Predictability of fog using surface observation-based machine learning models: evaluation at Argentinean airport

Predictability of fog using surface observation-based machine learning models: evaluation at Argentinean airport | 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 Predictability of fog using surface observation-based machine learning models: evaluation at Argentinean airport Melina Sol Yabra, Juan Jose Ruiz, Ramon de Elia, Luciano Vidal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8503721/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 Low visibility conditions due to the presence of fog can lead to delays or cancellations of arriving and departing flights to avoid possible incidents or accidents at airports. Therefore, accurate visibility forecasts are required to keep airport capacity as high as possible. Recently, some studies explored the use of machine learning algorithms and routine surface meteorological observations to produce fog forecasts. This study explores the potential of machine learning (ML) models trained solely on surface observations for short-term fog nowcasting at Ezeiza International Airport in Argentina, within 6 hours from initialization time. Three simple ML methods—a logistic regression, a decision tree, and a random forest—were evaluated using 20 years of data. Results show that nonlinear methods (decision tree and random forest) required fewer, simpler predictors to match or exceed the forecasting skill of the logistic regression, which relied more heavily on complex variable transformations. All methods significantly outperformed climatological and persistence-conditional reference models. In all three methods, the most important predictor was the fog condition at initialization time. A seasonal and daily data-splitting strategy improved performance at almost all forecasting time horizons. The models trained in this study represent a promising operational tool for aviation forecasting and could also complement operational numerical models, whose ability to resolve fog-scale processes remains limited. fog forecast surface observations machine learning aviation Argentina 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-8503721","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577611503,"identity":"79a9289d-99af-4512-b6a3-7cf48c634f05","order_by":0,"name":"Melina Sol Yabra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYFAC5oYDUBYbENsQo4URRUsacVpgLJCWw4Q16LY3Nh4uqGGwZ5BIfva4ouZ8Hn//4QeMXypw6zU7c7Dh8IxjDIkNEmnmhmeO3S6WuJFmwCxzBo+WG4kNh3nYGBIYJBLMJBvYbic23GAwYJZsw+0ps/sPgVr+gRyW/k2y4d+5xPnnj3/Ar+UGY8Nh3jZgIEjkmEk2th1I3HAgx4DxYxvu0DY7A3QYb59EYhvPm3LDxr7kxI03cgoOM5zBo+X44cOfeb7Z2POzp2972PDNLnHe+eMbH/6okMCpBQokGNgEEhDcwzyENIAB/wEEm/EHUVpGwSgYBaNghAAAFtxaACDq0RMAAAAASUVORK5CYII=","orcid":"","institution":"National Aeronautics and Space Administration","correspondingAuthor":true,"prefix":"","firstName":"Melina","middleName":"Sol","lastName":"Yabra","suffix":""},{"id":577611504,"identity":"b1c31eb4-28d8-4b7c-b3c2-67c92ae5d1b4","order_by":1,"name":"Juan Jose Ruiz","email":"","orcid":"","institution":"National Scientific and Technical Research Council","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Jose","lastName":"Ruiz","suffix":""},{"id":577611505,"identity":"4e84d8b9-d40e-4500-98c8-f1a3c04f3cc9","order_by":2,"name":"Ramon de Elia","email":"","orcid":"","institution":"National Meteorological Service","correspondingAuthor":false,"prefix":"","firstName":"Ramon","middleName":"","lastName":"de Elia","suffix":""},{"id":577611506,"identity":"4488a906-8a0f-4794-841e-9ecb5594745b","order_by":3,"name":"Luciano Vidal","email":"","orcid":"","institution":"National Meteorological Service","correspondingAuthor":false,"prefix":"","firstName":"Luciano","middleName":"","lastName":"Vidal","suffix":""}],"badges":[],"createdAt":"2026-01-03 00:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8503721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8503721/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297988,"identity":"64deed3e-691b-4113-8e3e-e0a7d986225b","added_by":"auto","created_at":"2026-02-10 10:30:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2371265,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8503721/v1_covered_c041d74e-0f44-42b3-a873-5b3eb4f5cfe0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictability of fog using surface observation-based machine learning models: evaluation at Argentinean airport","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":"fog, forecast, surface observations, machine learning, aviation, Argentina","lastPublishedDoi":"10.21203/rs.3.rs-8503721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8503721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLow visibility conditions due to the presence of fog can lead to delays or cancellations of arriving and departing flights to avoid possible incidents or accidents at airports. Therefore, accurate visibility forecasts are required to keep airport capacity as high as possible. Recently, some studies explored the use of machine learning algorithms and routine surface meteorological observations to produce fog forecasts. This study explores the potential of machine learning (ML) models trained solely on surface observations for short-term fog nowcasting at Ezeiza International Airport in Argentina, within 6 hours from initialization time. Three simple ML methods\u0026mdash;a logistic regression, a decision tree, and a random forest\u0026mdash;were evaluated using 20 years of data. Results show that nonlinear methods (decision tree and random forest) required fewer, simpler predictors to match or exceed the forecasting skill of the logistic regression, which relied more heavily on complex variable transformations. All methods significantly outperformed climatological and persistence-conditional reference models. In all three methods, the most important predictor was the fog condition at initialization time. A seasonal and daily data-splitting strategy improved performance at almost all forecasting time horizons. The models trained in this study represent a promising operational tool for aviation forecasting and could also complement operational numerical models, whose ability to resolve fog-scale processes remains limited.\u003c/p\u003e","manuscriptTitle":"Predictability of fog using surface observation-based machine learning models: evaluation at Argentinean airport","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 07:05:30","doi":"10.21203/rs.3.rs-8503721/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"4dbef85b-fece-4a50-8c65-8d644487158a","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T07:05:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 07:05:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8503721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8503721","identity":"rs-8503721","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}