land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

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".

This dataset contains daily histograms of wind speed at 100m ("WS100"), wind direction at 100 m ("WD100") and an atmospheric stability proxy ("STAB") derived from the ERA5 hourly data on single levels [1] accessed via the Copernicus Climate Change Climate Data Store [2]. The dataset covers six geographical regions (illustrated in regions.png) on a reduced 0.5 x 0.5 degrees regular grid and covers the period 1994 to 2023 (both years included). The dataset is packaged as a zip folder per region which contains a range of monthly zip folders following the convention of zarr ZipStores (more details here: https://zarr.readthedocs.io/en/stable/api/storage.html). Thus, the monthly zip folders are intended to be used in connection with the xarray python package (no unzipping of the monthly files needed).Wind speed and wind direction are derived from the U- and V-components. The stability metric makes use of a 5-class classification scheme [3] based on the Obukhov length whereby the required Obukhov length was computed using [4]. The following bins (left edges) have been used to create the histograms:Wind speed: [0, 40) m/s (bin width 1 m/s)Wind direction: [0,360) deg (bin width 15 deg)Stability: 5 discrete stability classes (1: very unstable, 2: unstable, 3: neutral, 4: stable, 5: very stable)Main Purpose: The dataset serves as minimum input data for the CLIMatological REPresentative PERiods (climrepper) python package (https://gitlab.windenergy.dtu.dk/climrepper/climrepper) in preparation for public release).References:[1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)[2] Copernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)'[3] Holtslag, M. C., Bierbooms, W. A. A. M., & Bussel, G. J. W. van. (2014). Estimating atmospheric stability from observations and correcting wind shear models accordingly. In Journal of Physics: Conference Series (Vol. 555, p. 012052). IOP Publishing. https://doi.org/10.1088/1742-6596/555/1/012052[4] Copernicus Knowledge Base, ERA5: How to calculate Obukhov Length, URL: https://confluence.ecmwf.int/display/CKB/ERA5:+How+to+calculate+Obukhov+Length, last accessed: Nov 2024

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.

Total precipitation:
Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:
The original hourly ERA5-Land 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 proportion of ERA5-Land / aggregated CHELSA
3. interpolate proportion with a Gaussian filter to 30 arc seconds
4. multiply the interpolated proportions with CHELSA
Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.
Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation.

File naming:
Average of daily sum: era5_land_prectot_avg_weekly_YYYY_MM_DD.tif
Sum of daily sum: era5_land_prectot_sum_weekly_YYYY_MM_DD.tif

The date in the file name determines the start day of the week (Saturday).

Pixel values:
mm * 10
Example: Value 218 = 21.8 mm

Coordinate reference system:
ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)

Spatial extent:
north: 82:00:30N
south: 18N
west: 32:00:30W
east: 70E

Spatial resolution:
1km

Temporal resolution:
weekly

Period:
01/01/2016 - 12/31/2020

Lineage:
Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Original ERA5-Land dataset license:
https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

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.kd1d4
Original 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

Other resources:
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b

Format: GeoTIFF

Representation type: Grid

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

Contact:
mundialis GmbH & Co. KG, info@mundialis.de

This dataset provides daily estimates of global sea surface temperature (SST) based on observations from multiple satellite sensors since September 1981. SST is known to be a significant driver of global weather and climate patterns and to play important roles in the exchanges of energy, momentum, moisture and gases between the ocean and atmosphere. As such, its knowledge is essential to understand and assess variability and long-term changes in the Earth’s climate. The SST data provided here are based on measurements carried out by the following infrared sensors flown onboard multiple polar-orbiting satellites: the series of Advanced Very High Resolution Radiometers (AVHRRs), the series of Along Track Scanning Radiometers (ATSRs), and the Sea and Land Surface Temperature Radiometer (SLSTR). The dataset provides SST products of different processing levels. Only Level-3 Collated and Level-4 and served through this entry in the Catalogue. Due to the large number of files at Level-2 Pre-processed and Level-3 Collated these products are served through the Climate Data Store API. For more information on how to access these levels consult the documentation. The four types of products are:

Level-2 Pre-processed (L2P): SST data on the native satellite swath grid and derived from single-sensor measurements. Level-3 Uncollated (L3U): SST product generated by regridding L2P data onto a global latitude-longitude grid. Level-3 Collated (L3C): global daily (day and night) single-sensor SST product based on collated L3U data. Level-4 (L4): spatially complete global SST product based on data from multiple sensors.

These products are available as Climate Data Records (CDRs), which have sufficient length, consistency, and continuity to be used to assess climate variability and changes. These SST CDRs are identical to those produced as part of the European Space Agency (ESA) SST Climate Change Initiative (CCI) project. Interim CDRs (ICDRs) are produced at levels L3C and L4 on behalf of the Copernicus Climate Change Service (C3S) to extend the baseline CDRs. Both SST CDRs and ICDRs are generated using software and algorithms developed as part of the ESA SST CCI. Users should use the most recent version of the dataset whenever possible. Data from the previous version are also made available but cover shorter periods.

ERA5-Land daily: Total precipitation, daily time series for Europe at 30 arc seconds (ca. 1000 meter) resolution (2000 - 2020)

Source data:
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.

