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15 results for “ERA5 Reanalysis”
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> </p>
Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"
<p>These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing.</p> <p>The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data.</p> <p>Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.</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>
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>
MED-GOLD Indicators for the Olives/olive oil pilot service over Iberian Peninsula from ERA5 Reanalysis 1979-2020
<p>Indicators of interest for the Olives/olive oil sector over the Iberian Peninsula using ERA5 Reanalysis data for the period 1979 to 2020:</p> <ol> <li>SPRTX Mean maximum temperature from Apr to May</li> <li>SPR32 Number of spring heat days with Tmax above 32°C during spring months (from 21 April to 21 June)</li> <li>SU36 Number of summer heat days with Tmax above 36°C during summer months (from 21 June to 21 September)</li> <li>SU40 Number of summer heat days with Tmax above 40°C during summer months (from 21 June to 21 September)</li> <li>WINRR Total winter (Oct-May) precipitation</li> </ol> <p>Datasets computed by ENEA, in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467</p>
MED-GOLD Indicators for the Wine pilot service over Iberian Peninsula from ERA5 Reanalysis 1979-2020
<p>Indicators of interest for the Wine sector over the Iberian Peninsula using ERA5 Reanalysis data for the period 1979 to 2020:</p> <ol> <li>Growing Season Temperature (GST) [Temp averaged between April and October]</li> <li>Spring Rain (SprR) [ Precip cumulated between 21 apr and 21 Jun],</li> <li>Harvest Rain (HarvestR) [ Precip cumulated between 21 aug and 21 Oct]</li> <li> (SU35) -number of days with temperature higher than 35°C [for April ot October],</li> <li>Warm Spell Duration Index (WSDI) [days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for April to Oct]</li> </ol> <p>Wine risk indicators, implemented specifically for the MED-GOLD Wine pilot service by ENEA and SOGRAPE VINHOS S.A.:</p> <ol> <li>Sanitary Risk Index= offgts*offsp*(percentile(SprR))+offhart*percentile(HarvestR)+(100-percentile(GST); with offhart=1.; offsp=1; offgts=1.; if percentile (SprR)>= 60; offsp=1.5; if percentile (GST)<= 40; offhart=1.5; if percentile (GST)>=70; offgts=1.5; where the percentile are here computed starting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> <li>Heat Risk Index = percentile(GST)+percentile(SU35_AMJJASO) + percentile(WSDI_AMJJASO),where the percentile are here computed srarting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> </ol> <p>Datasets computed by ENEA, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467</p>
A High Resolution (3km) Reanalysis Database for Mediterranean Coastal Winds Downscaled from ERA5, using the WRF Model
<p>A high resolution (3km) reanalysis database of Mediterranean coastal winds was constructed to support a research on potential sailing mobility in Antiquity. The database was created by downscaling the ERA5 reanalysis database using the WRF numerical prediction model.</p> <p>A detailed description of the reanalysis database is provided in the attached PDF file. The database format is GRIB version 2 and the total volume of the data files is 435GB. The GRIB files are hosted at <a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a> as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.</p> <p><strong>Link to the GRIB data files and index map:</strong></p> <p><strong><a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a></strong></p> <p><strong>Acknowledgements:</strong></p> <p>The Data Science Research Center (DSRC) at Haifa University kindly provided funding towards the creation of this data set.</p>
Detection of Atmospheric Rivers in the Northern Hemisphere based on ERA5 reanalysis data and the IPART algorithm, 1979-2020
<p># 1. Overview</p> <p>This is a catalogue of atmospheric river (AR) detections over the Northern Hemisphere, based on 6-hourly ERA5 reanalysis dataset and the Image-Processing based Atmospheric River Tracking (IPART) algorithm.</p> <p>Time domain of the data:</p> <ul> <li>From 1979-Jan-01 to 2020-Dec-31</li> <li>Temporal resolution is 6-hourly</li> </ul> <p>Spatial domain of the data:</p> <ul> <li>Northern Hemisphere, land and ocean</li> <li>Spatial resolution is 0.25 * 0.25 degrees latitude/longitude</li> </ul> <p>Input data from ERA5 include:</p> <ul> <li>Vertical integral of northward water vapour flux, in kg/(m s).</li> <li>Vertical integral of eastward water vapour flux, in kg/(m s).</li> </ul> <p>Data in the Northern Hemisphere domain (0 - 90 N), at 0.25 * 0.25 degrees latitude/longitude resolution are obtained from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.</p> <p>Version v3.0.8 of the IPART Python module used for detection and tracking of atmospheric rivers is preserved at <strong>10.5281/zenodo.4164826</strong>, available via Creative Commons Attribution 4.0 International license and developed openly at the Github repository https://github.com/ihesp/IPART.</p> <p># 2. File naming convention</p> <p>The data files are named using the following convention:</p> <p>ar_YYYYMM.nc</p> <p>where:</p> <ul> <li>YYYY: 4-digit year number</li> <li>MM: 2-digit month number</li> </ul> <p>E.g. `ar_199902.nc` means detections in Feb of 1999.</p> <p>Months are calendar months, including Feb-29th in leap-years.</p> <p># 3. Data format</p> <p>Data are saved in netCDF format.</p> <p>Each data file contains one 3-dimensional array, of a shape `(t, 360, 1440)`, where:</p> <ul> <li>`t`: length of the time dimension. Since data are 6-hourly, t equals 4 * num_of_days_in_month.