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197 results for “era5-land”
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1999–2015)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6367463</a></p> <p>AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6369796</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1967–1981)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6367463</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p> <p>AERA5-Asia (0.1°/ hourly, 1999–2015, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4264451">10.5281/zenodo.4264451</a></p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1951–1966)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6369796</a></p> <p>AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.4266081</a></p> <p>AERA5-Asia (0.1°/ hourly, 1999–2015, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4264451">10.5281/zenodo.4264451</a></p>
ERA5-Land weekly: Total precipitation, weekly time series for Europe at 1 km resolution (2016 - 2020)
<p>Overview:<br> 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.</p> <p>Total precipitation:<br> 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.</p> <p>Processing steps:<br> 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:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate proportion of ERA5-Land / aggregated CHELSA<br> 3. interpolate proportion with a Gaussian filter to 30 arc seconds<br> 4. multiply the interpolated proportions with CHELSA<br> 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.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.<br> Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation.</p> <p>File naming:<br> Average of daily sum: <code>era5_land_prectot_avg_weekly_YYYY_MM_DD.tif</code><br> Sum of daily sum: <code>era5_land_prectot_sum_weekly_YYYY_MM_DD.tif</code></p> <p>The date in the file name determines the start day of the week (Saturday).</p> <p>Pixel values:<br> mm * 10<br> Example: Value 218 = 21.8 mm</p> <p>Coordinate reference system:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 1km</p> <p>Temporal resolution:<br> weekly</p> <p>Period:<br> 01/01/2016 - 12/31/2020</p> <p>Lineage:<br> Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original ERA5-Land dataset license:<br> <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2):<br> 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. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> 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. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Other resources:<br> <a href="https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b">https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b</a></p> <p>Format: GeoTIFF</p> <p>Representation type: Grid</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact:<br> mundialis GmbH & Co. KG, info@mundialis.de</p>
ERA5-Land selected indicators daily aggregates for Africa, 2005
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2005.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>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.</p> <p>Total precipitation:<br>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.</p> <p>Processing steps:<br>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) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). 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: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate proportion of ERA5-Land / aggregated CHELSA <br>3. interpolate proportion with a Gaussian filter to 30 arc seconds <br>4. multiply the interpolated proportions with CHELSA <br>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.</p> <p>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.</p> <p>File naming:<br><code>ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif</code> <br>e.g.:<code>ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>mm * 10<br>Scaled to Integer, example: value 218 = 21.8 mm</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28:18N<br>south: 14:42N<br>west: 17:05W<br>east: 4:49W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Lineage:<br>Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original ERA5-Land dataset license:<br><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>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. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>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. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Representation type: Grid</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>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.</p> <p>Processing steps:<br>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) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). 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: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>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.</p> <p>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):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br>north: 6874000<br>south: -485000<br>west: 869000<br>east: 8712000</p> <p>Spatial resolution:<br>1000 m</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>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. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>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. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>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</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="https://doi.org/10.5281/zenodo.6146383">https://doi.org/10.5281/zenodo.6146383</a></p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 1963
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1963.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2009
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2009.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2001
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2001.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2011
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2011.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE (1982–1998)
<p>AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.</p> <p><strong>How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E–150°E, 15°S–55°N, land.</p> <p>AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6367463</a></p> <p>AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4266081">10.5281/zenodo.6369796</a></p> <p>AERA5-Asia (0.1°/ hourly, 1999–2015, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.4264451">10.5281/zenodo.4264451</a></p>
AERA5-Asia: a new Asian precipitation dataset (0.1°, hourly, 1951-2015, Asia) in exploiting the spatiotemporal characteristics of ERA5-Land under the total volume control by APHRODITE (1981)
<p>The datasets provide a long term precipitation dataset with finer quality over the Asia (0.1°, hourly, 1951-2015, Asia) for the Asian applications.</p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E-150°E, 15°S-55°N, land.</p>
Merged HLS2 (L30), ERA5-Land inputs and sample predictions of land surface temperature for the IBM granite-geospatial-land-surface-temperature model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and ERA5-Land data following the UTM CRS:WGS84. It has been assembled for predicting land surface temperature with a fine-tuned granite geospatial foundation model developed by IBM Research. In addition, we include sample predictions of land surface temperature derived from this model. Please see https://huggingface.co/ibm-granite/granite-geospatial-land-surface-temperature for more information on data preparation and model use.</p> <p><strong>HLS:</strong></p> <p>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <h4><strong>ERA5-Land:<br></strong></h4> <p>Copernicus Climate Change Service, Climate Data Store, (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p> <h4><strong>LST-predictions:</strong></h4> <p>These predictions of land surface temperature are derived from the IBM granite-geospatial-land-surface-temperature model and have been made available for Abidjan, Côte d’Ivoire and Johannesburg, South Africa for the period 2013-2023. </p> <h4>Attribution</h4> <p>Copernicus programme:</p> <p>Contains modified Copernicus Climate Change Service information [2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Data</strong></p> <p>Muñoz Sabater, J., Comyn-Platt, E., 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., Cagnazo, C., Cucchi, M. (2024): ERA5-land post-processed daily-statistics from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.e9c9c792">10.24381/cds.e9c9c792</a> (Accessed on 05-11-2024)</p>
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/ead8ccde-9167-472f-959f-e57a77227f7a. Produced on QA4SM (https://qa4sm.eu)
ERA5-Land and AEMET datasets for the region of Spain.
<p>This repository includes hourly wind speed data from the ERA5-Land dataset for the dates between 2010 and 2021 for the region of Spain. It also includes AEMET wind speed data already homogenized using the R package called Climatol</p>
Weekly time series of total precipitation for Europe at 1 km resolution (2016 - 2020) derived from ERA5-Land data
<p> </p> <p> </p> <p><strong>Data have been moved to</strong>: <a href="https://doi.org/10.5281/zenodo.6559048">https://doi.org/10.5281/zenodo.6559048</a></p> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.