Find research datasets worth reusing

opencc-by-sa-4.0Jun 2024View details →

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

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) (<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: 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: <code>ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif</code> e.g.:<code>ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif</code> The date within the filename is year and month of aggregated 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: 28:18N south: 14:42N west: 17:05W east: 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: <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> 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> 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> Representation type: Grid Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Contact: mundialis GmbH & Co. KG, info@mundialis.de

opencc-by-4.0Jun 2024View details →

HOTSSea v1 Forcings - CIOPSW, ERA5, ORAS5, Obs, Runoff etc

Forcings and analysis files used in Oldford et al., 2024, HOTSSea v1 (GMD-2024-58; https://doi.org/10.5194/gmd-2024-58), resources here to accompany manuscript.  All forcings provided here have been pre-processed for use in NEMO v3.6 HOTSSea v1 model. Original repositories are referenced below. Boundary: ORAS5 <ul> <li>see https://doi.org/10.5194/os-15-779-2019</li> </ul> Atmospheric: ERA5 <ul> <li>see <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></li> </ul> CIOPS-West <ul> <li>no DOI or publication currently available</li> <li>(Corresponding author email address: jean-philippe.paquin@ec.gc.ca)</li> <li>technical note: https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/tech_notes/technote_ciops-west-230_e.pdf</li> <li>technical specs: https://collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/tech_specifications/tech_specifications_CIOPS-WEST_e.pdf</li> <li>open data: https://eccc-msc.github.io/open-data/msc-data/nwp_ciops/readme_ciops_en/</li> <li>open data: https://open.canada.ca/data/dataset/390abee6-4ba0-4d6e-ae79-25753d1c43f3</li> </ul> ANALYSIS files output from running 'pypkg' scripts (see github repo) <ul> <li>Model run codes were renamed in manuscript, with mapping below:</li> <li>OLD-CODE | NEW-CODE</li> <li>RUN100   |   0.1</li> <li>RUN120   |   0.12</li> <li>RUN141   |   0.14</li> <li>RUN160   |   0.16</li> <li>RUN181   |   0.18</li> <li>RUN202   |   1.01</li> <li>RUN203   |   1.02</li> </ul> Observations (OBS-West) : <ul> <li>see manuscript (GMD-2024-58)</li> </ul>

opencc-by-4.0Jun 2024View details →

The processed atmospheric wind profiles dataset in near space from ERA5 (20-50km)

<h2>experimental data</h2>

opencc-by-sa-4.0Nov 2023View details →

Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2023) derived from ERA5-Land data

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<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: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> The resulting relative humidity has been aggregated to monthly averages. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. The data have been reprojected to EU LAEA. File naming scheme (YYYY = year; MM = month): <code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code> Projection + EPSG code: EU LAEA (EPSG: 3035) Spatial extent: north: 6874000 south: -485000 west: 869000 east: 8712000 Spatial resolution: 1000 m Temporal resolution: Monthly Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> 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> 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> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="https://doi.org/10.5281/zenodo.6146383">https://doi.org/10.5281/zenodo.6146383</a>

2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 1/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 4/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 5/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 6/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 3/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 2/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

ERA5_precipitation_sum_1950_2023

ERA5-Land is a reanalysis dataset that provides a consistent view of the evolution of terrestrial variables over decades at a higher resolution than ERA5, which was generated by replaying the terrestrial component of the ERA5 climate reanalysis of ECMWF. The reanalysis uses the laws of physics to combine model data with observations from around the world to produce a globally complete and consistent dataset. The reanalysis produces data that goes back decades and provides an accurate picture of past climate. The dataset includes all 50 variables available on the CDS. The asset is a monthly summary of the ECMWF ERA5 Land hourly asset, including both mobile and non-mobile bands. The mobile bands are created by collecting data from the first hour of the second day of each day of the month and adding them together, while the non-mobile bands are created by averaging all hourly data for the month. The flow bands are labelled with the ‘_sum’ identifier, which is different from the monthly data generated by the Copernicus Climate Data Store, which also averages the flow bands. When sub-monthly fields are not required, monthly totals have been pre-calculated for many applications that require easy and quick access to the data. ERA5-Land monthly summary data are available in real time from 1950, three months before the present. More information can be found in the Copernicus Climate Data Repository. Precipitation and other flow (accumulation) bands may occasionally have negative values that are physically unreasonable. Sometimes their values may be too high. This problem is caused by the way the GRIB format saves data: it simplifies or ‘packages’ the data into smaller, less precise numbers, which can lead to errors. These errors are made worse when the data changes a lot. As a result, when we look at a whole day's worth of data to calculate daily totals, sometimes the highest rainfall recorded at one time appears to be greater than the total rainfall measured throughout the day.

opencc-by-4.0Dec 2022View details →

Processing code and data for ERA5 analysis

Data, processing code, and instructions for ERA5 analysis

ERA5-Land selected indicators daily aggregates for the Latin America region, 1963

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1963.Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.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.Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for the Latin America region, 2009

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2009.Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.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.Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for the Latin America region, 2001

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2001.Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.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.Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for the Latin America region, 2011

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2011.Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.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.Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

opencc-zeroOct 2020View details →

Validation of ESA CCI SM combined v05.2 vs ESA CCI SM combined v04.7 vs ERA5 v20190613 - Whole period

QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ESA CCI SM combined v04.7 vs ERA5 v20190613. URL: https://qa4sm.eu/result/84315e3c-bd2d-4eab-82f4-076b153dff24/. Produced on QA4SM (https://qa4sm.eu)

opencc-by-4.0Nov 2020View details →

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)

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. 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. Data Format: GeoTIFF Spatial Coverage: 60°E–150°E, 15°S–55°N, land. 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> 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> 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>

opencc-by-4.0Nov 2020View details →

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)

The datasets provide a long term precipitation dataset with finer quality over the Asia (0.1°, hourly, 1951-2015, Asia) for the Asian applications. Data Format: GeoTIFF Spatial Coverage: 60°E-150°E, 15°S-55°N, land.