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310 results for “era5”
ERA5 overviews complementing temperature measurements of ground-based Rayleigh lidars for the investigation of gravity waves generated by moving sources
<p>ERA5 overviews to associate stratospheric gravity waves in temperature measurements from vertically staring (zenith-pointing) ground-based Rayleigh lidars with atmospheric processes. Animations are for a virtual lidar location over the Southern Ocean during research flight RF25 of the DEEPWAVE campaign (July 17 to 19, 2014) and for the location of the COmpact Rayleigh Autonomous Lidar (CORAL) in the lee of the southern Andes. Here, the first overview is for the CORAL measurement from June 22 to 23, 2018. The second one is for the nightly measurements between August 7 and 9, 2020.</p> <p>(a) and (b) emulate the measurement of a vertically staring ground-based lidar and show temperature perturbations after subtracting a temporal running mean of 12h (a) and the mean absolute temperature profile (b). Panels (c) and (d) are vertical sections of stratospheric 𝑇′ along sectors of the latitude circle (c) and meridian (d) of the virtual lidar location. (e) and (f) are corresponding vertical sections of thermal stability 𝑁2 (10−4 s−2, color-coded), potential temperature (K, thin grey lines), and potential vorticity (1, 2, 4 PVU: black, 2 PVU: green). Thin black lines in the vertical sections are zonal (d, f) and meridional (c, e) wind components (solid: positive, dashed: negative). Panel (g) is a horizontal section of the height of the 2 PVU surface (km, color-coded), geopotential height (m, solid lines) and wind barbs at the 850 hPa level. The black vertical line in (a) marks the time for (c)-(g) and dashed lines in (c)-(g) highlight the location of the virtual lidar and profiles in (a) and (b).</p> <p>The provided NETCDF files contain the corresponding CORAL temperature measurements for the two periods with CORAL measurements in 2018 and 2020.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2023
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2023.</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>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
ASCAT-ERA5 and ASCAT-MERRA-2 Antarctic Peninsula Daily Surface Meltwater Production (2007-2022)
<p>Estimates of daily surface meltwater production across the Antarctic Peninsula over 2007-2022. These data were generated using a random forest model combining enhanced resolution ASCAT C-Band radar backscatter, ASCAT-detected surface melt presence (https://zenodo.org/record/7995998), and ERA5 or MERRA-2 reanalysis sensible and latent heat fluxes, downward shortwave and longwave radiation, and 2-m temperature. Initial model training was undertaken using meteorological observations and surface melt estimates from surface energy balance modeling at several Larsen ice shelf automated weather stations (Jakobs et al., 2020), before scaling up to the Antarctic Peninsula region using ERA5 or MERRA-2. <br><br>Note that the ASCAT-ERA5 melt estimates here are updated to v2 using a revised random forest melt prediction model. <br>The ASCAT-MERRA-2 melt estimates here are the initial release (v1). </p> <p>This record will be updated upon publication of a forthcoming manuscript. </p> <p>Please reach out to Luke Trusel with questions! </p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: BOLAM precipitation daily data for the period 1979-2019
<p>This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";</p> <p>Grid spacing = "7 km";</p> <p>Grid = "890x482"</p>
GTSM-ERA5-E dataset - Data underlying the paper "Global dataset of storm surges and extreme sea levels for 1950-2024 based on the ERA5 climate reanalysis"
<p>Extreme sea levels, generated by storm surges and high tides, have the potential to cause coastal flooding and erosion. Global datasets are instrumental for mapping of extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing the statistics of water levels based on a global hydrodynamic model (GTSMv3.0) covering the period 1950-2024. This is an extension of a previously published dataset for 1979-2018 <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/full" target="_blank" rel="noopener">(Muis et al. 2020)</a>. The timeseries (10-min, hourly mean and daily maxima) are available via the Climate Data Store of ECMWF at DOI: 10.24381/cds.a6d42d60. Using this extended ERA5 dataset, we calculate percentiles and estimate extreme water levels for various return periods globally. The percentiles dataset includes the 1, 5, 10, 25, 50, 75, 90, 95 and 99th percentiles. The extreme water levels include return values for 1, 2, 5, 10, 25, 50, 75 and 100 years, and they are estimated using POT-GPD method applied with a threshold of 99th percentile of the timeseries and using a 72-hour window for declustering peak events, and MLE method for fitting the GPD parameters. The parameters (shape, scale and location) are also supplied with this dataset.