[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127963-en":3,"doc-seo-127963-105":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},127963,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting - Graduate Thesis","This graduate thesis evaluates baseline Global Forecast System (GFS) skill for forecasting borderline and bomb middle-latitude rapid cyclogenesis events and tests whether machine-learning post-processing can enhance prediction quality. Case lists for October–March, 2008–2021 are built using Tempest Extreme cyclone tracking and ERA5 analyses. GFS 24-hour base-state variables in 10°×10° cyclone-center subdomains are compressed via S-mode PCA, followed by genetic-algorithm selection of optimal predictors. Logistic regression and SVM reduce bias, but only logistic regression improves forecast skill beyond the GFS baseline.","Mississippi State University  \nScholars Junction  \n\n| Theses and Dissertations | Theses and Dissertations |\n| --- | --- |\n| 8-13-2024\u003Cbr>Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting\u003Cbr>Colin Matthew Snyder\u003Cbr>Mississippi State University, [colinmsnyder@gmail.com](colinmsnyder@gmail.com)\u003Cbr>Follow this and additional works at: [https://scholarsjunction.msstate.edu/td](https://scholarsjunction.msstate.edu/td) |  |\n\nRecommended Citation  \nSnyder, Colin Matthew, \"Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting\" (2024) . Theses and Dissertations. 6237.  \n[https://scholarsjunction.msstate.edu/td/6237](https://scholarsjunction.msstate.edu/td/6237)  \nThis Graduate Thesis-Open Access is brought to you for free and open access by the Theses and Dissertations at Scholars Junction. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholars Junction. For more information, please contact scholcomm@msstate. libanswers.com.  \nApplication of numerical weather prediction with machine learning techniques to improve middle latitude  \nrapid cyclogenesis forecasting  \nBy  \nColin Matthew Snyder  \nApproved by:  \nAndrew E. Mercer (Major Professor/Graduate Coordinator)  \nJamie L. Dyer  \nJohna Rudzin  \nRick Travis (Dean, College of Arts & Sciences)  \nA Thesis  \nSubmitted to the Faculty of  \nMississippi State University  \nin Partial Fulfillment of the Requirements  \nfor the Degree of Master of Science  \nin Geoscience (Professional Meteorology/Climatology  \nin the Department of Geosciences  \nMississippi State, Mississippi  \nCopyright by Colin Matthew Snyder 2024  \nName: Colin Matthew Snyder  \nDate of Degree: August 8, 2024  \nInstitution: Mississippi State University  \nMajor Field: Geoscience (Professional Meteorology/Climatology  \nMajor Professor: Andrew E. Mercer  \nTitle of Study: Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting  \nPages in Study: 54  \nCandidate for Degree of Master of Science  \nThis study goal was to first determine the baseline Global Forecast System (GFS) skill in forecasting borderline (non-bomb:0.75-0.95, bomb: 1.-1.25) bomb events, and second to determine if machine learning (ML) techniques as a post-processor can improve the forecasts. This was accomplished by using the Tempest Extreme cyclone tracking software and ERA5 analysis to develop a case list during the period of October to March for the years 2008-2021. Based on the case list, GFS 24-hour forecasts of atmospheric base state variables in 10-degree by 10-degree cyclone center subdomains was compressed using S-mode Principal Component Analysis. A genetic algorithm was then used to determine the best predictors. These predictors were then used to train a logistic regression as a baseline ML skill and a Support Vector Machine (SVM) model. Both the logistic regression and SVM provided an improved bias over the GFS baseline skill, but only the logistic regression improved skill.  \nACKNOWLEDGEMENTS  \nI would like to thank Dr. Johna Rudzin and Dr. Jamie Dyer for being a part of my committee and being willing to provide helpful questions and comments that greatly improved my thesis. I especially thank my advisor, Dr. Andrew Mercer for his willingness to share his knowledge and experience and always patient guidance over the last two years.  \nFinally, I’d like to thank my friends and fellow graduate students for their support over many long days and nights of coding and writing.  \nTABLE OF CONTENTS  \nACKNOWLEDGEMENTS ............................................................................................................ ii  \nLIST OF TABLES ...........................................................................................................................v  \n[LIST OF F","cbCaibx63X5xLfzF","https://ap.wps.com/l/cbCaibx63X5xLfzF","pdf",1817108,2,1,64,"English","en",105,"# Acknowledgements\n# Table of Contents\n# Chapter I. Introduction\n## Context/Motivation\n## Background\n## Research Objectives\n# Chapter II. Data and Methods\n## Data\n## Global Forecast System","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"The study first assesses baseline GFS skill for borderline and bomb cyclogenesis events, then determines whether machine-learning post-processing can improve those forecasts.\"},{\"question\":\"How were the cyclone cases and training data constructed?\",\"answer\":\"Cases from October to March for 2008–2021 are compiled using Tempest Extreme cyclone tracking and ERA5 analysis, creating a case list for model development.\"},{\"question\":\"Which machine-learning models were tested and what were the outcomes?\",\"answer\":\"Logistic regression and a Support Vector Machine (SVM) were trained using the selected predictors. Both improved bias compared with the GFS baseline, but only logistic regression improved overall forecast skill.\"}]","Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting - Graduate Thesis | PDF",1785943340,161,{"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},"application-of-numerical-weather-prediction-with-machine-learning-techniques-to-improve-middle-latitude-rapid-cyclogenesis-forecasting-graduate-thesis","",{"@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/application-of-numerical-weather-prediction-with-machine-learning-techniques-to-improve-middle-latitude-rapid-cyclogenesis-forecasting-graduate-thesis/127963/",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","2026-08-25","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},"What is the main objective of this study?","Question",{"text":76,"@type":77},"The study first assesses baseline GFS skill for borderline and bomb cyclogenesis events, then determines whether machine-learning post-processing can improve those forecasts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the cyclone cases and training data constructed?",{"text":81,"@type":77},"Cases from October to March for 2008–2021 are compiled using Tempest Extreme cyclone tracking and ERA5 analysis, creating a case list for model development.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine-learning models were tested and what were the outcomes?",{"text":85,"@type":77},"Logistic regression and a Support Vector Machine (SVM) were trained using the selected predictors. 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