</li> <li>`360`: latitude dimension, from 0 - 90N, with a 0.25-degree step.</li> <li>`1440`: longitude dimension, from 80 - 440 E (shifted eastward by 80 degrees to put both the Pacific and Atlantic oceans within the domain), with a 0.25-degree step.</li> </ul> <p>Each time slice of the data contains maps of the Northern Hemisphere, with integer values in grid cells. Possible values are:</p> <ul> <li>0: meaning no AR is detected in the grid cell.</li> <li>1, 2, ... ,n: integer labels, each corresponding to the region of an AR entity.</li> </ul> <p># 4. Important parameters in the IPART algorithm</p> <p>Here are the most important parameters used when detecting ARs from ERA5 data using the IPART python module:</p> <ul> <li> THR filtering kernel: `[16, 13, 13]`. `16` means 16 time slices, or equivalently 4 days given 6-hourly input data. `13` means 13 grid cells, or equivalently ~325 km, given 0.25 degrees latitude/longitude input data. Note that both of these temporal and spacial lengths are half of the sizes of the filtering kernel.</li> <li> minimum area: `50 * 1e4`, in km^2, minimum size of AR region candidates.</li> <li> maximum area: `1800 * 1e4`, in km^2, maximum size of AR region candidates.</li> <li> minimum L/W: `2.0`, minimum length/width ratio of AR region candiates.</li> <li> minimum length: `2000`, in km, minimum length of AR region candidates.</li> <li> minimum latitude: `20`, minimum latitude of the geometrical centroid of an AR region candidate.</li> <li> maximum latitude: `80`, maximum latitude of the geometrical centroid of an AR region candidate.</li> </ul> <p>For more details regarding these parameters, as well as the IPART algorithm, please refer to our published works:</p> <ul> <li>Xu, G., Ma, X., Chang, P., and Wang, L.: Image-processing-based atmospheric river tracking method version 1 (IPART-1), Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, 2020.</li> </ul> <p>Or the Github repository that houses the IPART module:</p> <ul> <li>https://github.com/ihesp/IPART</li> </ul>
A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of TRMM PR and VIRS
<p>This new merged gridded dataset (M-1B01-2A25-GD) contains precipitation, clouds and atmospheric parameters with 0.25° spatial resolution.</p> <p>It is produced by merging TRMM PR and VIRS measurements with the ERA5 reanalysis dataset at the same spatiotemporal resolution between 40° S and 40° N. The near-surface rain rate, profiles of rain rate and precipitation reflectivity factor, visible and infrared signals and atmospheric parameters (temperature, pressure, geopotential height, specific humidity, divergence, vertical velocity and so on) can be obtained in the dataset. The statistical results indicate that the merging and gridding will not dramatically distort the original data and the new dataset can be used to study the characteristics and distribution of the precipitation and clouds systems.</p>
Sample ERA5 Climate Reanalysis Data for UW Geospatial Data Analysis Course
<p>Used for Module 09: https://uwgda-jupyterbook.readthedocs.io/en/latest/modules/09_NDarrays_xarray_ERA5/</p> <p>Generated using Copernicus Climate Change Service information [2022]<br> Original license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</p>
Transformed Eulerian mean data from the ERA5 reanalysis (daily means)
<p>This dataset provides <strong>daily</strong> and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). <strong>Monthly</strong> means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081721).</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find. Please cite the reference publication when using this dataset.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> <span>\(\cos(\phi)\)</span></em>, where <span>\(\phi\)</span> is latitude, diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
Transformed Eulerian mean data from the ERA5 reanalysis (monthly means)
<p>This dataset provides <strong>monthly</strong> and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). <strong>Daily</strong> means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081436).</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> <span>\(\cos(\phi)\)</span></em>, where <span>\(\phi\)</span> is latitude, diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
MAR-ERA5 reanalysis of the Arctic land ice surface mass balance between 1950 and 2020
<p>This archive provides monthly outputs of the surface mass balance variables over the Arctic land ice, as modeled by MAR forced by ERA5.</p><p>These outputs were produced as part of the publication "Maure, D., Kittel,C., Lambin, C., Delhasse, A. and Fettweis, X.: "Spatially heterogeneous effect of climate warming<br>on the Arctic land ice", The Cryosphere, accepted. (2023). The data comes from the 6km domains presented in Fig.1 of the study, reinterpolated to a single Pan-Arctic 6km grid.</p><p> </p><p>Contact: Damien Maure </p><p>damien.maure@uliege.be</p><p> </p><p>The MAR code is available at https://gitlab.com/Mar-Group/MARv3. The version used to generate this dataset is tagged as v3.11.5.</p><p>About the dataset:<br>It contains one file per year</p><p>MAR_arctic_ERA5_v1_<strong>*year*</strong>.nc</p><p>MAR311</p><p>SMB: surface mass balance<br>SF: snowfall<br>RF: rainfall<br>RU: runoff<br>ME: melt<br>SU: sublimation - deposition (positive values indicates mass losses through sublimation)<br>(units: mm we day)</p><p> </p><p> </p>
Precipitation identifiers for meteorological features combining global GPM-IMERG retrievals and ERA5 reanalysis
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