in FIDUCEO EASY netCDF4 formats.

Campaign: Advanced Very High Resolution Radiometer Oceans Pathfinder Sea Surface Temperature Data Sets (AVHRR Pathfinder SST v5) The 4 km AVHRR Pathfinder Version 5 SST Project (Pathfinder V5) is a new reanalysis of the AVHRR data stream developed by the University of Miami's Rosenstiel School of Marine and Atmospheric Science (RSMAS) and the NOAA National Oceanographic Data Center (NODC). In partnership with NODC and RSMAS is NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), which has years of experience serving and developing earlier versions of the Pathfinder dataset. Currently in the third year of a three-year demonstration effort, it is hoped that this system can be implemented as an ongoing effort as part of a broader SST climate data record system. Provenance: local copy of the 4 km AVHRR Pathfinder Version 5 SST Monthly Means Data Set (daytime measurements) downloaded via FTP from: ftp://podaac.jpl.nasa.gov/pub/sea_surface_temperature/avhrr/pathfinder/data_v5/monthly/day/04km/. Files are located under WELLE.ZMAW.DE:/scratch/local3/u290022/DATA/SATELLITE/AVHRR/pathfinder/data_v5/monthly/day/04km/. The 4 km AVHRR Pathfinder Version 5 SST Project (Pathfinder V5) is a new reanalysis of the AVHRR data stream developed by the University of Miami's Rosenstiel School of Marine and Atmospheric Science (RSMAS) and the NOAA National Oceanographic Data Center (NODC). In partnership with NODC and RSMAS is NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC). Methods: this reprocessing uses an improved version of the Pathfinder algorithm and processing steps to produce twice-daily global SST and related parameters back to 1981, at a resolution of approximately 4 km, the highest possible for a global AVHRR data set. Temporal averages for 5-day, 7-day, 8-day, Monthly, and Yearly periods are also produced. Current key improvements over the original 9 km Pathfinder SST data set include a more accurate, consistent land mask, higher spatial resolution, and inclusion of sea ice information. Additional improvements including better flagging of aerosol-contaminated retrievals and the provision of wind and aerosol ancillary data will be implemented in a future Version 6 reprocessing. Additionally the parameters in version 5.0 are contained in separate files which are in the HDF-SDS (scientific data set) format, unlike version 4.1 which was in HDF-RASTER. The data can be accessed via the NODC, see http://www.nodc.noaa.gov/SatelliteData/pathfinder4km for more information regarding user guide, tools, available data, quality, etc. The data can also be accessed via NASA's PO.DAAC website, see: http://podaac.jpl.nasa.gov/PRODUCTS/p216.html

Ten operational Numerical Weather Prediction (NWP) and two data assimilation centers are currently contributing analysis/assimilation and forecast model products from global and regional NWP suites, including both operational and reanalysis systems to this component of CEOP. The contributing centers include:

BoM: Bureau of Meteorology CPTEC: Centro de Previsao de Tempo e Estudos Climaticos ECMWF: European Centre for Medium-Range Weather Forecasts ECPC: Experimental Climate Prediction Center EMC: EPSON Meteo Center (Centro EPSON Meteo) GLDAS: Global Land Data Assimilation System GMAO: NASA Global Modeling and Assimilation Office JMA: Japan Meteorological Agency MSC: Meteorological Service Canada NCEP: National Centers for Environmental Prediction NCMRWF: National Center for Medium Range Weather Forecasting UKMO: UK Met Office The Max-Planck Institute for Meteorology (MPIM) in coordination with the ICSU World Data Center for Climate (WDCC) in Hamburg, Germany was designated as the CEOP model output archive center. The WDCC is administered by the Model and Data Group (M&D) at MPIM and the German Climate Computing Center (DKRZ).

To assist with the organization of this activity during the Coordinated Enhanced Observing Period ('CEOP'), a Model Output Management Document was drafted as a guide for the participating centers to use in setting up their processes for meeting their commitments to 'CEOP'. The Guidance Document addressed the two issues of (1) the model output variables requested by 'CEOP' and (2) the two types of requested model output, namely global gridded (in GRIB format) and site-specific Model Output Location Time Series (MOLTS) at each of the 'CEOP' Reference Sites.

