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RainBench: Towards Global Precipitation Forecasting from Satellite Imagery

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dc.abstract.enExtreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release \textbf{PyRain}, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.
dc.affiliationUniwersytet Warszawski
dc.conference.countryKanada
dc.conference.datefinish2021-02-09
dc.conference.datestart2021-02-02
dc.conference.placeVancouver
dc.conference.seriesNational Conference of the American Association for Artificial Intelligence
dc.conference.seriesNational Conference of the American Association for Artificial Intelligence
dc.conference.seriesshortcutAAAI
dc.conference.shortcutAAAI 2021
dc.conference.weblinkhttps://aaai.org/Conferences/AAAI-21/
dc.contributor.authorWitt, Christian Schroeder de
dc.contributor.authorTong, Catherine
dc.contributor.authorBiliński, Piotr
dc.contributor.authorKalaitzis, Freddie
dc.contributor.authorWatson-Parris, Duncan
dc.contributor.authorZantedeschi, Valentina
dc.contributor.authorChantry, Matthew
dc.contributor.authorMartini, Daniele De
dc.date.accessioned2024-01-25T18:46:02Z
dc.date.available2024-01-25T18:46:02Z
dc.date.issued2021
dc.description.financePublikacja bezkosztowa
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/117823
dc.identifier.weblinkhttps://ojs.aaai.org/index.php/AAAI/article/view/17749
dc.languageeng
dc.pbn.affiliationcomputer and information sciences
dc.relation.pages1-11
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.titleRainBench: Towards Global Precipitation Forecasting from Satellite Imagery
dc.typeJournalArticle
dspace.entity.typePublication