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Dataset results
29 results for “Nowcasting”
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>
Rtlive.de Nowcasting Results: DE until 2021-05-31
<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 (this record)</li> <li>DE_2021-06-01_to_2021-07-31 (https://zenodo.org/record/5683308)</li> </ul> <p>This file contains the model fits for (regions in) Germany, starting 2021-09-24 until 2021-05-31.</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>
Data used in the paper titled 'Reconstructing and Nowcasting the Rainfall Field by a CML Network'
<p>The dataset contains the CML, OTT disdrometer, rain gauge and radar data as well as some of the scripts used in the paper titled 'Reconstructing and Nowcasting the Rainfall Field by a CML Network'</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>
Data from: Adaptive nowcasting of influenza outbreaks using Google searches
Open the record for dataset details and reuse information.
A Benchmark Dataset for Lightning Nowcasting in Latin America
<p>In 2023, the <a href="https://wmo.int/" target="_blank" rel="nofollow noreferrer noopener">World Meteorological Organization</a> (WMO) commissioned a pilot project for nowcasting convective weather hazards using artificial intelligence (AI) methods. The project focuses on predicting lightning and quantitative precipitation estimation (QPE) over Latin America, Africa, and southeast Asia. This project is called the AI Nowcasting Pilot Project (AINPP), and is part of WMO's Early Warning for All initiative (<a href="https://wmo.int/activities/early-warnings-all/wmo-and-early-warnings-all-initiative" target="_blank" rel="nofollow noreferrer noopener">EW4All</a>).</p> <p>This benchmark dataset is for evaluating the prediction of lightning in Latin America. The dataset currently consists of 30 days data at 10-minute temporal resolution (5 consecutive days for 6 consecutive months). <br><br>Contents:</p> <ul> <li>mexico_targets.tar.gz: The targets or predictand over Mexico, using 1- and 2-hour accumulations of flash-extent density observed by the GOES-16 Geostationary Lightning Mapper.</li> <li>mexico_example_predictions.tar.gz: Example predictions from the <a href="https://cimss.ssec.wisc.edu/severe_conv/ptlg.html" target="_blank" rel="noopener">NOAA/CIMSS LightningCast model</a>, matching the files in mexico_targets.tar.gz.</li> <li>south_america_targets.tar.gz: The targets or predictand over South America, using 1- and 2-hour accumulations of flash-extent density observed by the GOES-16 Geostationary Lightning Mapper.</li> <li>south_america_example_predictions.tar.gz: Example predictions from the <a href="https://cimss.ssec.wisc.edu/severe_conv/ptlg.html" target="_blank" rel="noopener">NOAA/CIMSS LightningCast model</a>, matching the files in south_america_targets.tar.gz.</li> </ul>
Global Landslide Nowcast from LHASA L4 1 day 1 km x 1 km version 1.1 (Global_Landslide_Nowcast) at GES DISC
The Landslide Hazard Assessment for Situational Awareness (LHASA) model identifies locations with high potential for landslide occurrence at a daily temporal resolution. LHASA combines satellite‐based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a “nowcast” is issued to indicate the times and places where landslides are more probable. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run and spatial coverage is from 60°N to 60°S .
Global Landslide Nowcast from LHASA L4 1 day 1 km x 1 km version 2.0.0 (Global_Landslide_Nowcast) at GES DISC
The Global Landslide Nowcast addresses the need for real-time situational awareness of landslide hazard. The Landslide Hazard Assessment for Situational Awareness model (LHASA) combines satellite rainfall estimates from the Global Precipitation Measurement mission (GPM) with soil moisture estimates from the Soil Moisture Active Passive (SMAP) satellite and other factors to produce a map of locations where rainfall-triggered landslide activity is probable. Due to the latency of the rainfall data, the nowcast is a near-real time product with a minimum latency of 5 hours. Although the model could be run every half hour, this archive contains a daily record derived from a retrospective model run.The Global Landslide Nowcast version 2.0.0 retains replaces the heuristic decision tree from version 1.0 with a machine learning model. Instead of merging all factors other than precipitation into a susceptibility map, LHASA 2.0 takes in each variable as a separate input layer. The most important change is the replacement of the categorical nowcast with a probabilistic output. This will enable users to adjust the threshold to suit their specific application and geographic location.
IEA PVPS Task 16 Satellite nowcasting benchmark : Dataset of animated satellite images for the selection of case studies
<p>This dataset contains animated satellite images used for the selection of case studies in the framework of the IEA PVPS task 16 satellite nowcasting benchmark. The animated images contains images from the channel 12 of MSG as well as NWC SAF products (cloud types and cloud top height) over France over the year 2020.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.