A new version of the Guidance Document will be compiled that clarifies what model output data will be generated by the NWP Centers and Groups contributing to the model output component of Coordinated Energy and Water Cycle Observations Project (CEOP) and how they will interface/transfer the data that will be handled and retained at the WDCC. The issues covered in the document will include: (1) global versus regional products; (2) desired assimilation output; Interval and length of free-running forecasts; (3) Operational versus reanalysis data; (4) the CEOP schedule/archive periods; (5) the number and locations of MOLTS sites; and (6) the homogenizing of the model output and metadata formats (i.e. standard parameters).

Results up to this point in the CEOP model output generation effort make it clear that the transfer aspect of the data handling effort has been progressing well. Data from all twelve Centers participating in CEOP have been received at the data archive center and has either been placed into the database at the Hamburg facility, or is in the process of being entered into the database. The current data holdings in the MPIM archive can be viewed http://www.mad.zmaw.de/fileadmin/extern/wdc/ceop/Data_timeline_L_12.pdf.

This dataset offers a global (60°S to 75°N; 180°W to 180°E), high-resolution (0.1° × 0.1°), hourly historical reconstruction of 13 comprehensive heat stress indices for the period 1950–2024. Named the CHI (Comprehensive Heat Indices) dataset, the dataset provides the most comprehensive and detailed heat-stress climatology currently available, supporting studies of human thermal comfort, mortality risk, and climate adaptation across diverse environmental conditions.The dataset is built around 13 key thermal comfort and stress indicators, including:1. Mean Radiant Temperature (Tmrt)2. Universal Thermal Climate Index (UTCI)3. Wet-Bulb Globe Temperature (Twbg)4. Natural Wet-Bulb Temperatures (Tnwb)5. Indoor Wet-Bulb Temperature (Twb)6. Natural Lethal Heat Stress Index (Lsin)7. Indoor Lethal Heat Stress Index (Lsi)8. Globe Temperature (Tg)9. Heat Index (HI)10. Apparent Temperature (AT)11. Wind Chill (WC)12. Humidex (Hu)13. Normal Effective Temperature (NET)These indices quantify how the human body experiences atmospheric conditions, incorporating the effects of air temperature, humidity, wind speed, solar radiation, and soil moisture—factors critical to assessing heat-related discomfort, workability, and mortality risk.The dataset is derived from the ERA5 and ERA5-Land reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 provides hourly atmospheric data at a global scale using advanced data assimilation techniques, while ERA5-Land refines this output over land with a higher spatial resolution. Together, they underpin the CHI dataset's detailed global coverage (60°S to 75°N and 180°W to 180°E) and extended historical range.CHI dataset improves upon earlier datasets by:· Extended long-term coverage from 1950 to 2024· Utilising 2 m wind speed instead of the standard 10 m, enhancing near-surface physiological relevance· Computing solar zenith angles only during sunlit hours to avoid radiation-related index errors· Providing both indoor and outdoor variants of key indices (e.g., Lsi, Tw)· Offering the first global hourly dataset for the empirical, mortality-based lethal heat stress index (Ls)The dataset is particularly relevant for applications in climate-health research, occupational heat risk, and climate adaptation, especially in vulnerable regions such as the Middle East, South Asia, and sub-Saharan Africa.The dataset currently covers 01/01/1950 to 12/31/2024. The dataset is produced by the Climate Change Center at King Abdullah University of Science and Technology (KAUST) in collaboration with international researchers.Data Download Link and User Guide to Access DataThe CHI dataset is available for public access and download via Globus, hosted by the KAUST (King Abdullah University of Science & Technology) Data Repository – Datawaha. To access the data, the user should read the following document:'User-Guide-to-Access-CHI-Dataset-via-Globus'For further important details, e.g., metadata, the user should read the following document:'CHI-Dataset_Description'Cite Data: Malik, Abdul; Masabathini, Sateesh; Ahmed Shaikh, Mohsin; Kong, Qinqin; Usman, Muhammad; Prasad Dasari, Hari; et al. (2025). Comprehensive Heat Indices (CHI) Dataset (v1.0). figshare. Dataset. https://doi.org/10.6084/m9.figshare.30539867LicenseERA5-CHI (v1.0) © [Abdul Malik (KAUST)], 2025; Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0); Source data: ERA5 reanalysis (© ECMWF, Copernicus Climate Change Service). Contains modified Copernicus Climate Change Service information [2025]. Neither the European Commission nor ECMWF is responsible for any use of the information it contains.For license, read LICENSE.txt

These datasets were prepared within the scope of the EJP SOIL programme. The datasets are extracted from different sources, clipped and reprojected to EPSG:3035. The sources are listed in the table below. The datasets were used as environmental layers to prodict soil property distribution (soil maps) at National and continental level within the EJP SOIL programme. Dataset sources: Copernicus Climate Data Store https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview RESOLVE Biodiversity and Wildlife Solutions https://ecoregions2017.appspot.com/ Copernicus Land Monitoring Service https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/ European Union/ESA/Copernicus https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/ GLiM - Global Lithological Map https://www.geo.uni-hamburg.de/en/geologie/forschung/aquatische-geochemie/glim.html

Overview:ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.

