SIGMAformer: a spatiotemporal gaussian mixture correlation transformer for global weather forecasting

SIGMAformer: a spatiotemporal gaussian mixture correlation transformer for global weather 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 Article SIGMAformer: a spatiotemporal gaussian mixture correlation transformer for global weather forecasting Do-Yeon Kim, Heung-Il Suk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8131606/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2026 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 12 You are reading this latest preprint version Abstract SpatIotemporal Gaussian Mixture correlAtion transformer (SIGMAformer) is a spatiotemporal forecasting architecture that integrates a Gaussian mixture pattern extractor (GMPE) with a dynamic spatiotemporal correlation (DSTC) mechanism. The DSTC module leverages GMPE to automatically compute spatiotemporal pattern-specific weights from the data. These weights are first used to calculate temporal correlations within each station and then integrated with global pattern weights to evaluate spatial correlations across stations. This nonlinear, dynamically adaptive modeling approach emphasizes critical spatiotemporal patterns while suppressing less relevant ones. Experiments on global weather datasets reveal that SIGMAformer consistently outperforms state-of-the-art forecasting models and significantly improves wind speed prediction. Removing DSTC increased the mean squared error values by up to 7.18% and 7.22% for wind speed and temperature predictions, respectively. These findings underscore SIGMAformer’s capacity to capture essential spatiotemporal patterns and establish a scalable methodology for intelligent sensor-network fusion in environmental forecasting. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Mar, 2026 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Revision requested 14 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviewers agreed at journal 30 Dec, 2025 Reviewers agreed at journal 30 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 28 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor assigned by journal 19 Nov, 2025 Submission checks completed at journal 19 Nov, 2025 First submitted to journal 17 Nov, 2025 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. 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