Total precipitation:
Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:
The original hourly ERA5-Land data (period 2000 - 2020) 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 proportion of ERA5-Land / aggregated CHELSA
3. interpolate proportion with a Gaussian filter to 30 arc seconds
4. multiply the interpolated proportions with CHELSA
Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

Data available is the daily sum of precipitation.
File naming:
era5_land_daily_prectot_YYYYMMDD_sum_30sec.tif
e.g.:era5_land_daily_prectot_20200418_sum_30sec.tif

The date within the filename is Year, Month and Day of timestamp.

Pixel values:
mm * 10
Scaled to Integer, example: value 218 = 21.8 mm

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:
north: 82:00:30N
south: 18:00:00N
west: 32:00:30W
east: 70:00:00E

Temporal extent:
01.01.2000 - 31.12.2020
NOTE: Due to file size, only 2020 data are available here. Data for other years are available on request.

Spatial resolution:
30 arc seconds (approx. 1000 m)

Temporal resolution:
daily

Lineage:
Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Format: GeoTIFF

Original ERA5-Land dataset license:
https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

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.kd1d4
Original 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

Representation type: Grid

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

Contact:
mundialis GmbH & Co. KG, info@mundialis.de

ERA5-Land daily: Air temperature at 2 meter above surface, daily time series for Europe at 30 arc seconds (ca. 1000 meter) resolution (2000 - 2020)

Source data: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.

Air temperature (2 m):Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.

Processing steps:The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to:- aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) 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

Data available is the daily average, minimum and maximum of air temperature (2 m).

File naming:Daily average: ERA5_land_daily_t2m_YYYYMMDD_avg_30sec.tif Daily min: ERA5_land_daily_t2m_YYYYMMDD_min_30sec.tif Daily max: ERA5_land_daily_t2m_YYYYMMDD_max_30sec.tif

The date within the filename is Year, Month and Day of timestamp.

Pixel value:°C * 10Example: Value 44 = 4.4 °C

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:north: 82:00:30Nsouth: 18:00:00Nwest: 32:00:30Weast: 70:00:00E

Temporal extent:01.01.2000 - 31.12.2020NOTE: Due to file size, only 2020 data are available here. Data for other years are available on request.

Spatial resolution:30 arc seconds (approx. 1000 m)

Temporal resolution:daily

Format: GeoTIFF

Representation type: Grid

Software used:GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Lineage:Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Original ERA5-Land dataset license:https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

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

Other resources:https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b

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

Contact:mundialis GmbH & Co. KG, info@mundialis.de

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.

Air temperature (2 m): Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.

Processing steps: The original hourly ERA5-Land 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

The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of air temperature (2 m).

File naming: Average of daily average: era5_land_t2m_avg_weekly_YYYY_MM_DD.tif Max of daily max: era5_land_t2m_max_weekly_YYYY_MM_DD.tif Min of daily min: era5_land_t2m_min_weekly_YYYY_MM_DD.tif

The date in the file name determines the start day of the week (Saturday).

Pixel value: °C * 10 Example: Value 44 = 4.4 °C

The QML or SLD style files can be used for visualization of the temperature layers.

Coordinate reference system: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)

Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E

Spatial resolution: 1km

Temporal resolution: weekly

Time period: 01/01/2016 - 12/31/2020

Format: GeoTIFF

Representation type: Grid

Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Lineage: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

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.kd1d4 Original 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

Other resources: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b

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

Contact: mundialis GmbH & Co. KG, info@mundialis.de

Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)

Source data: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.

Total precipitation:Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:The original hourly ERA5-Land 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 proportion of ERA5-Land / aggregated CHELSA 3. interpolate proportion with a Gaussian filter to 30 arc seconds 4. multiply the interpolated proportions with CHELSA Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month.

File naming:ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif e.g.:ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif

The date within the filename is year and month of aggregated timestamp.

Pixel values:mm * 10Scaled to Integer, example: value 218 = 21.8 mm

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:north: 28:18Nsouth: 14:42Nwest: 17:05Weast: 4:49W

Temporal extent:January 2019 - December 2023

Spatial resolution:30 arc seconds (approx. 1000 m)

Temporal resolution:monthly

Lineage:Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:GRASS GIS 8.3.2

Format: GeoTIFF

Original ERA5-Land dataset license:https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

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

Representation type: Grid

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

Contact: mundialis GmbH & Co. KG, info@mundialis.de

Pre-proccesd data

The pre-proccesd data consists of input-target pairs. The inputs include surface weather observations within a 3-hour window, GOES-16 satellite imagery within a 3-hour window, HRRR forecast, and topography. The target is a combination of RTMA and surface weather observations. The table below summarizes the input and target datasets utilized in this study. All data were regularized to grids of size 512 $\times$ 1280 with a spatial resolution of 0.05 $\times$ 0.05 $^\circ$.

2022-10-01_06.nc is a sample of pre-proccesd data. The vairables in this file contain the input-target pairs mentioned above.

Pre-comuted normalization statistics

stats.csv is pre-comuted normalization statistics.

Pre-trained model weights

best_ckpt.tar is the pre-trained model weights.

References

1. Jin, W. et al. WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models. (2024).
2. Dowell, D. et al. The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description. Weather and Forecasting 37, (2022).
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