Processing steps:The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA

Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.

Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):

maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))

actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))

relative humidity = actual water pressure / maximum water pressure

The resulting relative humidity has been aggregated to monthly averages.

Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].

The data have been reprojected to EU LAEA.

File naming scheme (YYYY = year; MM = month):ERA5_land_rh2m_avg_monthly_YYYY_MM.tif

Projection + EPSG code:EU LAEA (EPSG: 3035)

Spatial extent:north: 6874000south: -485000west: 869000east: 8712000

Spatial resolution:1000 m

Temporal resolution:Monthly

Pixel values:Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)

Software used:GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2

Original ERA5-Land dataset license:https://apps.ecmwf.int/datasets/licences/copernicus/

CHELSA climatologies (V1.2):Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Processed by:mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC

Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: https://doi.org/10.5281/zenodo.6146383

This dataset provides bias-corrected reconstruction of near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). It is intended to be used as a meteorological forcing dataset for land surface and hydrological models. The dataset has been obtained using the same methodology used to derive the widely used water, energy and climate change (WATCH) forcing data, and is thus also referred to as WATCH Forcing Data methodology applied to ERA5 (WFDE5). The data are derived from the ERA5 reanalysis product that have been re-gridded to a half-degree resolution. Data have been adjusted using an elevation correction and monthly-scale bias corrections based on Climatic Research Unit (CRU) data (for temperature, diurnal temperature range, cloud-cover, wet days number and precipitation fields) and Global Precipitation Climatology Centre (GPCC) data (for precipitation fields only). Additional corrections are included for varying atmospheric aerosol-loading and separate precipitation gauge observations. For full details please refer to the product user-guide. This dataset was produced on behalf of Copernicus Climate Change Service (C3S).

Ten operational Numerical Weather Prediction (NWP) and two data assimilation centers are currently contributing analysis/assimilation and forecast model products from global and regional NWP suites, including both operational and reanalysis systems to this component of CEOP. The contributing centers include:

BoM: Bureau of Meteorology CPTEC: Centro de Previsao de Tempo e Estudos Climaticos ECMWF: European Centre for Medium-Range Weather Forecasts ECPC: Experimental Climate Prediction Center EMC: EPSON Meteo Center (Centro EPSON Meteo) GLDAS: Global Land Data Assimilation System GMAO: NASA Global Modeling and Assimilation Office JMA: Japan Meteorological Agency MSC: Meteorological Service Canada NCEP: National Centers for Environmental Prediction NCMRWF: National Center for Medium Range Weather Forecasting UKMO: UK Met Office The Max-Planck Institute for Meteorology (MPIM) in coordination with the ICSU World Data Center for Climate (WDCC) in Hamburg, Germany was designated as the CEOP model output archive center. The WDCC is administered by the Model and Data Group (M&D) at MPIM and the German Climate Computing Center (DKRZ).

To assist with the organization of this activity during the Coordinated Enhanced Observing Period ('CEOP'), a Model Output Management Document was drafted as a guide for the participating centers to use in setting up their processes for meeting their commitments to 'CEOP'. The Guidance Document addressed the two issues of (1) the model output variables requested by 'CEOP' and (2) the two types of requested model output, namely global gridded (in GRIB format) and site-specific Model Output Location Time Series (MOLTS) at each of the 'CEOP' Reference Sites.

A new version of the Guidance Document will be compiled that clarifies what model output data will be generated by the NWP Centers and Groups contributing to the model output component of Coordinated Energy and Water Cycle Observations Project (CEOP) and how they will interface/transfer the data that will be handled and retained at the WDCC. The issues covered in the document will include: (1) global versus regional products; (2) desired assimilation output; Interval and length of free-running forecasts; (3) Operational versus reanalysis data; (4) the CEOP schedule/archive periods; (5) the number and locations of MOLTS sites; and (6) the homogenizing of the model output and metadata formats (i.e. standard parameters).

