[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117197-en":3,"doc-seo-117197-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117197,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Creating Grid-Based Machine Learning Severe Weather Guidance for Watch-to-Warning Lead Times in the Warn-on-Forecast System - Thesis","This thesis develops grid-based machine learning guidance to improve watch-to-warning lead times within the Warn-on-Forecast (WoFS) system. It builds a workflow spanning dataset construction, feature engineering from WoFS ensemble forecasts, and several baseline and advanced modeling approaches. Traditional machine learning and deep learning methods are trained and evaluated with verification metrics, including objective skill, case studies, stratified verification by initialization time, and feature ablation to quantify contributions across severe hazards.","UNIVERSITY OF OKLAHOMA  \nGRADUATE COLLEGE  \nCREATING GRID-BASED MACHINE LEARNING SEVERE WEATHER GUIDANCE FOR WATCH-TO-WARNING LEAD TIMES IN THE WARN-ON-FORECAST SYSTEM  \nA THESIS  \nSUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the Degree of  \nMASTER OF SCIENCE  \nBy  \nSAMUEL VARGA  \nNorman, Oklahoma  \n2024  \nCREATING GRID-BASED MACHINE LEARNING SEVERE WEATHER GUIDANCE FOR WATCH-TO-WARNING LEAD TIMES IN THE  \nWARN-ON-FORECAST SYSTEM  \nA THESIS APPROVED FOR THE  \nSCHOOL OF METEOROLOGY  \nBY THE COMMITTEE CONSISTING OF  \nDr. Corey Potvin, Chair  \nDr. Montgomery Flora  \nDr. Aaron Hill  \nDr. Cameron Homeyer  \n© Copyright by SAMUEL VARGA 2024 All Rights Reserved.  \nAcknowledgments  \nTo begin, I thank my research advisors: Dr. Corey Potvin and Dr. Montgomery Flora. Throughout my two years at the University of Oklahoma, they have continued to challenge me to grow professionally and academically. Their continuous support and guidance have been instrumental to my success and growth over the last two years. I also thank the members of the WoFS team for welcoming me and for all of the opportunities they have provided. The ability to consult with them has been paramount to both my growth and the success of this project. Quite literally, this project would not be possible without their hard work. Finally, I thank my family and friends for their support. They may not have understood what I was saying, yet they listened all the same.  \nThis material is based upon work supported by the Joint Technology Transfer Initiative Program within the NOAA/OAR Weather Program Office under Award No. NA22OAR4590171 .  \nTable of Contents  \nAcknowledgments iv  \nList Of Tables vii  \nList Of Figures viii Abstract xiii 1 Introduction 1  \n2 Literature Review 3  \n2.1 Watch-to-Warning Guidance ........................ 3  \n2.2 Machine Learning for Severe Weather ................... 5  \n3 Data & Methods 10  \n3.1 Warn-on-Forecast System ......................... 10  \n3.2 Dataset ................................... 11  \n3.2.1 Warn-on-Forecast Datasets ..................... 11  \n3.2.2 Feature Engineering ........................ 13  \n3.2.3 Target Data ............................. 15  \n3.3 Baseline and Machine Learning ...................... 18  \n3.3.1 Baseline Models ........................... 18  \n3.3.2 Logistic Regression ......................... 19  \n3.3.3 Tree-based Machine Learning ................... 20  \n3.3.4 Convolutional Neural Networks and U-nets ............ 22  \n3.4 Deep Learning Methods .......................... 28  \n3.5 Verification Metrics ............................. 29  \n4 Results 34  \n4.1 Performance of Traditional Machine Learning Techniques ........ 34  \n4.1.1 Objective Skill ........................... 34  \n4.1.2 Case Study ............................. 37  \n4.2 Stratified Verification ............................ 46  \n4.2.1 Skill by Initialization Time ..................... 46  \n4.2.2 Impact of Removing Cases ..................... 48  \n4.3 Feature Ablation .............................. 56  \n4.4 Performance of Models on Final Dataset ................. 62  \n4.4.1 Objective Skill ........................... 62  \n4.4.2 Case Study ............................. 63  \n4.5 Performance of Deep Learning Techniques ................ 71  \n4.5.1 Objective Skill ........................... 71  \n4.5.2 Case Study ............................. 72  \n5 Conclusions and Summary 77  \n5.1 Discussion .................................. 77  \n5.2 Summary and Future Work ........................ 80  \nReference List 85  \nList Of Tables  \n3. 1 Fields extracted from WoFS ensemble forecasts and used to create features. Fields 1-8 are intrastorm while the remaining fields are classed as environmental. The ensemble statistics for each field type are also listed. Each statistic is calculated three times per field due to the three  \ndifferent smoothing radii, resulting in 174 predictor fields   14  \n3.2 The standard 2x2 contingency table, also k","cbCaigEVD3fygWYP","https://ap.wps.com/l/cbCaigEVD3fygWYP","pdf",11463058,1,102,"English","en",105,"# Acknowledgments\n# List Of Tables\n# List Of Figures\n# Abstract\n# Introduction\n# Literature Review\n## Watch-to-Warning Guidance\n## Machine Learning for Severe Weather\n# Data & Methods\n## Warn-on-Forecast System\n## Dataset\n### Warn-on-Forecast Datasets\n### Feature Engineering\n### Target Data\n## Baseline and Machine Learning\n### Baseline Models\n### Logistic Regression\n### Tree-based Machine Learning\n### Convolutional Neural Networks and U-nets\n## Deep Learning Methods\n## Verification Metrics\n# Results\n## Performance of Traditional Machine Learning Techniques\n## Stratified Verification\n## Feature Ablation\n## Performance of Models on Final Dataset\n## Performance of Deep Learning Techniques\n# Conclusions and Summary\n## Discussion\n## Summary and Future Work\n# Reference List","[{\"question\":\"What problem does the thesis address in severe weather forecasting?\",\"answer\":\"It targets improved watch-to-warning lead times by generating guidance using grid-based machine learning inside the Warn-on-Forecast (WoFS) system.\"},{\"question\":\"How is the dataset constructed for model training and evaluation?\",\"answer\":\"The work uses WoFS ensemble forecasts to create features, defines target data for the warning-related guidance, and compiles datasets spanning initial and final case sets.\"},{\"question\":\"What verification approaches are used to assess model performance?\",\"answer\":\"Performance is evaluated using objective skill measures, case studies, stratified verification by initialization time, feature ablation studies, and metrics suitable for probabilistic verification.\"}]","Creating Grid-Based Machine Learning Severe Weather Guidance for Watch-to-Warning Lead Times in the Warn-on-Forecast System - Thesis | PDF",1785674374,257,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"creating-grid-based-machine-learning-severe-weather-guidance-for-watch-to-warning-lead-times-in-the-warn-on-forecast-system-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/creating-grid-based-machine-learning-severe-weather-guidance-for-watch-to-warning-lead-times-in-the-warn-on-forecast-system-thesis/117197/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in severe weather forecasting?","Question",{"text":75,"@type":76},"It targets improved watch-to-warning lead times by generating guidance using grid-based machine learning inside the Warn-on-Forecast (WoFS) system.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset constructed for model training and evaluation?",{"text":80,"@type":76},"The work uses WoFS ensemble forecasts to create features, defines target data for the warning-related guidance, and compiles datasets spanning initial and final case sets.",{"name":82,"@type":73,"acceptedAnswer":83},"What verification approaches are used to assess model performance?",{"text":84,"@type":76},"Performance is evaluated using objective skill measures, case studies, stratified verification by initialization time, feature ablation studies, and metrics suitable for probabilistic verification.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]