</p> <p>Validation of the underlying timeseries and the statistical values shows that there is a good agreement between observed and modelled sea levels, with the level of agreement being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of extremes, often resulting in smaller uncertainties than its 40-year precursor. The present dataset can be used in global assessments of flood risk, climate variability and climate changes.</p> <p>Global modelling of water levels and extreme value analysis are associated with a number of uncertainties and limitations, that are particularly important to consider when conducting local assessments. Please refer to the Usage Notes in the corresponding manuscript (Aleksandrova et al. 2025, paper currently under review) for an overview of limitations.</p>
Country-ocean-moisture-flows-reconciled-with-ERA5-reanalysis obtained processing Lagrangian moisture connections
<p>The dataset "Reconciled global atmospheric moisture flows between countries/oceans and subcontinents" presents tracked volumes of precipitation and evaporation reconciled with reanalysis data, closing the annual hydrological balance, and provides robust estimates of terrestrial moisture recycling and net moisture flows to support global water governance analysis.</p> <p>This repository is supplement to a study by De Petrillo & Fahrländer et al. (2025), which describes the development of the reconciliation framework, includes a perfromance analysis of the method and shows an exemplary case study on the published data. </p> <p>The atmospheric moisture flows are sourced from the UTrack atmospheric moisture flow dataset by Tuinenburg et al. (2020a) (dataset access: Tuinenburg et al., 2020b) and reconciled with ERA5 precipitation and evaporation data (Hersbach et al., 2020) on the mean annual basis in the period 2008-2017, by means of a post-processing framework, based on the Iterative Proportional Fitting (IPF) algorithm.</p> <p>NOTE: The final dataset is available in form of bilateral matrices (country/ocean and subcontinent/ocean) and in form of direct flows (flow edges). Supporting material to read the dataset is in the folder "List" . Processed ERA5 data (where the precipitation-evaporation annual balance is met) and input data to generate the figures are also available.</p> <p>References:</p> <p>De Petrillo, E., Fahrländer, S., Tuninetti, M., Andersen, L.S., Monaco, L., Ridolfi, L., Laio, F. (2025). Reconciling tracked atmospheric moisture flows to close the global freshwater cycle.<em> </em><em>Commun Earth Environ <strong>6</strong>, 347 (2025). </em><a href="https://doi.org/10.1038/s43247-025-02289-y">https://doi.org/10.1038/s43247-025-02289-y</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020a). High-resolution global atmospheric moisture connections from evaporation to precipitation. <em>Earth System Science Data</em>, <em>12</em>(4), 3177–3188. <a href="https://doi.org/10.5194/essd-12-3177-2020">https://doi.org/10.5194/essd-12-3177-2020</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., Staal, A. (2020b): Global evaporation to precipitation flows obtained with Lagrangian atmospheric moisture tracking. PANGAEA, <a href="https://github.com/ObbeTuinenburg/UTrack_global_database">https://doi.pangaea.de/10.1594/PANGAEA.912710</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>146</em>(730), 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p> <p> </p>
Indian Summer monsoon Low-Pressure Systems Dataset from ERA5
<p>This dataset contains downstream and in situ LPS genesis dates and their location over the Bay of Bengal classified using the algorithm developed by Srujan et al. (2021) from 1979-2017 using the ERA5 reanalysis dataset. The LPS are tracked from mean sea level pressure using the algorithm developed by Praveen et al. (2015).</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p> <p>Srujan, K. S. S. S., Sandeep, S., & Suhas, E. (2021). <strong>Downstream and In Situ Genesis of Monsoon Low‐Pressure Systems in Climate Models</strong>. <em>Earth and Space Science</em>, <em>8</em>(9), e2021EA001741.</p>
Arctic maritime cyclone distribution and trends in the ERA5
