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29 results for “Nowcasting”
Caravan MultiMet (Part 2, Forecasts): Extending Caravan with Multiple Weather Nowcasts and Forecasts
<p>Caravan MultiMet is a novel extension to Caravan, focusing on enriching the meteorological forcing data. Our extension adds three precipitation nowcast products (CPC, IMERG v07 Early, and CHIRPS) and three weather forecast products (ECMWF IFS HRES, GraphCast, and CHIRPS-GEFS). Since all data is kept in it's original time zone (UTC+0) and the ERA5-Land data in the original Caravan data set is shifted to local time of each gauge, we also include ERA5-Land reanalysis data in this extension, matching the UTC-0 timezone for all gauges of the other forcings. This part of the extension includes the <strong>forecast</strong> products.</p> <p>The inclusion of diverse data sources, particularly weather forecasts, enables more robust evaluation and benchmarking of hydrological models, especially for real-time forecasting scenarios. To the best of our knowledge, this extension makes Caravan the first open large-sample hydrology dataset to incorporate weather forecast data.</p> <p>The data is also publicly available on Google Cloud Platform (GCP), and we provide below a colab with an example of how to access it which does not require downloading the entire dataset.</p> <p>Additional resources:</p> <ul> <li>The <a href="https://github.com/kratzert/Caravan">Caravan GitHub repository</a> includes further information and links to other extensions.</li> <li>The original <a href="https://www.nature.com/articles/s41597-023-01975-w">Caravan paper</a></li> </ul> <p>------</p> <p>Channel log:</p> <ul> <li>21 November 2024: Version 1.1 - Fixed bug in the FAO Penman-Monteith potential evaporation - values are now clipped to 0 (previously, some values were negative). Only the ERA5-Land dataset was changed.</li> </ul>
Caravan MultiMet (Part 1, Nowcasts): Extending Caravan with Multiple Weather Nowcasts and Forecasts
<p>Caravan MultiMet is a novel extension to Caravan, focusing on enriching the meteorological forcing data. Our extension adds three precipitation nowcast products (CPC, IMERG v07 Early, and CHIRPS) and three weather forecast products (ECMWF IFS HRES, GraphCast, and CHIRPS-GEFS). Since all data is kept in it's original time zone (UTC+0) and the ERA5-Land data in the original Caravan data set is shifted to local time of each gauge, we also include ERA5-Land reanalysis data in this extension, matching the UTC-0 timezone for all gauges of the other forcings. This part of the extension includes the <strong>nowcast</strong> products.</p> <p>The inclusion of diverse data sources, particularly weather forecasts, enables more robust evaluation and benchmarking of hydrological models, especially for real-time forecasting scenarios. To the best of our knowledge, this extension makes Caravan the first open large-sample hydrology dataset to incorporate weather forecast data.</p> <p>The data is also publicly available on Google Cloud Platform (GCP), and we provide below a colab with an example of how to access it which does not require downloading the entire dataset.</p> <p>Additional resources:</p> <ul> <li>The <a href="https://github.com/kratzert/Caravan">Caravan GitHub repository</a> includes further information and links to other extensions.</li> <li>The original <a href="https://www.nature.com/articles/s41597-023-01975-w">Caravan paper</a></li> </ul> <p>------</p> <p>Channel log:</p> <ul> <li>21 November 2024: Version 1.1 - Fixed bug in the FAO Penman-Monteith potential evaporation - values are now clipped to 0 (previously, some values were negative). Only the ERA5-Land dataset was changed.</li> </ul>
Machine learning code and dataset for "Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance"
<p>This repository contains the code and dataset for the paper:</p> <p>Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance, Natural Hazards and Earth System Sciences, 2022, <a href="https://doi.org/10.5194/nhess-2021-171">https://doi.org/10.5194/nhess-2021-171</a></p> <p>The GitHub code repository at <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">https://github.com/meteoswiss-mdr/ts-nowcast-datasources</a> may contain a more up-to-date version of the code if bug fixes etc. have been necessary. The file <a href="https://zenodo.org/api/files/41faa1b7-17f6-4a75-be09-7743426ef13c/ts-nowcast-datasources-publication.zip">ts-nowcast-datasources-publication.zip</a> in this Zenodo release contains the status of the GitHub repository at the time of the publication of the paper.</p> <p>For instructions for using the data, please see the <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">code repository</a>.</p> <p> </p>
