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datasets available to search
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Dataset results
6 results for “Precipitation Nowcasting”
Dataset for "GPTCast: a weather language model for precipitation nowcasting"
<p>Dataset for "<em><strong>GPTCast: a weather language model for precipitation nowcasting</strong></em>"</p> <ul> <li>Preprint: <a href="https://arxiv.org/abs/2407.02089">https://arxiv.org/abs/2407.02089</a></li> <li>Code: <a href="https://github.com/DSIP-FBK/GPTCast">https://github.com/DSIP-FBK/GPTCast</a></li> <li>Pretrained models: <a href="https://doi.org/10.5281/zenodo.13594332">https://doi.org/10.5281/zenodo.13594332</a></li> </ul> <p>Version 2 of this dataset contains also the "Forecaster Test Set" (fts.tar) which includes all generated forecasts for GPTCast8x8, GPTCast16x16, and Linda, to ensure reproducibility of the results.</p>
3DTREC precipitation nowcasts
<p>The following netCDF files show rainfall forecasts generated by the 3D-TREC nowcasting system (Otsuka et al 2019) for a convective event on 29th July 2021. The files include 120 nowcasts made every 30 seconds between 0700-0800 UTC. </p>
Precipitation nowcasting verification dataset
<p>Verification files for the Verif program. See https://github.com/WFRT/verif for details.</p>
Deep Learning approach towards Precipitation Nowcasting: RADOLAN
<p>In this repository you can find the converted images of the RADOLAN Product provided by the German Weather Service (DWD). This data was used as input data to train the models in the paper "Deep Learning Approach Towards Precipitation Nowcasting: Evaluating Regional Extrapolation Capabilities".</p> <p>Due to file size, the data set is split up into years. All years need to be placed in the same directory, so for example a correct directory structure would be /radarPNG/2017/, /radarPNG/2018/ etc.</p> <p>The raw RADOLAN data is available at https://opendata.dwd.de/weather/radar/radolan/</p> <p>More details at https://github.com/Nakroma/Precipitation-Nowcasting</p>
Dataset accompanying "Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data"
<p>Dataset accompanying the article <a href="https://arxiv.org/abs/2409.10367" target="_blank" rel="noopener">Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data</a>. </p> <p>Contains almost 8000 events that are sampled from the summer months (where convective precipitation events are most likely to occur) of 2019-2023, centred over Austria.</p> <p>Each sample has a temporal span of 4 hours with a spatial extent of 400 x 700 km, with following data streams:</p> <ul> <li>4 MSG infrared channels with central wavelengths of 6.2, 7.3, 8.7, and 10.8 μm</li> <li>Rain rates mosaicked from ground-based radar observations</li> <li>Lightning data from ground-based observations</li> <li>INCA precipitation analysis</li> <li>INCA convective available potential energy (CAPE) estimates</li> </ul> <p>The dataset is accompanied by elevation and coordinate information. </p> <p>Please refer to the manuscript and the <a href="https://github.com/caglarkucuk/earthformer-multisource-to-inca">GitHub repository</a> for further information and helper code for reading the data files.</p>
Data Archive for "Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification"
<p>This repository contains the training data and pretrained models for the paper "Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification".</p> <p>To use the data, clone the repository at <a href="https://github.com/MeteoSwiss/ldcast">https://github.com/MeteoSwiss/ldcast</a>. Unzip the files as follows:</p> <ul> <li>Demo files "ldcast-demo-20210622.zip" to the "data" directory</li> <li>Training and evaluation data archive "ldcast-datasets.zip" to the "data" directory</li> <li>Pretrained model archive "models-genforecast.zip" to the "models" directory</li> </ul>
ScienceDex guides
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