[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127322-en":3,"doc-seo-127322-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127322,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Center Fixing Tropical Depressions and Tropical Storms Using Machine Learning - Nighttime Visible Imagery","The first step in tropical cyclone (TC) retrieval algorithms is determining the storm’s central position, yet satellite-based centers can be obscured during intensifying and decaying phases by thick clouds or overlying cirrus. Microwave imagery helps but often suffers discontinuous coverage from polar-orbiting sensors across the TC life cycle. This study evaluates machine learning-derived Nighttime Visible Imagery (NVI) to improve center fix accuracy for tropical depression (TD)- and tropical storm (TS)-strength systems in darkness. NVI is generated from Himawari channels 7, 11, 13, and 15 with brightness temperature differences mapped to lunar reflectance comparable to DNB measurements.","Air Force Institute of Technology  \nAFIT Scholar  \n\n| Theses and Dissertations | Student Graduate Works |\n| --- | --- |\n| 3-2024\u003Cbr>Center Fixing Tropical Depressions and Tropical Storms Using Machine Learning-Nighttime Visible Imagery\u003Cbr>Nathan K. Stanford\u003Cbr>Follow this and additional works at: [https://scholar.afit.edu/etd](https://scholar.afit.edu/etd)\u003Cbr> Part of the Meteorology Commons |  |\n\nRecommended Citation  \nStanford, Nathan K., \"Center Fixing Tropical Depressions and Tropical Storms Using Machine LearningNighttime Visible Imagery\" (2024) . Theses and Dissertations. 7787.  \n[https://scholar.afit.edu/etd/7787](https://scholar.afit.edu/etd/7787)  \nThis Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, [please contact](please contact AFIT.ENWL.Repository@us.af.mil)[ AFIT.ENWL.Repository@us.af.mil](please contact AFIT.ENWL.Repository@us.af.mil).  \nCENTER FIXING TROPICAL DEPRESSIONSAND TROPICAL STORMS USING MACHINE LEARNING-NIGHTTIME VISIBLE  \nIMAGERY  \nTHESIS  \nNathan K. Stanford, Captain, USAF  \nAFIT-METG-THESIS  \nDEPARTMENT OF THE AIR FORCE  \nAIR UNIVERSITY  \nAIR FORCE INSTITUTE OF TECHNOLOGY  \nWright-Patterson Air Force Base, Ohio  \nAPPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED  \nThe views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.  \nAFIT-METG-THESIS  \nCENTER FIXING TROPICAL DEPRESSIONS AND TROPICAL STORMS USING MACHINE LEARNING-NIGHTTIME VISIBLE IMAGERY  \nTHESIS  \nPresented to the Faculty  \nDepartment of Engineering Physics Graduate School of Engineering and Management Air Force Institute of Technology  \nAir University  \nAir Education and Training Command in Partial Fulfillment of the Requirements for the Degree of Master of Science in Meteorology  \nNathan K. Stanford, B.S.M. , M.S.P.M.  \nCaptain, USAF  \nMarch 4, 2024  \nAPPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED  \nAFIT-METG-THESIS  \nCENTER FIXING TROPICAL DEPRESSIONS AND TROPICAL STORMS USING MACHINE LEARNING-NIGHTTIME VISIBLE IMAGERY  \nTHESIS  \nNathan K. Stanford, B.S.M. , M.S.P.M.  \nCaptain, USAF  \nCommittee Membership:  \nChandra M. Pasillas, Ph.D  \nChair  \nAnthony J. Wimmers, Ph.D  \nMember  \nMatthew F. Horan, Ph.D  \nMember  \nAFIT-METG-THESIS  \nAbstract  \nThe first step in most tropical cyclone (TC)-retrieval algorithms is determining the storm’s central position. When viewed in satellite imagery, mature TC centers are often often highlighted by a distinct eye and curved band pattern; however, in intensifying and decaying storms, the center is often obscured by thick clouds or overlying cirrus. Generally, microwave imagery addresses this challenge; however, microwave sensors aboard polar orbiting satellites provide discontinuous coverage throughout the TC life cycle; therefore, analysts rely on geostationary satellite imagery for continuous storm coverage. This study assesses the benefits of incorporating machine learningderived nighttime visible imagery to improve center fix accuracy in intensifying and decaying tropical depression (TD)-and tropical storm (TS)-strength TCs during periods of darkness. Machine Learning-Nighttime Visible Imagery (NVI) incorporates Himawari Channels 7 (3.9µm), 11 (8.6µm), 13 (10.4µm), and 15 (12.4µm), as well as their brightness temperature differences, to create lunar reflectance comparable to the lunar reflectance values derived from Day/Night Band (DNB) measured radiances.  \nThe study is divided into two parts: the first, an objective analysis using the Automated Rotational Center Hurricane Eye Retrieval (ARCHER-2) algorithm, and the second, a subjective imag","cbCaiq2MZQ2YjBOL","https://ap.wps.com/l/cbCaiq2MZQ2YjBOL","pdf",9468669,2,1,72,"English","en",105,"# Abstract\n# Background and Literature Review\n## Tropical Cyclone Satellite Scenes\n## Objective Center Fixing\n## Radiative Transfer\n## Machine Learning\n# Methodology","[{\"question\":\"Why is center fixing difficult for tropical cyclones during intensifying and decaying stages?\",\"answer\":\"Mature centers may be obscured by thick clouds or cirrus, making the central position hard to identify in satellite imagery. The study highlights this limitation and the need for better nighttime-aware approaches.\"},{\"question\":\"What is Nighttime Visible Imagery (NVI) in this thesis?\",\"answer\":\"NVI is machine learning-derived imagery that uses Himawari channels 7, 11, 13, and 15 plus brightness temperature differences to produce lunar reflectance comparable to Day/Night Band (DNB) measurements.\"},{\"question\":\"How does the proposed approach improve center fix accuracy?\",\"answer\":\"In the objective analysis using ARCHER-2, NVI improved center fix assessments by 21.9 km versus SWIR and by 64.5 km versus LWIR. In the subjective analysis, analysts favored SWIR by 30.9 km based on 7 Western Pacific TC image sets.\"}]","Center Fixing Tropical Depressions and Tropical Storms Using Machine Learning - Nighttime Visible Imagery | PDF",1785938291,181,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"center-fixing-tropical-depressions-and-tropical-storms-using-machine-learning-nighttime-visible-imagery","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/center-fixing-tropical-depressions-and-tropical-storms-using-machine-learning-nighttime-visible-imagery/127322/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is center fixing difficult for tropical cyclones during intensifying and decaying stages?","Question",{"text":76,"@type":77},"Mature centers may be obscured by thick clouds or cirrus, making the central position hard to identify in satellite imagery. The study highlights this limitation and the need for better nighttime-aware approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is Nighttime Visible Imagery (NVI) in this thesis?",{"text":81,"@type":77},"NVI is machine learning-derived imagery that uses Himawari channels 7, 11, 13, and 15 plus brightness temperature differences to produce lunar reflectance comparable to Day/Night Band (DNB) measurements.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed approach improve center fix accuracy?",{"text":85,"@type":77},"In the objective analysis using ARCHER-2, NVI improved center fix assessments by 21.9 km versus SWIR and by 64.5 km versus LWIR. In the subjective analysis, analysts favored SWIR by 30.9 km based on 7 Western Pacific TC image sets.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]