<p>This dataset supplements our paper published in the AMS Journal of Applied Meteorology and Climatology (JAMC) under the same title. The repository includes the cyclone tracks, the HST case study, and the delineation for different Arctic sea sections. For any questions, please contact Zihan Chen (via <a href="mailto:zihan_chen@alumni.brown.edu">zihan_chen@alumni.brown.edu</a>).</p>
Fires_ERA5_Reanalysis_Data
<p>ERA5 reanalysis data obtained for each fire, hourly and at different pressure levels (37) from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The files are in netCDF format, and the variables requested: temperature, relative humidity, U-component of wind, and V-component of wind.</p> <p>Source: 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. (2018): ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS).</p>
A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)
<p>Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018).</p> <p>Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain.</p> <p>Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.</p>
ARtracks - a Global Atmospheric River Catalogue Based on ERA5 and IPART
<p>The <strong>ARtracks Atmospheric River Catalogue</strong> is based on the ERA5 climate reanalysis dataset, specifically the output parameters "vertical integral of east-/northward water vapour flux". Most of the processing relies on<br>IPART (Image-Processing based Atmospheric River (AR) Tracking, https://github.com/ihesp/IPART), a Python package for automated AR detection, axis finding and AR tracking. The catalogue is provided as a pickled pandas.DataFrame as well as a CSV file.</p> <p>For detailed information, please see <a href="https://github.com/dominiktraxl/artracks">https://github.com/dominiktraxl/artracks</a>.</p> <p>The ARtracks catalogue covers the years from 1979 to the end of the year 2019.</p>
Validation of ERA5 v20190613 vs CGLS SWI 1km V1.0
QA4SM validation of soil moisture data: ERA5 v20190613 vs CGLS SWI 1km V1.0. URL: https://qa4sm.eu/ui/validation-result/f5b6c916-ebea-4762-87f9-677ac1263f35. Produced on QA4SM (https://qa4sm.eu)
European winter windstorm days from ERA5 and CMIP6 models
European winter storm hazard days generated from ERA5 (1980–2010) and CMIP6 models (1980–2010 and 2070–2100) for the historical and Shared Socioeconomic Pathways SSP126, SSP245, SSP370, and SSP585 experiments, based on Severino et al., 2023.<br> For each storm day, the specific ensemble member of the climate model which has been used to model the data can be read in the "event_name" property (e.g. 'event_name': 'IPSL-CM6A-LR_ssp585_mem0' corresponds to a storm day which has been generated by the ensemble member 0 of the climate model IPSL-CM6A-LR for the ssp585 experiment).<br> <br> <a href="https://doi.org/10.5194/egusphere-2023-205">Severino, L. G., Kropf, C. M., Afargan-Gerstman, H., Fairless, C., de Vries, A. J., Domeisen, D. I. V., and Bresch, D. N.: Projections and uncertainties of future winter windstorm damage in Europe, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-205, 2023.</a>
ERA5-Land selected indicators daily aggregates for Africa, 1991
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1991.</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 data to run example tutorials in RASCAL
<p>This is data from 2000 to 2023 to run some examples of reconstructions with the RASCAL model. It contains the geopotential height at 925hPa, and the temperature at 2m</p>
Country averages of Copernicus ERA5 hourly meteorological variables
<p><strong>Note: a new <a href="https://zenodo.org/record/2650191#.XMCUEBMzY3E">time-series dataset from ERA5</a> has been published — this one won't be updated/maintained anymore</strong></p> <p>Country averages of meteorological variables generated using the R routines available in the package <a href="https://github.com/matteodefelice/panas">panas</a> based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">Copernicus Climate Change ERA5 reanalyses</a>. The time-series are at hourly resolution and the included variables are:</p> <ul> <li>2-meter temperature (t2m),</li> <li>snow depth (snow_depth),</li> <li>mean sea-level pressure (mslp),</li> <li>runoff,</li> <li>surface solar radiation (ssrd),</li> <li>surface solar radiation with clear-sky (ssrdc),</li> <li>temperature at 850hPa (t850),</li> <li>total precipitation (total_prec),</li> <li>zonal (west-east direction) wind speed at 10m (u10) and 100m (u100),</li> <li>meridional (north-sud) wind speed at 10m (v10) and 100m (v100),</li> <li>dew point temperature (dew)</li> </ul> <p>The original gridded data has been averaged considered the national borders of the following countries (European 2-letter country codes are used, i.e. ISO 3166 alpha-2 codes with the exception of GB->UK and GR->EL): AL, AT, BA, BE, BG, BY, CH, CY, CZ, DE, DK, DZ, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MD, ME, MK, NL, NO, PL, PT, RO, RS, SE, SI, SK, UA, UK.</p> <p>The unit measures here used are listed in the official page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/era5-hourly-data-on-single-levels-from-2000-to-2017?tab=overview</p> <p>The script used to generate the files is available on github <a href="https://github.com/matteodefelice/era5-country-averages">here</a></p> <p> </p> <p> </p>
