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. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

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".

ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31 kilometer (km) grid and resolve the atmosphere using 137 levels from the surface up to a height of 80 km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 MONTHLY provides aggregated values for each month for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, monthly minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Monthly total precipitation values are given as monthly sums. All other parameters are provided as monthly averages. ERA5 data is available from 1940 to three months from real-time, the version in the EE Data Catalog is available from 1979. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Monthly aggregates have been calculated based on the ERA5 hourly values of each parameter.

Overview: era5.copernicus: air temperature daily averages from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: 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. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily air temperature 2m above ground in degrees Celsius x 10.

developed by C3S at ECMWF

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time.

ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters (for example, mean rates and accumulations) that are not available from the analyses.

Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles.

Overview: era5.copernicus: precipitation daily sums from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: 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. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent cumulative daily precipitation in mm x 10.

ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, and the first reanalysis produced as an operational service. It utilizes the best available observation data from satellites and in-situ stations, which are assimilated and processed using ECMWF's Integrated Forecast System (IFS) Cycle 41r2. The dataset provides all essential atmospheric meteorological parameters like, but not limited to, air temperature, pressure and wind at different altitudes, along with surface parameters like rainfall, soil moisture content and sea parameters like sea-surface temperature and wave height. ERA5 provides data at a considerably higher spatial and temporal resolution than its legacy counterpart ERA-Interim. ERA5 consists of high resolution version with 31 km horizontal resolution, and a reduced resolution ensemble version with 10 members. It is currently available since 2008, but will be continuously extended backwards, first until 1979 and then to 1950. Learn more about ERA5 in Jon Olauson's paper ERA5: The new champion of wind power modelling?.

This dataset contains ERA5-Land air temperature data, which is a reanalysis model information with a spatial resolution of 0.1° x 0.1° (approximately 9 km), available from 2000-2022 for Barranquilla, Colombia. The air temperature variable at 2 meters with hourly resolution was processed to calculate average, minimum and maximum temperature (°C) per epidemiological week. See Metadata file for more information.

ERA-5 Hydrometeorological Variables Daily – Verbier, Switzerland

The dataset contains hydrometeorological variables from the ERA5-Land dataset, covering the period from January 10, 2023, to August 29, 2024.

It is provided in CSV format and includes the following variables:

• date: The date of observation (format: DD-MM-YY).
• dewpoint_temperature_2m: Dewpoint temperature at 2 meters above the surface (Kelvin).
• runoff_sum: Total runoff (surface + subsurface) (meters).
• skin_temperature: Temperature of the land surface (Kelvin).
• snow_cover: Percentage of land covered by snow (%).
• snow_depth: Snow depth (meters).
• snow_depth_water_equivalent: Equivalent water content of snow depth (meters).
• snowfall_sum: Total snowfall (meters).
• snowmelt_sum: Total snowmelt (meters).
• sub_surface_runoff_sum: Total subsurface runoff (meters).
• surface_net_solar_radiation_sum: Total net solar radiation received at the surface (Joules per square meter).
• surface_net_thermal_radiation_sum: Total net thermal radiation at the surface (Joules per square meter).
• surface_runoff_sum: Total surface runoff (meters).
• temperature_2m: Air temperature at 2 meters above the surface (Kelvin).
• total_precipitation_sum: Total precipitation (meters).

The reference dataset is: "ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis"

More details for the reference dataset can be found at: https://doi.org/10.24381/cds.68d2bb30

ben-ge/ERA-5: BigEarthNet Extended with Geographical and Environmental Data/Environmental Data

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:

* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;
* land-use/land-cover data extracted from ESA Worldcover;
* climate zone information extracted from Beck et al. 2018;
* environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;
* a seasonal encoding.

This archive contains the environmental data of ben-ge, which were extracted from the ERA-5 global reanalysis.

Data

Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations).

Environmental data are available in the file ben-ge_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters:
* atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar]
* temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K]
* temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K]
* wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s]
* wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]
* relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%]
* relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%]

as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details.

Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:
* patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;
* patch_id_s1: the Sentinel-1 patch id for this specific patch;
* timestamp_s2: the timestamp for the Sentinel-2 observation;
* timestamp_s1: the timestamp for the Sentinel-1 observation;
* season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;
* season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;
* lon: longitude (WGS-84) of the center of the patch [degrees];
* lat: latitude (WGS-84) of the center of the patch [degrees];
* climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).

File and directory structure

This archive contains the following directory and file structure:

|
|--- README (this file)
|--- ben-ge_meta.csv (ben-ge meta data)
|--- ben-ge_era-5.csv (ben-ge environmental data)

To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/era-5 requires 80 MB of space.

Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows:

|
|--- ben-ge_meta.csv (ben-ge meta data)
|--- ben-ge_era-5.csv (ben-ge environmental data)
|--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data)
|--- dem/ (digital elevation model data)
| |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif
| ...
|--- esaworldcover/ (land-use/land-cover data)
| |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif
| ...
|--- sentinel-1/ (Sentinel-1 SAR data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)
| |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)
| ...
|--- sentinel-2/ (Sentinel-2 multispectral data)
| |--- S2B_MSIL2A_20170818T112109_31_83/
| |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data)
| |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data)
| |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file)
...