Data archive for Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
<p>This dataset contains the machine learning training data files, pretrained model weights and results for the paper Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning, submitted to Natural Hazards and Earth System Sciences, 2023.</p> <p>The radar dataset can be found at the following Zenodo repository: <a href="https://doi.org/10.5281/zenodo.6325370">https://doi.org/10.5281/zenodo.6325370</a></p> <p>For instructions for using the data, please see the GitHub code repository at <a href="http://github.com/meteoswiss/c4dl-polar">https://github.com/meteoswiss/c4dl-polar</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Training data (patches_quality-index_2020.zip or patches_*_2020.nc) -> data/2020/</li> <li>Results: (results.zip) -> runs/run*/results/</li> <li>Pretrained models (models_run*) -> runs/run*/</li> </ul>
Machine-learning based lightning nowcasting data archive
<p>This data archive contains the derived data supporting the findings of article "Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset". The paper is currently in the preprint version: https://doi.org/10.21203/rs.3.rs-2616886/v1</p> <p>The prediction results in this data archive are generated by various models:</p> <p>1. Current model. The model involves data input of aerosol observations together with meteorological variables and auxiliary datasets, as well as data enrichment by Geostationary Lightning Mapper (GLM). In the demo of the dataset, the year of 2020 is trained and predicted on a cross-validation scheme. </p> <p>2. LMA model. The model acts as the baseline model considering only data label obtained from the ground-based Lightning Mapping Array (LMA), which observes accurate lightning occurrence in limited spatial range.</p> <p>3. No-AOD model. The model acts as the baseline model considering no aerosol observation is utilized during the machine learning process. </p> <p>The model results are demonstrated in a continuous value in 0-1. Trade-offs between Probability of Detection (POD) and False Alarm Ratio (FAR) can be optimized by selection of different thresholds. </p> <p>Other datasets:</p> <p>1. Dataset for training. It is for the public use of machine learning training for the current model and no-AOD model (training input features vary).</p> <p>2. PM2.5 dataset. The real-time spatially continuous and hourly-level PM<sub>2.5</sub> dataset is obtained following a published method by Zeng et al.. In this method, the fundamental in-situ measurements are obtained from Air Quality System (AQS) monitoring network operated by United States Environmental Protection Agency.</p> <p>Reference:</p> <p>Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1]</p> <p>Zeng, Z. et al. Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research 254, 105516 (2021).</p>
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>
INCA-CH seamless nowcasting system: 1km digital elevation model
<p>Digital elevation model at 1km horizontal resolution used in the INCA-CH seamless nowcasting system (in Swiss coordinates CH03). For INCA-CH parameters see here: <a href="https://zenodo.org/record/6470725">INCA-CH set of data</a></p>
Supplementary data for: Nowcasting 3D cloud fields using forward warping optical flow
Open the record for dataset details and reuse information.
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>
"Nowcasting Solar EUV Irradiance with Photospheric Magnetic Fields and the MgII Index" Figures, Scripts, and Data
<p>These tar files, scripts, and datasets were used in the paper "Nowcasting Solar EUV Irradiance with Photospheric Magnetic Fields and the MgII Index," submitted for publication to the Space Weather Journal. More information on what is included in this archive can be found the the ReadMe file. </p>
Data archive for "Seamless lightning nowcasting with recurrent-convolutional deep learning"
