Modeling Atmospheric Variables Influencing Extreme Rainfall Events in Makassar City During February 2025

Modeling Atmospheric Variables Influencing Extreme Rainfall Events in Makassar City During February 2025 | 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 Atmospheric Variables Influencing Extreme Rainfall Events in Makassar City During February 2025 Fahrezzy Arthur Ridho, Bintiara Safitri, Eldwy Heny Parrangan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6867645/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Extreme rainfall events have become increasingly frequent and intense in tropical urban areas, posing serious challenges for disaster management and early warning systems. This study aims to identify the key atmospheric variables influencing extreme rainfall intensity in Makassar City, Indonesia, during the period of 7–15 February 2025, focusing on a major event that occurred on 11 February. Using secondary meteorological data from NASA POWER, a Multiple Linear Regression (MLR) model was constructed with a stepwise selection method in MATLAB to determine the most significant predictors. The analysis revealed six significant variables: relative humidity, air pressure, wind speed, wind direction, temperature, and specific humidity. The model demonstrated strong predictive performance, achieving a coefficient of determination (R²) of 0.676 and a Root Mean Square Error (RMSE) of 11.265 mm. Pearson correlation analysis confirmed a strong linear relationship between observed and predicted rainfall values. Relative humidity, specific humidity, and wind speed showed positive correlations with rainfall intensity, while air pressure and temperature were negatively correlated, and wind direction exhibited negligible influence. These results indicate that atmospheric moisture and dynamic variables play a crucial role in triggering extreme rainfall events, while some factors such as temperature and wind direction have a lesser impact. The findings highlight the potential of using stepwise regression modeling as a tool for short-term rainfall forecasting and urban flood mitigation in tropical coastal environments. Extreme rainfall multiple linear regression stepwise model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6867645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477632578,"identity":"7dbba786-ef78-4bb4-829d-c5edb1a110df","order_by":0,"name":"Fahrezzy Arthur Ridho","email":"data:image/png;base64,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","orcid":"","institution":"Hasanuddin University","correspondingAuthor":true,"prefix":"","firstName":"Fahrezzy","middleName":"Arthur","lastName":"Ridho","suffix":""},{"id":477632579,"identity":"eb2c2f70-6b33-4819-bdc7-97da354b5afe","order_by":1,"name":"Bintiara Safitri","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Bintiara","middleName":"","lastName":"Safitri","suffix":""},{"id":477632580,"identity":"de0cb1ce-83a2-44bf-b377-8cf80c8e2ef0","order_by":2,"name":"Eldwy Heny Parrangan","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Eldwy","middleName":"Heny","lastName":"Parrangan","suffix":""},{"id":477632581,"identity":"bd3da8d5-485d-41bd-a7b8-48c113c766f0","order_by":3,"name":"nurfaisah nurfaisah","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"nurfaisah","middleName":"","lastName":"nurfaisah","suffix":""}],"badges":[],"createdAt":"2025-06-11 04:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6867645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6867645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86806247,"identity":"9f8fdd04-d37e-441d-bcf0-a459297fb1ec","added_by":"auto","created_at":"2025-07-15 18:16:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":601201,"visible":true,"origin":"","legend":"","description":"","filename":"ModelingAtmosphericVariablesInfluencingExtremeRainfallEventsinMakassarCityDuringFebruary2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6867645/v1_covered_3ac3dfa4-3e74-46e4-843a-273a7892f8d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling Atmospheric Variables Influencing Extreme Rainfall Events in Makassar City During February 2025","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Extreme rainfall, multiple linear regression, stepwise model","lastPublishedDoi":"10.21203/rs.3.rs-6867645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6867645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExtreme rainfall events have become increasingly frequent and intense in tropical urban areas, posing serious challenges for disaster management and early warning systems. This study aims to identify the key atmospheric variables influencing extreme rainfall intensity in Makassar City, Indonesia, during the period of 7\u0026ndash;15 February 2025, focusing on a major event that occurred on 11 February. Using secondary meteorological data from NASA POWER, a Multiple Linear Regression (MLR) model was constructed with a stepwise selection method in MATLAB to determine the most significant predictors. The analysis revealed six significant variables: relative humidity, air pressure, wind speed, wind direction, temperature, and specific humidity. The model demonstrated strong predictive performance, achieving a coefficient of determination (R\u0026sup2;) of 0.676 and a Root Mean Square Error (RMSE) of 11.265 mm. Pearson correlation analysis confirmed a strong linear relationship between observed and predicted rainfall values. Relative humidity, specific humidity, and wind speed showed positive correlations with rainfall intensity, while air pressure and temperature were negatively correlated, and wind direction exhibited negligible influence. These results indicate that atmospheric moisture and dynamic variables play a crucial role in triggering extreme rainfall events, while some factors such as temperature and wind direction have a lesser impact. 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