ECMWF ERA5 Monthly surface air temperature anomalies (Celcius) relative to 1981-2010
<p><em>Monthly global-mean and European-mean surface air temperature anomalies relative to 1981-2010, from January 1979 to August 2019. Data source: ERA5. Credit: Copernicus Climate Change Service/ECMWF.</em></p> <p>See <a href="https://climate.copernicus.eu/surface-air-temperature-august-2019">https://climate.copernicus.eu/surface-air-temperature-august-2019</a> for more information.</p> <p><br> </p>
Global FLEXPART-ERA5 simulations using 30 million atmospheric parcels since 1980
<h2><strong>Abstract</strong></h2> <p>This database compiles the outputs of the global experiment performed with the Lagrangian particle dispersion model FLEXPART since 1980. The experiment was conducted using the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and homogeneously dividing the atmosphere into 30 million particles. The database can be used to investigate global moisture and heat transport and to establish sink-source relationships.</p> <h2><strong>Input data</strong></h2> <p>The data employed for FLEXPART running was the ERA5 reanalysis dataset from the ECMWF (Hersbach et al., 2020). To feed the model, the input data was downloaded and pre-processed by using the software Flex_extract v7.1 (Tipka et al., 2020).</p> <p>The original available ERA5 resolution is 0.1-degree and 1-hour. For this experiment, ERA5 input data was retrieved for the global area (90ᵒS to 90ᵒN and 180ᵒW to 180ᵒE) at a 0.5-degree horizontal resolution for 137 level from the surface to 1 hPa and a 3-hour temporal resolution (00, 03, 06, 09, 12, 15,18 and 21 UTC).</p> <p>The data is stored in individual GRIB files for each time step, following the name criteria "EAYYMMDDHH". The size of each file is approximately 530 MB. The variables included in each file are: temperature, specific humidity, u- and v-wind components, Eta-coordinate vertical velocity, divergence, specific cloud liquid water content, specific cloud ice water content, and the logarithm of surface pressure on model levels; and 2m temperature an dew-point temperature, 10m u and v wind component, geopotential, land-sea mask, mean sea level pressure, snow depth, the standard deviation of orography, surface pressure, total cloud cover, convective precipitation, large-scale precipitation, surface sensitive heat flux, eastward and northward turbulent surface stress and surface net solar radiation at the surface level.</p> <h2><strong>Software and running</strong></h2> <p>The software used for the simulations is the Lagrangrian particle dispersion model FLEXPART on version 10.4 (Pisso et al., 2019). The software is configured for a global experiment, and the simulations were obtained from 1980 to the present with a temporal resolution of 3-h. For the experiment, 30 million particles were homogeneously distributed on the global area, and their trajectories were followed according to the model configuration specified in the COMMAND and RELEASES files. The complete period is distributed in individual annual experiments, with each annual experiment obtained continuously running the model from October of the previous year to December of that year.</p> <h2><strong>Outputs characteristics</strong></h2> <p>The outputs were stored in individual GRIB files for each time step, with the file name following the naming convention "partposit_YYYYMMDDHH". Each file has a size of 1,76 GB, and the total size of the annual experiment is 6 TB. Each file contains information about each particle of the experiment: the particle identification number (particle ID), the particle's position (latitude, longitude, and altitude), topographic height, potential vorticity, specific humidity, air density, atmospheric boundary layer height, and temperature. The file corresponding to the 1st January 2023 at 00UTC is provided in this repository as an example. Due to the size of each file, the complete dataset is accessible by personal contact (see <em>Data Access</em> section). </p> <h2><strong>Post-process and applications</strong></h2> <p>The dataset presented here allows for the analysis of moisture and heat transport