<p>This dataset contains the machine learning training data files, pretrained model weights and precomputed results for the paper "Seamless lightning nowcasting with recurrent-convolutional deep learning" published in:<br> Leinonen, J., Hamann, U., & Germann, U. (2022). Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning, <em>Artificial Intelligence for the Earth Systems</em>, <em>1</em>(4), e220043, doi:<a href="https://doi.org/10.1175/AIES-D-22-0043.1">10.1175/AIES-D-22-0043.1</a>.<br> A preprint of the paper can be found at <a href="https://arxiv.org/abs/2203.10114">https://arxiv.org/abs/2203.10114</a>.</p> <p>The ML code can be found at <a href="https://github.com/MeteoSwiss/c4dl-lightningdl">https://github.com/MeteoSwiss/c4dl-lightningdl</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Training data (c4dl-patches-*.zip) -> data/2020/</li> <li>Results (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-results-lightningdl.zip?versionId=54046830-4c7e-48c6-af42-d6d5606af86b">c4dl-results-lightningdl.zip</a>) -> results/</li> <li>Pretrained models (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-models-lightningdl.zip?versionId=364bca7c-e6ad-4ed9-9264-57c759ea0ac6">c4dl-models-lightningdl.zip</a>) -> models/</li> </ul> <p>Additionally, the file <a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-randomexamples-lightningdl.zip?versionId=426ca113-950f-4a50-8ef0-8e5d12afe697">c4dl-randomexamples-lightningdl.zip</a> contains the randomly selected examples complementing Figs. 7–9 of the paper, and the file <a href="https://zenodo.org/api/files/939609f2-6699-4f56-9428-391ebe78e010/c4dl-inputsamples-lightningdl.zip">c4dl-inputsamples-lightningdl.zip</a> contains figures showing samples of all the input variables for the three cases shown in Figs. 7–9.</p>
Nowcast of Aerospace Ionizing Radiation System (NAIRAS) simulation of the effect of the 2024-05-11 coronal mass ejection and solar particle event on Earth's atmosphere
<p>The effect of the CME on the cutoff rigidity and the dose at different altitude as computed by NAIRAS.</p> <p>The neutron monitor data from OULU and the DSCOVR data for solar wind density and speed are put as a reference for when the Forbush decrease happens and when the CME arrives.</p> <p> </p> <p>The version 2 added files with shorter lead time before the CME arrival and bigger labels</p>
Pretrained models and results for "Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model"
<p>This dataset contains the pretrained model weights and precomputed results for the paper "Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model" submitted to <em>Geophysical Research Letters</em>. A preprint of the paper can be found at <a href="https://arxiv.org/abs/2211.01001">https://arxiv.org/abs/2211.01001</a>.</p> <p>The ML code can be found at <a href="https://github.com/MeteoSwiss/c4dl-multi">https://github.com/MeteoSwiss/c4dl-multi</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Results (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-results-lightningdl.zip?versionId=54046830-4c7e-48c6-af42-d6d5606af86b">c4dl-results-lightningdl.zip</a>) -> results/</li> <li>Pretrained models (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-models-lightningdl.zip?versionId=364bca7c-e6ad-4ed9-9264-57c759ea0ac6">c4dl-models-lightningdl.zip</a>) -> models/</li> <li>If you want to train models, data (<a href="https://zenodo.org/api/files/0cfc0cf4-755a-4618-8341-39b107a3901c/c4dl-patches-2020-additional.zip">c4dl-patches-2020-additional.zip</a>) -> data/2020/</li> </ul> <p>Additionally, you will need the datasets from <a href="https://zenodo.org/deposit/6802292">this Zenodo archive</a>. Follow the instructions there for downloading.</p>
Precipitation nowcasting verification dataset
<p>Verification files for the Verif program. See https://github.com/WFRT/verif for details.</p>
Temperature nowcasting verification dataset
<p>Verification files for the Verif program. See https://github.com/WFRT/verif for details.</p>
Data from: Adaptive nowcasting of influenza outbreaks using Google searches
Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population. Others have however argued that equally good estimates of current flu levels can be forecast using historic flu measurements. Here, we build dynamic 'nowcasting' models; in other words, forecasting models that estimate current levels of influenza, before the release of official data one week later. We find that when using Google Flu Trends data in combination with historic flu levels, the mean absolute error (MAE) of in-sample 'nowcasts' can be significantly reduced by 14.4%, compared with a baseline model that uses historic data on flu levels only. We further demonstrate that the MAE of out-of-sample nowcasts can also be significantly reduced by between 16.0% and 52.7%, depending on the length of the sliding training interval. We conclude that, using adaptive models, Google Flu Trends data can indeed be used to improve real-time influenza monitoring, even when official reports of flu infections are available with only one week's delay.
