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310 results for “era5”
Validation of C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/9241fbf2-995f-4ece-be64-2302efaae280. Produced on QA4SM (https://qa4sm.eu)
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/b61ec3bb-0eb9-4803-bd70-2dd052c5bcfb. Produced on QA4SM (https://qa4sm.eu)
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/ceed8392-014f-48e6-a7f7-eb00d0b4ddd9. Produced on QA4SM (https://qa4sm.eu)
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/24418dc9-98ae-4b96-a272-87c4c6583df3. Produced on QA4SM (https://qa4sm.eu)
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/ed647646-8513-4bca-8d0a-1db534c145fd. Produced on QA4SM (https://qa4sm.eu)
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/62c2f4f1-572a-4932-b4c9-cf0d29da156b. Produced on QA4SM (https://qa4sm.eu)
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/d6936b1a-e01c-4908-8f71-ea4be275c4fd. Produced on QA4SM (https://qa4sm.eu)
Data: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
<p>Data for: <br><br>Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts</p> <p>Marian Schönauer<sup>1</sup>, Anneli M. Ågren<sup>2</sup>, Klaus Katzensteiner<sup>3</sup>, Florian Hartsch<sup>1</sup>, Paul Arp<sup>4</sup>, Simon Drollinger<sup>5</sup>, Dirk Jaeger<sup>1</sup></p> <p><sup>1</sup>Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany</p> <p><sup>2</sup>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden</p> <p><sup>3</sup>Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria</p> <p><sup>4</sup>Forestry and Environmental Management, University of New Brunswick, New Brunswick, Canada</p> <p><sup>5</sup>Department of Physical Geography, University of Göttingen, Göttingen, Germany</p>
Global Daily River Discharge product at 0.5° resolution from ISBA-CTRIP simulations based on ERA5-GPCC forcing from 1950 to 2023
<p>This product provides <strong>global daily river discharge at a resolution of 0.5 degree from 1950 to 2023</strong>. It is derived from an offline simulation conducted with the <a href="http://www.umr-cnrm.fr/spip.php?article1092&lang=en" target="_blank" rel="noopener">ISBA-CTRIP</a> global land surface modeling system (<a href="https://doi.org/10.1029/2018MS001545" target="_blank" rel="noopener"><em>Decharme et al. </em>2019</a>) embedded in the <a href="http://www.umr-cnrm.fr/spip.php?article145&lang=en" target="_blank" rel="noopener">SURFEX</a> version 8 modeling platform. It was designed for use in large-scale hydrological applications, as well as in the the <a href="http://www.umr-cnrm.fr/cmip6/spip.php?rubrique8" target="_blank" rel="noopener">CNRM climate models</a> that participate in <a href="https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6" target="_blank" rel="noopener">CMIP6</a> but also in large scale hydrological applications. The model is driven by meteorological forcing data (temperature, precipitation, humidity, winds, etc) derived from the <a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5" target="_blank" rel="noopener">ERA5</a> global atmospheric reanalysis, but where montly precipitation is corrected to align with the <a href="https://www.dwd.de/EN/ourservices/gpcc/gpcc.html" target="_blank" rel="noopener">GPPC</a> observed precipitation product. GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v2022_doi_download.html">Full Data Monthly Product Version 2022</a> at 0.5 degree resolution are used from 1950 to 2020. For the last three years (2021 to 2023), we used the GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_monitoring_v2022_doi_download.html" target="_blank" rel="noopener">Monitoring Product Version 2022</a>, which is only available at 1 degree resolution, and then interpolated at 0.5 degree using a conservative remapping.</p>
ALDIS cloud to ground lightning strike occurrence aggregated to spatiotemporal ERA5 cells (summer months 2010 to 2019)
<p>The dataset contains a binary classification whether at least one cloud-to-ground lightning flash as detected by the ALDIS [1] lightning location system occurred in the previous hour in an ERA5 grid cell. Data with an amplitude between -2 kA and +15 kA are excluded.<br><br></p> <p>The data cover 8.25E to 16.75E longitude and 45.25N to 49.75N latitude.<br><br>Non-commercial use is allowed conditional on proper citation of the data source, ALDIS, and the accompanying manuscript <em>Ehrensperger, G., Simon, T., Mayr, G. J., and Hell, T.: Identifying lightning processes in ERA5 soundings with deep learning, Geosci. Model Dev., 18, 1141–1153, <a href="https://doi.org/10.5194/gmd-18-1141-2025">https://doi.org/10.5194/gmd-18-1141-2025</a>, 2025</em>.</p>
DCv2, PCA and k-means on ERA5 covering Europe (1964-2023)
<p>Contains:</p> <ul> <li>DCv2 cluster assignments, feature space embeddings of centroids and samples (12:00 UTC of ERA5) and their respective distances to the cluster centroids.</li> <li>DCv2 time series labels for `k=14`.</li> <li>PCA time series labels for `k=30`.</li> <li>GWL time series labels.</li> <li>PCA for six PCs and the respective k-means clustering result.</li> <li>Performance benchmarking results.</li> </ul>
