Data-driven reconstruction of wind speed randomness in an urban area

Data-driven reconstruction of wind speed randomness in an urban area | 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 Data-driven reconstruction of wind speed randomness in an urban area Otoniel Walle, María Valentina Iréndira Soto, Fernando Saldaña, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3994840/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Feb, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted 4 You are reading this latest preprint version Abstract This study proposes two diffusion models to analyze the wind speed variability in an urban area. The analysis is based on annual time series data collected from fourteen weather stations. A basic criterion has been suggested to categorize these stations based on the variance of the stochastic process for the stationary case. This criterion can be used in studies of air pollution, wind energy, and other related fields where the geographical classification of weather stations is not feasible. The Kramers-Moyal (KM) coefficients and kernel-based regression (KBR) have been utilized to estimate the drift and diffusion terms. The numerical solution of the proposed Langevin equation was used to calculate the statistical properties of the process, taking into account the variance values for station classification. The results show that only two Langevin models are required instead of the original fourteen, based on the variance values. This demonstrates that it is feasible to establish models using basic statistical properties of time series when geographical classification is not possible. Reconstruction Langevin equation Wind speed Parameter estimation Time series Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Feb, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted Editorial decision: Revision requested 03 Mar, 2024 Editor assigned by journal 03 Mar, 2024 Submission checks completed at journal 02 Mar, 2024 First submitted to journal 27 Feb, 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. 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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-3994840","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276038567,"identity":"ce7c3826-c2aa-4909-a5b8-9d0273823d28","order_by":0,"name":"Otoniel Walle","email":"","orcid":"","institution":"Universidad Autónoma de Nuevo León","correspondingAuthor":false,"prefix":"","firstName":"Otoniel","middleName":"","lastName":"Walle","suffix":""},{"id":276038568,"identity":"bb3eaa3c-da44-4ba5-9edf-d913038427f7","order_by":1,"name":"María Valentina Iréndira Soto","email":"","orcid":"","institution":"Universidad Autónoma de Nuevo 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