A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting"
<p>Copyright (2024) EUMETSAT</p>
Rtlive.de Nowcasting Results: DE 2021-06-01 to 2021-07-31
<p><strong>Summary</strong></p> <p>This entry belongs to a multi-part dataset of daily Rtlive.de modeling results:</p> <ul> <li>DE_2020-09-24_to_2021-05-31 (https://zenodo.org/record/5070416)</li> <li>DE_2021-06-01_to_2021-07-31 (this record)</li> </ul> <p>This file contains the model fits for (regions in) Germany, starting 2021-06-01 until 2021-07-31.</p> <p><strong>Acknowledgments</strong></p> <p>The Rtlive model was built by Kevin Systrom with help by Thomas Vladeck, Junpeng Lao and more (see https://github.com/rtcovidlive/rtlive-global and https://github.com/rtcovidlive/covid-model).</p> <p>Laura Helleckes and Michael Osthege refactored the model and added routines for forecasting of total tests in Germany.</p> <p>The MCMCs were computed on infrastructure provided by the Modeling and Simulation group at the IBG-1, Forschungszentrum Jülich.</p> <p>Data of regionally resolved tests that went into the data analyses was kindly provided by the Antibiotic Resistance Surveillance (ARS) group at the Robert Koch-Institut. For more information see <a href="https://ars.rki.de/Content/COVID19/Reports.aspx">https://ars.rki.de/Content/COVID19/Reports.aspx</a>.</p>
INCA-CH seamless nowcasting system, example data
<p><strong>INCA-CH seamless nowcasting system</strong></p> <p>A new generation of nowcasting systems is in development at MeteoSwiss (since 2025). For this reason only a subset of the current INCA-CH parameters will be available trough our <a href="http://opendatadocs.meteoswiss.ch/">open data provision</a>. During the transition phase to the new system data push deliveries of all current parameters will be guaranteed only for already existing users. However, a change is to be planned (probably in 2027): parameters will be renamed, added and/or removed. Information on this will follow as soon as it becomes available.</p> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Update frequency</strong></p> </td> <td> <p><strong>Forecast range</strong></p> </td> <td> <p><strong>Availability after </strong><br><strong><strong>obs. time**</strong></strong></p> </td> <td> <p><strong>Output granularity</strong></p> </td> </tr> <tr> <td> <p><strong>RR</strong></p> </td> <td> <p>Precipitation quantitative<br>(based on CombiPrecip)</p> </td> <td> <p>mm/h</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>7-10 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>RR_ext*</strong></p> </td> <td> <p>Precipitation quantitative extended forecast<br>(based on CombiPrecip)</p> </td> <td> <p> mm/h</p> </td> <td> <p>10 min</p> </td> <td> <p>0-28/33h</p> </td> <td> <p>8-11 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>RP</strong></p> </td> <td> <p>Precipitation qualitative<br>(based on radar only)</p> </td> <td> <p>mm/h</p> </td> <td> <p>5 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>4-5 min</p> </td> <td> <p>5 min</p> </td> </tr> <tr> <td> <p><strong>RP_ext*</strong></p> </td> <td> <p>Precipitation qualitative extended forecast<br>(based on radar only)</p> </td> <td> <p>mm/h</p> </td> <td> <p> 5 min</p> </td> <td> <p>0-28/33h</p> </td> <td> <p>5-6 min</p> </td> <td> <p>5 min</p> </td> </tr> <tr> <td> <p><strong>RS</strong></p> </td> <td> <p>Snowfall quantitative (based on CombiPrecip)</p> </td> <td> <p>mm/h</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>5-6 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>RS_ext*</strong></p> </td> <td> <p>Snowfall quantitative extended forecast (based on CombiPrecip)</p> </td> <td> <p>mm/h</p> </td> <td> <p>10 min</p> </td> <td> <p>0-28/33h</p> </td> <td> <p>6-7 min </p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>PN</strong></p> </td> <td> <p>Snowfall qualitative (based on radar only)</p> </td> <td> <p>mm/h</p> </td> <td> <p>5 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>5-6 min</p> </td> <td> <p>5 min</p> </td> </tr> <tr> <td> <p><strong>PN_ext*</strong></p> </td> <td> <p>Snowfall qualitative extended forecast (based on radar only)</p> </td> <td> <p>mm/h</p> </td> <td> <p>5 min</p> </td> <td> <p>0-28/33h</p> </td> <td> <p>6-7 min</p> </td> <td> <p>5 min</p> </td> </tr> <tr> <td> <p><strong>PT</strong></p> </td> <td> <p>Precipitation type for RR:<br>rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only)</p> </td> <td> <p>classes (0-5)</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>7-10 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>PT_ext*</strong></p> </td> <td> <p>Precipitation type for RR_ext, extended forecast:<br>rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only)</p> </td> <td> <p>classes (0-5)</p> </td> <td> <p>10 min </p> </td> <td> <p>0-28/33h</p> </td> <td> <p>8-11 