ERA5 data for air density calculations in WAsP
<div>ERA5 data for air density calculations in WAsP</div>
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
<p>A global long-term tropical cyclone (TC) size and intensity reconstruction dataset is generated, covering a time period from 1959 to 2022, with a 3-hour temporal resolution. The machine learning model was established by taking ERA5-derived 10 m azimuthal mean azimuthal wind profiles in six basins for which TCs were generated as input, while the maximum sustained wind speed and radius of maximum wind from the International Best Track Archive for Climate Stewardship (IBTrACS) was used as the learning target. An empirical wind–pressure relationship and six wind profile models were employed to estimate the minimum central pressure and outer sizes (radial distances from the cyclone center to locations where sustained wind speeds of 34, 50 and 64 knots are observed on surface) of the TCs, respectively. Compared to the IBTrACS dataset, the reconsturction dataset contains approximately 3–4 times more data points per characteristic.</p> <p>Over all, this dataset is in terms of both coverage and good accuracy.</p>
Snow-off and snow-on days for Arctic/boreal region from ERA5, 1959-2024
<p>The variables are extracted from the hourly ERA5 data, variable SD ("snow depth" as water equivalent in m) as follows:</p> <p><strong>lastsnowJDay:</strong> The Julian Day of the last date during the Jan-Jun period of a given year with > 0 snow.</p> <p><strong>lastsnowstreakJDay:</strong> The Julian Day of the last date during the Jan-Jun period of a given year that concludes an at least 7-day streak of consecutive days with > 0 snow.</p> <p><strong>cumulsnowJanJun:</strong> Number of days during Jan-Jun period of a given year with > 0 snow.</p> <p><strong>firstsnowJDay:</strong> The Julian Day of the first date during the Jul-Dec period of a given year with > 0 snow.</p> <p><strong>firstsnowstreakJDay:</strong> The Julian Day of the first date during the Jul-Dec period of a given year that starts an at least 7 day streak of > 0 snow.</p> <p><strong>cumulsnowJulDec:</strong> Number of days during Jul-Dec period of a given year with > 0 snow.</p> <p>Time range: 1959-2024</p>
Sea Surface Temperature Graphs from ERA5
<p>The sea surface temperature graphs were generated from the ERA5 reanalysis product and used in the paper: Graph-Based Deep Learning for Sea Surface Temperature Forecasts, which was accepted at the Tackling Climate Change with Machine Learning Workshop at ICLR 2023.</p>
WBGT Daily Maximum, Mean, Minimum 1979-2021 (Brimicombe WBGT method, from hourly ERA5 data)
<p>Wet Bulb Globe Temperature, or WBGT, calculated using the method of Brimicombe (GeoHealth: https://doi.org/10.1029/2022GH000701). Using Python Xarray, daily statistics (mean, max, min) from hourly WBGT data were saved. WBGT calculated from hourly ERA5 data (near-surface, single level variables: t2m, d2m, 10m u+v wind vectors, mrt) Jan 1 1979 to Dec 31 2021. Daily statistics are saved in Netcdf (nc) files in annual slices, then annual files are saved in ~5 to 20 year groups within the zip files.</p> <p>Methodology from Brimicombe et al., 2023 (GeoHealth: https://doi.org/10.1029/2022GH000701), using the Thermofeel Python package shared by the ECMWF team on on <a href="https://https://github.com/ecmwf-projects/thermofeel/blob/master/thermofeel/thermofeel.py">Github</a>.</p>
Validation of C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/80beb102-f5f4-4b1a-94f4-57008df9e322. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/aaf66b58-ce30-4754-9349-8c48dcf81595. Produced on QA4SM (https://qa4sm.eu)
ERA5 daily meteorological data (Puget Sound) for weather system identification
<p>This dataset includes the ERA5-based daily meteorological data over the US Puget Sound region. The data includes the following meteorological variables at 850hPa pressure level: temperature (T), relative humidity (RH), horizontal wind vector (U and V), vertical wind (W), and geopotential height (Z).</p> <p>The scripts here are used to establish the weather system classification model as in Chen et al. (submitted). More details, including the complete scripts for analysis/plotting will be updated here after the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, N. Sun, Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes. (submitted)</p>
ERA5 Cutout of the Western United States (y.2019)
<p>2019 ERA5 Cutout for Western United States for use in the pypsa-usa workflow</p> <p> </p> <p>Created Using the Atlite tool: Hofmann et al., (2021). atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series. Journal of Open Source Software, 6(62), 3294, https://doi.org/10.21105/joss.03294</p>
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