Title: Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting
Abstract: Chapter 15 Deep Learning and the Weather Forecasting Problem: Precipitation Nowcasting Zhihan Gao, Search for more papers by this authorXingjian Shi, Search for more papers by this authorHao Wang, Search for more papers by this authorDit-Yan Yeung, Search for more papers by this authorWang-chun Woo, Search for more papers by this authorWai-Kin Wong, Search for more papers by this author Zhihan Gao, Search for more papers by this authorXingjian Shi, Search for more papers by this authorHao Wang, Search for more papers by this authorDit-Yan Yeung, Search for more papers by this authorWang-chun Woo, Search for more papers by this authorWai-Kin Wong, Search for more papers by this author Book Editor(s):Gustau Camps-Valls, Universitat de València, SpainSearch for more papers by this authorDevis Tuia, EPFL, SwitzerlandSearch for more papers by this authorXiao Xiang Zhu, German Aerospace Center and Technical University of Munich, GermanySearch for more papers by this authorMarkus Reichstein, Max Planck Institute, GermanySearch for more papers by this author First published: 20 August 2021 https://doi.org/10.1002/9781119646181.ch15 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Summary Precipitation nowcasting refers to the prediction of rainfall with high spatiotemporal resolutions in a timely and accurate manner for the next 6 hours. The skillful and high-quality rainfall forecasts meet various operational needs in support of rainstorm monitoring, alerting or warning systems that are invaluable to weather services, and disaster risk reduction of high-impact weather or rainstorms for protecting people's lives. Conventional approaches that rely on expert knowledge are not easy to generalize and require considerable computational cost. Recently, deep learning (DL)-based methods for precipitation nowcasting have shown promise in overcoming these problems. In this chapter, we introduce current progress of DL-based methods for precipitation nowcasting. Firstly, we mathematically formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem and introduce several general learning strategies. After that, we provide a comprehensive review of existing DL-based models and introduce a systematic benchmark for performance evaluation. Finally, future research directions on development of DL in precipitation nowcasting and meteorological forecasting applications are discussed. Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences RelatedInformation
Publication Year: 2021
Publication Date: 2021-08-20
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 12
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