More Information

For more information, please refer to https://github.com/HSG-AIML/ben-ge.

Citing ben-ge

If you use data contained in this archive, please cite the following paper:

M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.

[ Derived from parent entry - See data hierarchy tab ]

This experiment comprises data that have been used in Hagemann et al. (submitted). It comprises daily data of surface runoff and subsurface runoff from HydroPy and simulated daily discharges (river runoff) of the HD model. The discharge data close the water cycle at the land-ocean interface so that the discharges can be used as lateral freshwater input for ocean models applied in the European region.

a) HD5-ERA5 ERA5 is the fifth generation of atmospheric reanalysis (Hersbach et al., 2020) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides hourly data on many atmospheric, land-surface, and sea-state parameters at about 31 km resolution. The global hydrology model HydroPy (Stacke and Hagemann, 2021) was driven by daily ERA5 forcing data from 1979-2018 to generate daily input fields of surface and subsurface runoff at the ERA5 resolution. It uses precipitation and 2m temperature directly from the ERA5 dataset. Furthermore, potential evapotranspiration (PET) was calculated from ERA5 data in a pre-processing step and used as an additional forcing for HydroPy. Here, we applied the Penman-Monteith equation to calculate a reference evapotranspiration following (Allen et al., 1998) that was improved by replacing the constant value for albedo with a distributed field from the LSP2 dataset (Hagemann, 2002). In order to initialize the storages in the HydroPy model and to avoid any drift during the actual simulation period, we conducted a 50-years spin-up simulation by repeatedly using year 1979 of the ERA5 dataset as forcing. To generate river runoff, the Hydrological discharge (HD) model (Hagemann et al., 2020; Hagemann and Ho-Hagemann, 2021) was used that was operated at 5 arc minutes horizontal resolution. The HD model was set up over the European domain covering the land areas between -11°W to 69°E and 27°N to 72°N. First, the forcing data of surface and sub-surface runoff simulated by HydroPy were interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

b) HD5-EOBS The E-OBS dataset (Cornes et al., 2018) comprises several daily gridded surface variables at 0.1° and 0.25° resolution over Europe covering the area 25°N-71.5°N x 25°W-45°E. The dataset has been derived from station data collated by the ECA&D (European Climate Assessment & Dataset) initiative (Klein Tank et al., 2002; Klok and Klein Tank, 2009). In the present study, we use the best-guess fields of precipitation and 2m temperature of vs. 22 (EOBS22) at 0.1° resolution for the years 1950-2018. HydroPy was driven by daily EOBS22 data of temperature and precipitation at 0.1° resolution from 1950-2019. The potential evapotranspiration (PET) was calculated following the approach proposed by (Thornthwaite, 1948) including an average day length at a given location. As for HD5-ERA5, the forcing data of surface and sub-surface runoff simulated by HydroPy were first interpolated to the HD model grid. Then, daily discharges were simulated with the HD model.

Main reference: Hagemann, S., Stacke, T. (2022) Complementing ERA5 and E-OBS with high-resolution river discharge over Europe. Oceanologia 65: 230-248, doi:10.1016/j.oceano.2022.07.003

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). 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. So far this has not been the case and when this does occur users will be 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 1979 to present".

This dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model. The data was produced on behalf of the Copernicus Climate Change Service.

The USGS’s FORE-SCE model was used to produce land-use and land-cover (LULC) projections for the conterminous United States. The projections were originally created as part of the "LandCarbon" project, an effort to understand biological carbon sequestration potential in the United States. However, the projections are being used for a wide variety of purposes, including analyses of the effects of landscape change on biodiversity, water quality, and regional weather and climate. The year 1992 served as the baseline for the landscape modeling. The 1992 to 2005 period was considered the historical baseline, with datasets such as the National Land Cover Database (NLCD), USGS Land Cover Trends, and US Department of Agriculture's Census of Agriculture used to guide the recreation of historical land cover for this period. 2006 to 2100 was considered the future projection time frame. Four scenarios were modeled for 2006 to 2100, corresponding to four major scenario storylines from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES). The global IPCC SRES (A1B, A2, B1, and B2 scenarios) were downscaled to ecoregions in the conterminous United States, with the USGS Forecasting Scenarios of land use (FORE-SCE) model used to produce landscape projections consistent with the IPCC SRES. The land-use scenarios focused on socioeconomic impacts on anthropogenic land use (demographics, energy use, agricultural economics, and other socioeconomic considerations). The projections provided here are characterized by: 1) 250-meter spatial resolution (250-m pixels) 2) 17 land-cover classes, similar to classes from NLCD 3) Annual land cover maps from 1992 to 2100 4) Spatial coverage for the entire conterminous United States 5) An additional "forest stand age" layer for both the historical period (1992-2005) and the projected period (2006-2100). These data mark age in years since last land-use change or disturbance for forest pixels. Data are provided here for 1) the historical 1992 to 2005 period, and 2) for each of the four scenarios from 2006 to 2100. 10 .zip files are available for download, 5 representing land-use and land-cover maps for both the historical period and the four future scenarios, and 5 representing forest stand age. Each zip file contains GeoTIFF files with annual maps for the given timeframe. The metadata associated with this data release provides a key for identifying file names associated with each of the .zip files, as well as definitions for the 17 land-cover classes.

The Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is a NASA atmospheric reanalysis for the satellite era using the Goddard Earth Observing System Model, Version 5 (GEOS-5) with its Atmospheric Data Assimilation System (ADAS), version 5.12.4. The MERRA project focuses on historical climate analyses for a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. MERRA-2 was initiated as an intermediate project between the aging MERRA data and the next generation of Earth system analysis envisioned for the future coupled reanalysis. Without a substantial investment to update MERRA's data assimilation routines, the system lacked the capability to analyze the latest observations. In addition, numerous advances to the GEOS5 system had been implemented since freezing the MERRA system in 2008. Therefore, a new full reanalysis integration was undertaken. MERRA-2 covers the period 1980-present, continuing as an ongoing climate analysis as resources allow.

These data represent five high-latitude sites studied in the PAGE21 project (https://www.page21.eu): Samoylov, Kytalyk, Abisko, Zackenberg and Bayelva. Please see the linked manuscript for details of the sites. These are meteorological driving data, which were prepared using observations from the sites combined with reanalysis data for the grid cell containing the site. For the period 1901-1979, Water and Global Change forcing data (WFD) were used (Weedon et al., 2011). This has half-degree resolution for the whole globe at 3-hourly time resolution from 1901 to 2001. For the period 1979-2014, WATCH-ForcingData-ERA-Interim (WFDEI) was used (Weedon, 2013). For the time periods in which observed data were available, correction factors were generated by calculating monthly biases relative to the WFDEI data. These corrections were then applied to the time series from 1979 to 2014 of the WFDEI data. The WFD before 1979 were then corrected to match these data and the two datasets were joined at 1979 to provide gap-free 3-hourly forcing from 1901 to 2014. Local meteorological station observations were used for all variables except snowfall, which was estimated from the observed snow depth by treating increases in snow depth as snowfall events with an assumed snow density. See linked manuscript for more details. Variables provided: Air temperature Specific humidity Wind speed Incoming shortwave radiation Incoming longwave radiation Surface pressure Rainfall (both CRU and GPCC) (https://crudata.uea.ac.uk/cru/data/hrg/ ; https://climatedataguide.ucar.edu/climate-data/gpcc-global-precipitation-climatology-centre) Snowfall (both CRU and GPCC) Both corrected and uncorrected precipitation variables are provided. The data that were used in the linked publication were "Snowf_GPCC_cor" and "Rainf_GPCC_cor".

Global hourly 0.5-degree Surface Air Temperature (SAT) datasets were developed based on four reanalysis products [Modern-Era Retrospective Analysis for Research and Applications (MERRA for 1979-2009), 40-year ECMWF Re-Analysis (ERA-40 for 1958-2001), ECMWF Interim Re-Analysis (ERA-Interim for 1979-2009), and NCEP/NCAR reanalysis for 1948-2009)] and the Climate Research Unit Time Series version 3.10 (CRU TS3.10) for 1948-2009. The three-step adjustments included the spatial downscaling to 0.5-degree grid cells, the temporal interpolation from 6-hourly (in ERA-40 and NCEP/NCAR reanalysis) to hourly using the MERRA hourly SAT climatology for each day (and the linear interpolation from 3-hourly in ERA-Interim to hourly), and the bias correction in both monthly-mean maximum (Tmax) and minimum (Tmin) SAT using the CRU data. The final products have exactly the same monthly Tmax and Tmin as the CRU data, and perform well in comparison with in-situ hourly measurements over six sites and with a regional daily SAT dataset over Europe. They agree with each other much better than the original reanalyses, and the spurious SAT jumps of reanalyses over some regions are also substantially eliminated. One of the uncertainties in the final products can be quantified by the differences in the true monthly mean (using 24-hourly values) and the monthly averaged diurnal cycle from different final products.

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-5 reanalysis. The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The simulations cover the period covered by ERA5 reanalysis : 1981-2020 (1979-1980=Spin-up years) Data are available at the daily frequency, with one variable (10 years of data) per file. The available variables in this deposit are : LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] MB: Total snow water equivalent, [mm.We] MBrr: Daily rainfall, mm.We MBsf: Daily snowfall, mm.We QQz: Near-surface specific humidity at constant height, g/kg SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CC: Cloud cover, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP: Surface pressure, [hPa] ST: Surface temperature, [C] TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), C TTp: Constant pressure-level mean air temperature, C ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m] SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUl.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level at 2m (3) For variables UUz, VVz constant height level at 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City The region covered by this data is roughly the Greater Alpine region : ~ 42N-49N / ~2E:18E