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>NT</strong></p> </td> <td> <p>Precipitation type for RP:<br>rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only)</p> </td> <td> <p>classes (0-5)</p> </td> <td> <p>5 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>4-5 min</p> </td> <td> <p>5 min</p> </td> </tr> <tr> <td> <p><strong>NT_ext*</strong></p> </td> <td> <p>Precipitation type for RP_ext extended forecast:<br>rain, snow, snow-rain, freezing rain, rain and hail (hail first 30min only)</p> </td> <td> <p>classes (0-5)</p> </td> <td> <p>5 min </p> </td> <td> <p>0-28/33h</p> </td> <td> <p>5-6 min</p> </td> <td> <p>5 min</p> </td> </tr> <tr> <td> <p><strong>SH_BK_24, SH_BK_12</strong></p> </td> <td> <p>New snow accumulation: past 24h or 12h</p> </td> <td> <p>cm</p> </td> <td> <p>10 min</p> </td> <td> <p>0 h</p> </td> <td> <p>15-20 min</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>SH_FC_06</strong></p> </td> <td> <p>6 h new snow accumulation forecast</p> </td> <td> <p>cm</p> </td> <td> <p>10 min</p> </td> <td> <p>6 h</p> </td> <td> <p>15-20 min</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>ZS</strong></p> </td> <td> <p>Snowfall line</p> </td> <td> <p>m</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>13-16 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>Z0</strong></p> </td> <td> <p>Zero degree isotherm</p> </td> <td> <p>m</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>13-16 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>FF, WG, DD, UU, VV</strong></p> </td> <td> <p>Wind speed, wind gust and wind direction:<br>hourly mean and hourly max for wind gust</p> </td> <td> <p>m/s, °</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>17-19 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>FF_10min, WG_10min, DD_10min, UU_10min, VV_10min</strong></p> </td> <td> <p>Wind speed, wind gust and wind direction:<br>10 min mean and 10 min max for wind gust</p> </td> <td> <p>m/s, °</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6h</p> </td> <td> <p>17-19 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>TT</strong></p> </td> <td> <p>2 m temperature</p> </td> <td> <p>°C</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>13-16 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>TT_ext</strong></p> </td> <td> <p>2 m temperature extended forecast</p> </td> <td> <p>°C</p> </td> <td> <p>10 min</p> </td> <td> <p>0-28/33 h</p> </td> <td> <p>14-17 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>TD, RH</strong></p> </td> <td> <p>2 m dew point and humidity</p> </td> <td> <p>°C, %</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>13-16 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>TD_ext, RH_ext</strong></p> </td> <td> <p>2 m dew point and humidity extended forecast</p> </td> <td> <p>°C, %</p> </td> <td> <p>10 min</p> </td> <td> <p>0-28/33 h</p> </td> <td> <p>14-17 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>TG</strong></p> </td> <td> <p>Soil surface temperature</p> </td> <td> <p>°C</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6 h</p> </td> <td> <p>13-16 min</p> </td> <td> <p>60 min</p> </td> </tr> <tr> <td> <p><strong>CT, CL, CM, CH</strong></p> </td> <td> <p>Cloud cover: total, low, medium, high</p> </td> <td> <p>%</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6h </p> </td> <td> <p>5-10 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>SU</strong></p> </td> <td> <p>Relative sunshine duration</p> </td> <td> <p> %</p> </td> <td> <p>10 min</p> </td> <td> <p>0-6h </p> </td> <td> <p>5-10 min</p> </td> <td> <p>10 min</p> </td> </tr> <tr> <td> <p><strong>LI</strong></p> </td> <td> <p>Lightning counts analysis</p> </td> <td> <p> #flash/ 7x7 km²/10 min</p> </td> <td> <p>10 min</p> </td> <td> <p>0 h</p> </td> <td> <p>5 min</p> </td> <td> <p> -</p> </td> </tr> <tr> <td> <p><strong>CN</strong></p> </td> <td> <p>Convective inhibition</p> </td> <td> <p>J/kg</p> </td> <td> <p>10 min</p> </td> <td> <p>0 h</p> </td> <td> <p>14-17 min</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>CP</strong></p> </td> <td> <p>Convective available potential energy</p> </td> <td> <p>J/kg</p> </td> <td> <p>10 min</p> </td> <td> <p>0 h</p> </td> <td> <p>14-17 min</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>nx = 710<br>ny = 640<br>resolution = 1000 [m]</p> <p>Coordinates of lower left pixel in Swiss Coordinates <a href="https://epsg.io/21781">CH1903/LV03</a>:<br>x0 = 255500 [m]<br>y0 = -159500 [m]</p> <p>Example how to read INCA netcdf data:<a href="https://inca-examples.readthedocs.io/"> https://inca-examples.readthedocs.io/ </a></p> <p>INCA-CH digital elevation Model here: <a href="https://zenodo.org/record/7614565">https://zenodo.org/record/7614565</a></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>
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.