Multi-scale attention encoding-dynamic decoding network for short-term precipitation forecasting

Multi-scale attention encoding-dynamic decoding network for short-term precipitation forecasting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-scale attention encoding-dynamic decoding network for short-term precipitation forecasting Xianjun Du, Hangfei Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4813013/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Dec, 2024 Read the published version in Earth Science Informatics → Version 1 posted 10 You are reading this latest preprint version Abstract Short-term precipitation forecasting is a critical task in the fields of meteorology and hydrology. To overcome the limitations of traditional forecasting methods in handling complex meteorological phenomena and the issue of cumulative errors in image sequence prediction by recurrent neural networks, a short-term precipitation forecasting method called multi-scale attention encoding-dynamic decoding network (MAEDDN) has been developed. It predicts future precipitation by learning the spatiotemporal features of the input data. Within the encoding process, convolutional blocks with spatial and channel attention are utilized for encoding, and a multi-scale fusion module is employed to address the challenge of capturing both small-scale and large-scale information in precipitation distribution simultaneously. In the short-term precipitation processes to address the generation and dissipation. In the decoding process, a dynamic decoding network is proposed to flexibly select the decoding process based on the learned intensity distribution and change trends from the past input data Experiments are conducted by using the precipitation data from the open-source SEVIR dataset, and comparisons are made with the best methods reported so far. The experimental results reveal that: (1) MAEDDN enhances the forecasting capability in areas with high-intensity precipitation, and (2) MAEDDN outperforms other models in terms of the resolution of predicted image sequences. The constructed multi-scale attention encoding captures the complex relationships in meteorological data more effectively, while the dynamic decoding adapts the decoding process based on different scenarios, resulting in more accurate prediction outcomes. Short-term forecasting Dynamic decoding network Spatio-temporal features Multi-scale fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2024 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 21 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviewers agreed at journal 09 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviewers invited by journal 09 Aug, 2024 Editor assigned by journal 09 Aug, 2024 Submission checks completed at journal 05 Aug, 2024 First submitted to journal 27 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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