in the atmosphere for any region of the world up to 3-h temporal resolution and different horizontal resolutions. The transport may be established between sources and sinks, both in a forward or backward tracking in time. Currently, two open-source post-processing options developed within the EPhyslab-UVigo group are available for the analysis of these data: TROVA (Fernadez-Alvarez et al., 2022) and LATTIN (Perez-Alarcón et al., 2024) with different moisture tracking calculation options, and the latter including tools for heat transport analysis. Both options allow different methodologies (those most widely used) for the moisture transport analysis. The studies can be configured for any region of the planet, specifying it by a NetCDF 2-D mask, and the moisture transport can be set for different time periods (from 1 to 15 days, being from 8 to 10 days the periods most commonly applied according to the mean residence time of water vapor in the atmosphere). For further discussion on the residence time of water vapor in the atmosphere and its application for Lagrangian studies see Gimeno et al. (2021) and Nieto and Gimeno (2019).</p> <h2><strong>Example of application</strong></h2> <p>J. C. Fernández-Álvarez, M. Vázquez, A. Pérez-Alarcón, R. Nieto, L. Gimeno (2023) Comparison of moisture sources and sinks estimated with different versions of FLEXPART and FLEXPART-WRF models forced with ECMWF reanalysis data, Journal of Hydrometeorology, doi: 10.1175/JHM-D-22-0018.1.</p> <p>A. Pérez-Alarcón, R. Sorí, M. Stojanovic, M. Vázquez, R.M. Trigo, R. Nieto, L. Gimeno (2024) Assessing the Increasing Frequency of Heat Waves in Cuba and Contributing Mechanisms, Earth Systems and Environment, DOI: 10.1007/s41748-024-00443-8</p> <h2><strong>Validation</strong></h2> <p>The moisture transport analysis provided by this dataset was validated by Fernández-Alvarez et al. (2023) through an in-depth comparison with different versions of the model, horizontal resolutions and input data, including the ERA-Interim reanalysis from the ECMWF, which has been widely used for this purpose over the past decades.</p> <h2><strong>Data Access</strong></h2> <p>Data access is available by contacting the EPhysLab group via: rnieto[at]uvigo.gal or l.gimeno[at]uvigo.gal</p>
ERA5 atmospheric stability and Geostrophic wind shear for usage in WAsP
<p>The dataset "ERA5-meso.nc" is obtained by loading the variables needed for calculating the temperature scale from hourly ERA5 files. These files are available in grib format that have been obtained from the Copernicus Data Store (CDS). They are opened using xarray and cfgrib and processed using the functions stability_histogram from the python package PyWAsP. Because a conditional mean based on the 50% highest wind speeds must be calculated, all values are binned according to wind speed at 100 m and this histogram is then used to calculate the mean and root-mean-square of the temperature scale. The boundary layer height scale is calculated in a similar fashion. For more documentation see the accompanying paper. A validation of the WAsP model using these data is available in the references.</p> <p>The file "ERA5-baro.nc" contains the geostrophic wind shear. The mean magnitude and direction is obtained sector-wise in similar fashion as described above. The geostrophic wind shear can be calculated from the pressure level geopotential height. The way to do this is described here:</p> <p><a href="https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w" target="_blank" rel="noopener">https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w</a></p> <p>These data are for estimating atmospheric stability conditions, if you are looking for data to estimate air density, please refer to the item "ERA5 data for air density calculations in WAsP" (related materials item 5). The methods for this are described in related materials item 7.</p> <p>v1-v2: Version corresponding to paper before review (related materials 3), do not use these.</p> <p>v3: Final version that corresponds to the published version of the paper (related materials 6):<br>https://doi.org/10.1007/s10546-023-00803-3<br>This is slightly different then the first version due to Eq. 9</p> <p>v4: Updates to load the files using PyWAsP versions specifically suited for use in pywasp with the variable names adopted in PyWAsP. For ERA5-baro.nc NaNs are filled with 0.0, i.e. assuming a barotropic atmosphere.</p> <p>Mirror of: https://data.dtu.dk/articles/dataset/ERA5_atmospheric_stability_for_usage_in_WAsP_12_8/19576042</p>
ScienceDex guides
Understand access before you commit
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.