Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System | 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 Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System Haoxuan Yuan, Rahat Ihsan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1214869/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 Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a super-resolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the pre-collected data of model weather radar echo patches. Second, the most relevant sub-dictionaries are adaptively select for each low-resolution echo patches during the spare coding using a complex decision support system. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics. Geometry Topology Theoretical Computer Science weather radar data processing sparse representation decision support system regularization Full Text 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-1214869","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":74655224,"identity":"e6a115ab-f81d-4780-9863-a3d7d4cbc6a8","order_by":0,"name":"Haoxuan Yuan","email":"","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Haoxuan","middleName":"","lastName":"Yuan","suffix":""},{"id":74655225,"identity":"5d8ac6d1-7d62-442d-b714-3fa197b17419","order_by":1,"name":"Rahat Ihsan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYFACHiA2OAAkmI8xNoBFEoD4AAEtB8Ba2NJI0QJWwWNGnBbz9rMHP38ouGPPP7vn28OZbXYM/Ow5BswFZ3BrkTmTlyxxwOBZ4ow7Z7cbbmxLZpDseWPAPOMGbi0SDDkGQC2HExhu5G6TfNh2gMHgBtAWng94tPC/Mf4B1GIvfyPnGViLPUEtEjlmIFsYN9zIYZPcCLJFAqQFn8Mk3phZnDE4nLjxRpq54YxzyTwSZ54VHJ6Bx/sS/DnGNyr+HLaXu5H87GFPmZ0cf3vyxscFx3BrwQCgxMBwmAQNUMBMupZRMApGwSgYxgAA2TVZrIuWs8AAAAAASUVORK5CYII=","orcid":"","institution":"Bacha Khan University Charsadda","correspondingAuthor":true,"prefix":"","firstName":"Rahat","middleName":"","lastName":"Ihsan","suffix":""}],"badges":[],"createdAt":"2021-12-29 20:22:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1214869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1214869/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17105972,"identity":"2558139b-9226-495f-8c6c-a09563e4729f","added_by":"auto","created_at":"2022-01-07 16:01:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":947077,"visible":true,"origin":"","legend":"","description":"","filename":"RahatWeatherRadar.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1214869/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1214869/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"weather radar, data processing, sparse representation, decision support system, regularization","lastPublishedDoi":"10.21203/rs.3.rs-1214869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1214869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a super-resolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the pre-collected data of model weather radar echo patches. Second, the most relevant sub-dictionaries are adaptively select for each low-resolution echo patches during the spare coding using a complex decision support system. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for \u0026times;2 and \u0026times;4 reconstruction in terms of both visual quality and quantitative evaluation metrics.\u003c/p\u003e","manuscriptTitle":"Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-07 16:01:19","doi":"10.21203/rs.3.rs-1214869/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":"9664a963-76e1-42a8-b6bb-47901a348de0","owner":[],"postedDate":"January 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":9587880,"name":"Geometry"},{"id":9587881,"name":"Topology"},{"id":9587882,"name":"Theoretical Computer Science"}],"tags":[],"updatedAt":"2022-01-21T16:54:40+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-07 16:01:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1214869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1214869","identity":"rs-1214869","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}