Results up to this point in the CEOP model output generation effort make it clear that the transfer aspect of the data handling effort has been progressing well. Data from all twelve Centers participating in CEOP have been received at the data archive center and has either been placed into the database at the Hamburg facility, or is in the process of being entered into the database. The current data holdings in the MPIM archive can be viewed http://www.mad.zmaw.de/fileadmin/extern/wdc/ceop/Data_timeline_L_12.pdf.

Release 2 of the Fundamental Climate Data Record (FCDR) of the Microwave Humidity Sounder consists of measurements from Special Sensor Microwave Humidity (SSM/T-2) onboard Defense Meteorologocal Satellite Program (DMSP) satellites F11, F12, F14, and F15. The FCDR provides recalibrated brightness temperatures with detailed uncertainty estimates and error correlation information. It also comes with additional cloud flag information. The FCDR was generated for the Copernicus Climate Change Service (C3S) and as further elaborated in the Product User Guide, the FCDR files are provided in netCDF4 format.

M2TCNXLTM (or tavgC_2d_ltm_Nx) is a 2-dimensional monthly data collection for climatological long term mean and standard deviation representing the interannual variability on a monthly timescale, derived from monthly Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) datasets. V2 of this data collection is calculated with data from January 1991 to December 2020. In contrast, V1, the original version, is computed with data from an earlier 30-year time of 1981-2010.

This collection consists of meteorological diagnostics, such as air temperature (maximum, mean, and minimum at 2-meter), wind components at different vertical levels (2-meter, 10-meter, 50-meter, 850 hPa, 500hPa, and 250 hPa), sea level pressure, surface pressure, and total precipitation, evaporation, and total precipitable water vapor.

MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by the NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present, with a latency of ~3 weeks after the end of the previous month.

Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes”, linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original filename.

MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changes to tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.

Questions: If you have a question, please read the "MERRA-2 File Specification Document'', “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page for more information. If these documents do not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

M2C0NXASM (or const_2d_asm_Nx) is a data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of 2-dimensional constant model parameters, such as the fraction of lake, land, and ocean within a model grid cell.

MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4.

MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.

Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

Ten operational Numerical Weather Prediction (NWP) and two data assimilation centers are currently contributing analysis/assimilation and forecast model products from global and regional NWP suites, including both operational and reanalysis systems to this component of CEOP. The contributing centers include:

BoM: Bureau of Meteorology CPTEC: Centro de Previsao de Tempo e Estudos Climaticos ECMWF: European Centre for Medium-Range Weather Forecasts ECPC: Experimental Climate Prediction Center EMC: EPSON Meteo Center (Centro EPSON Meteo) GLDAS: Global Land Data Assimilation System GMAO: NASA Global Modeling and Assimilation Office JMA: Japan Meteorological Agency MSC: Meteorological Service Canada NCEP: National Centers for Environmental Prediction NCMRWF: National Center for Medium Range Weather Forecasting UKMO: UK Met Office The Max-Planck Institute for Meteorology (MPIM) in coordination with the ICSU World Data Center for Climate (WDCC) in Hamburg, Germany was designated as the CEOP model output archive center. The WDCC is administered by the Model and Data Group (M&D) at MPIM and the German Climate Computing Center (DKRZ).

To assist with the organization of this activity during the Coordinated Enhanced Observing Period ('CEOP'), a Model Output Management Document was drafted as a guide for the participating centers to use in setting up their processes for meeting their commitments to 'CEOP'. The Guidance Document addressed the two issues of (1) the model output variables requested by 'CEOP' and (2) the two types of requested model output, namely global gridded (in GRIB format) and site-specific Model Output Location Time Series (MOLTS) at each of the 'CEOP' Reference Sites.

A new version of the Guidance Document will be compiled that clarifies what model output data will be generated by the NWP Centers and Groups contributing to the model output component of Coordinated Energy and Water Cycle Observations Project (CEOP) and how they will interface/transfer the data that will be handled and retained at the WDCC. The issues covered in the document will include: (1) global versus regional products; (2) desired assimilation output; Interval and length of free-running forecasts; (3) Operational versus reanalysis data; (4) the CEOP schedule/archive periods; (5) the number and locations of MOLTS sites; and (6) the homogenizing of the model output and metadata formats (i.e. standard parameters).

Results up to this point in the CEOP model output generation effort make it clear that the transfer aspect of the data handling effort has been progressing well. Data from all twelve Centers participating in CEOP have been received at the data archive center and has either been placed into the database at the Hamburg facility, or is in the process of being entered into the database. The current data holdings in the MPIM archive can be viewed http://www.mad.zmaw.de/fileadmin/extern/wdc/ceop/Data_timeline_L_12.pdf.