Estimation of Thermal Comfort Index Under Climate Model Uncertainty 

Estimation of Thermal Comfort Index Under Climate Model Uncertainty | 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 Estimation of Thermal Comfort Index Under Climate Model Uncertainty Yidan Gao, Ido Nevat, Francois Septier This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4727116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2025 Read the published version in Environmental Modeling & Assessment → Version 1 posted 3 You are reading this latest preprint version Abstract We develop a new statistical model and an algorithm for estimating any Thermal Comfort Index (TCI) by combining two practical considerations: 1) the climate model estimates are imperfect (noisy) due to model inaccuracies. 2) the climate variables exhibit complex non-linear dependence structure and follow non-Gaussian distributions, which we capture via Statistical Copula modelling. We first show that our model can be interpreted from a statistical perspective as a hierarchical statistical model and is challenging to perform inference of the TCI values due to its mathematical intractability. To overcome this challenging problem, we develop a novel inferential procedure, based on an Importance sampling algorithm to estimate the statistical properties of the TCI.We show that our algorithm provides much lower Mean Squared Error (MSE) of estimating the TCI values than a conventional approach which does not take the uncertainties into account. We then demonstrate the suitability of our algorithm for real-world examples using WRF climate model simulations and the Heat index thermal comfort model. The proposed algorithm can help policymakers make well-informed decisions regarding health and climate-informed urban design. Thermal Comfort Index Climate model Copula theory Importance sampling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2025 Read the published version in Environmental Modeling & Assessment → Version 1 posted Editor assigned by journal 28 Jul, 2024 Submission checks completed at journal 17 Jul, 2024 First submitted to journal 11 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4727116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336445114,"identity":"0378734c-83f5-4b97-84e9-65602592f837","order_by":0,"name":"Yidan Gao","email":"","orcid":"","institution":"TUMCREATE","correspondingAuthor":false,"prefix":"","firstName":"Yidan","middleName":"","lastName":"Gao","suffix":""},{"id":336445117,"identity":"27e77cf0-5f5f-4cb7-a5ce-fc51111101ac","order_by":1,"name":"Ido Nevat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACNoYECIMfRCQUkKBFQrIBpMWAKHugWgwOgChitPCxJx978LHNrs74/OrEDw8MGOT5xQ4QcBjPs3TDmW3JEmY33m6WADrMcObsBAJaJHLMpHnOMAO1nN0A0pJgcJsYLX/O1EsYzzi7+QfxWhgqDksY8PduI9IWnmdpkj0VxyVn3ODdZpFgIEHYL/LtycckfhhU8/P3n91880eFjTy/NAEtCCABVilBrHIQ4D9AiupRMApGwSgYSQAAN3s+a6xx+r8AAAAASUVORK5CYII=","orcid":"","institution":"TUMCREATE","correspondingAuthor":true,"prefix":"","firstName":"Ido","middleName":"","lastName":"Nevat","suffix":""},{"id":336445118,"identity":"77ea789f-183d-4d69-8556-d397e17245e6","order_by":2,"name":"Francois Septier","email":"","orcid":"","institution":"University of Southern Brittany","correspondingAuthor":false,"prefix":"","firstName":"Francois","middleName":"","lastName":"Septier","suffix":""}],"badges":[],"createdAt":"2024-07-12 00:33:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4727116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4727116/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10666-025-10056-8","type":"published","date":"2025-08-28T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90344873,"identity":"bccc2d88-bc50-4517-b55a-97952302b2c6","added_by":"auto","created_at":"2025-09-01 16:07:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2844333,"visible":true,"origin":"","legend":"","description":"","filename":"submittedversion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4727116/v1_covered_dd7ab7fe-6eb7-4893-9f7e-2c2f412d1f1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of Thermal Comfort Index Under Climate Model Uncertainty ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-modeling-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enmo","sideBox":"Learn more about [Environmental Modeling \u0026 Assessment](https://www.springer.com/journal/10666)","snPcode":"10666","submissionUrl":"https://submission.nature.com/new-submission/10666/3","title":"Environmental Modeling \u0026 Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Thermal Comfort Index, Climate model, Copula theory, Importance sampling","lastPublishedDoi":"10.21203/rs.3.rs-4727116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4727116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We develop a new statistical model and an algorithm for estimating any Thermal Comfort Index (TCI) by combining two practical considerations: 1) the climate model estimates are imperfect (noisy) due to model inaccuracies. 2) the climate variables exhibit complex non-linear dependence structure and follow non-Gaussian distributions, which we capture via Statistical Copula modelling. 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