{"paper_id":"47df5dbd-9d58-495c-a0ff-68d5c5d198c1","body_text":"Modeling the Impact of Tropical Cyclone Intensity on Rainfall under Stochastic Processes | 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 Modeling the Impact of Tropical Cyclone Intensity on Rainfall under Stochastic Processes Patrick Chidzalo, Donnex Beyamu, John Mutepuwa, Charles Kambale, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6353520/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Modeling Earth Systems and Environment → Version 1 posted 12 You are reading this latest preprint version Abstract Rainfall prediction is crucial for agricultural planning and risk management in cyclone-prone areas like Malawi's Machinga district, where farming largely depends on rain-fed agriculture. Recent severe cyclones have highlighted the inadequacies of current predictive models, particularly in capturing extreme weather patterns influenced by tropical cyclones. This study addresses this gap by integrating Gamma and Weibull probability distributions into stochastic differential equations (SDEs) to model rainfall with greater accuracy. Physics-Informed Neural Networks (PINNs) are used to solve the SDE. The models have successfully demonstrated the ability to capture key rainfall characteristics, such as seasonality, mean reversion, and extreme values, through rigorous numerical simulations. Simulation results confirm the model's effectiveness, showing distinct behaviors as a normal rainfall parameter varies, indicating its critical role in scaling the impact of cyclones on rainfall. Application to real data from Machinga district revealed that lower values of the parameter correlate with increased cyclone activity and abnormal rainfall patterns. Over several seasons, the model accurately predicted extreme rainfall events, with performance metrics such as (R^2) values consistently exceeding 0.55, validating the model’s reliability and precision. Tropical Cyclones Stochastic Differential Equations PINNs Normal rainfall parameter Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Modeling Earth Systems and Environment → Version 1 posted Editorial decision: Revision requested 21 May, 2025 Reviews received at journal 08 May, 2025 Reviews received at journal 07 May, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Editor assigned by journal 03 Apr, 2025 Submission checks completed at journal 03 Apr, 2025 First submitted to journal 01 Apr, 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6353520\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":442304455,\"identity\":\"f2869e67-ad76-4ec7-baec-638e352b8cef\",\"order_by\":0,\"name\":\"Patrick 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