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310 results for “Era5”
Low-pressure system (LPS) tracks in ERA5 over South Asia (1979-2025) v4.1
<h1>Low-Pressure System (LPS) tracks over South Asia — v4.1</h1> <p><strong>Region:</strong> South Asia (India, Sri Lanka, Pakistan; tracks may extend beyond these borders)<br><strong>Data source:</strong> 3-hourly ERA5 reanalysis<br><strong>Method:</strong> Automated LoG tracking following Hunt & Fletcher (2018, doi:10.1007/s00382-019-04744-x) and Hunt et al. (2016, doi:10.1175/MWR-D-15-0138.1)<br><br></p> <h2>Coverage</h2> <ul> <li><strong>Temporal span:</strong> <strong>1940-01-01</strong> to <strong>2025-08-31</strong> (inclusive)</li> <li><strong>Frame epoch:</strong> <code>frame</code> is hours since <strong>1940-01-01 00:00 UTC</strong></li> <li><strong>Timestep:</strong> hourly detections</li> </ul> <h3>Variables (columns)</h3> <ul> <li><code>x</code> — longitude (deg E)</li> <li><code>y</code> — latitude (deg N)</li> <li><code>frame</code> — hours since 1940-01-01 00:00 UTC (int)</li> <li><code>year</code>, <code>month</code>, <code>day</code>, <code>hour</code> — UTC components of detection time</li> <li><code>max_vort</code> — maximum relative vorticity within ~4° (~400–450 km) of the LPS centre from the <strong>850–700 hPa mean</strong>; units <strong>10⁻⁵ s⁻¹</strong></li> <li><code>mean_vort_850</code>, <code>mean_vort_700</code>, <code>mean_vort_500</code> — mean relative vorticity within ~4° at each level; units <strong>10⁻⁵ s⁻¹</strong></li> <li><code>precip_1hr</code> — mean precipitation rate within ~8° (~800–900 km) over the hour; <strong>mm h⁻¹</strong> </li> <li><code>precip_24hr</code> — mean of the <strong>calendar-day</strong> 24-h precipitation sum within ~8°; <strong>mm day⁻¹</strong></li> <li><code>mean_wind</code>, <code>max_wind</code> — 10 m wind speed within ~4° (area-mean and maximum); <strong>m s⁻¹</strong></li> <li><code>min_mslp</code> — minimum mean sea-level pressure within ~4°; <strong>hPa</strong></li> <li><code>mslp_contours</code> — count of <strong>closed</strong> 2 hPa isobars that enclose the LPS centre (dimensionless)</li> <li><code>over_land</code> — 1 if the centre is over land (Natural Earth 1:50 m), else 0</li> <li><code>category</code> — intensity class (IMD-style): 1=L, 2=D, 3=DD, 4=CS, 5=SCS, 6=VSCS, 7=SuCS</li> <li><code>track_id</code> — unique integer identifier for each track (all points with the same <code>track_id</code> belong to the same system)</li> </ul> <h3>Intensity category (<code>category</code>)</h3> <p>IMD-style classes, derived from the number of enclosing closed isobars and 10 m wind (over ocean) / combination with isobars (over land).<br>Mapping used in figures/legends:</p> <ul> <li><strong>1 = L</strong> (Low)</li> <li><strong>2 = D</strong> (Depression)</li> <li><strong>3 = DD</strong> (Deep Depression)</li> <li><strong>4 = CS</strong> (Cyclonic Storm)</li> <li><strong>5 = SCS</strong> (Severe Cyclonic Storm)</li> <li><strong>6 = VSCS</strong> (Very Severe Cyclonic Storm)</li> <li><strong>7 = SuCS</strong> (Super Cyclonic Storm)<br><br></li> </ul> <p><strong>Notes:</strong></p> <ul> <li>Over <strong>ocean</strong>, thresholds follow IMD (knots converted to m s⁻¹): 17, 28, 34, 48, 64, 119 kt breakpoints.</li> <li>Over <strong>land</strong>, the number of enclosing closed isobars (2 hPa spacing) informs classification together with wind.</li> </ul>
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/ead8ccde-9167-472f-959f-e57a77227f7a. Produced on QA4SM (https://qa4sm.eu)
2018-2020 Dataset [7/7] 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'
<p>This repository contains part 7/7 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". </p> <p>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 2018-2019. 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.</p> <p>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.</p>
Sample dataset 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'
<p>This repository contains a sample of the input data 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". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. 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.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</p>
Comparison Metrics for ERA5 against in situ observations in the Arctic
<p>The comparison metrics (R2, slope, RMSE, Pearson correlation coefficient) were produced from a comparison between the ERA5 reanalysis model and in situ observations from ground-based stations in the Arctic. The datasets accompany the manuscript "Comparison of selected surface level ERA5 variables against in situ observations in the Arctic" by Pernov et al. (2023). The Excel files are located in three sub-folders (All, Seasonal, and Temporal). Each variable has its own Excel file. The sub-folder "All" gives the metrics when using all available data from each station at a 1-hour temporal resolution. The sub-folder "Seasonal" gives the metrics when the comparison is made on a seasonal basis, with sub-folders indicating the season. The sub-folder "Temporal" gives the metrics when comparing different temporal resolutions, which are indicated in the sub-folders. </p> <p>For questions, please contact jakob.pernov@epfl.ch or julia.schmale@epfl.ch</p>
Sentinel-1 interferograms and weather-based tropospheric delays maps (ERA5, GACOS) for two volcanoes: Piton de la Fournaise and Merapi
<div> <p>InSAR Sentinel-1 dataset and weather-based models used in the paper "Benefits of GNSS local observations compared to global weather-based models for InSAR tropospheric corrections over tropical volcanoes: case studies of Piton de la Fournaise and Merapi" by Albino et al. (2024).</p> <p>The two zip files are associated with the two targets: REUNION_ISLAND and MERAPI. The directories "ERA5" and "GACOS" contain the GACOS and ERA5 tropospheric delay maps per epoch in geotiff format, respectively. The directory "INTERFEROGRAM" contains the unwrapped interferograms in geotiff format. The "AUX" directory contains the DEM and incidence angle in geotiff format.</p> </div>
Interpolated tropical high-resolution radiosonde and ERA5 nearest neighbor data
Open the record for dataset details and reuse information.
ERA5-Land and AEMET datasets for the region of Spain.
<p>This repository includes hourly wind speed data from the ERA5-Land dataset for the dates between 2010 and 2021 for the region of Spain. It also includes AEMET wind speed data already homogenized using the R package called Climatol</p>
Daily meteorological data extracted from ERA5 based on DHS-GIS (2000-2020)
<p>Daily meteorological dataset extracted from ERA5 based on DHS-GIS (2000-2020). It has three meteorological variables: relative humidity (1000hpa), daily 2m temperature, daily maximumu 2m temperature, daily minimum 2m temperature, and total precipitation. </p> <p> </p>
Weekly time series of total precipitation for Europe at 1 km resolution (2016 - 2020) derived from ERA5-Land data
<p> </p> <p> </p> <p><strong>Data have been moved to</strong>: <a href="https://doi.org/10.5281/zenodo.6559048">https://doi.org/10.5281/zenodo.6559048</a></p> <p> </p> <p> </p>
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