[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121855-en":3,"doc-seo-121855-105":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},121855,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning Applications for Weather and Climate Modeling - A Dissertation","This dissertation investigates machine learning (ML) applications to weather and climate modeling. It develops a low-resolution ML-based global atmospheric model for 3D prediction, demonstrating stability over 21-day forecasts and skill beyond persistence and climatology for midlatitude leads. Compared with a simplified AGCM, the ML-only approach performs best for AGCM variables strongly driven by parameterizations. A hybrid ML-parallel algorithm with SPEEDY improves forecasts through at least day 7 and enables an 11-year free-run with reduced systematic errors and more realistic variability. Finally, the work couples a hybrid atmospheric model with a ML ocean model to simulate long-term atmosphere-ocean variability such as ENSO with no climate drift and conservation of atmospheric and water vapor mass over 70 years.","MACHINE LEARNING APPLICATIONS FOR WEATHER AND CLIMATE MODELING  \nA Dissertation  \nby  \nTROY JOSEPH ARCOMANO  \nSubmitted to the Office of Graduate and Professional Studies of Texas A&M University  \nin partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nChair of Committee, Committee Members,  \nHead of Department,  \nIstvan Szunyogh Ramalingam Saravanan Craig Epifanio  \nPing Chang Ramalingam Saravanan  \nDecember 2022  \nMajor Subject: Atmospheric Sciences  \nCopyright 2022 Troy Joseph Arcomano  \nABSTRACT  \nThis study investigates the applications of machine learning (ML) to weather and climate modeling. We first show the potential for data-driven weather prediction by creating a low resolution, ML-based global atmospheric model that predicts the 3-dimensional atmosphere in the same format as a physic-based numerical model. The ML-only atmospheric model is stable during 21-day forecasts and can reproduce large-scale atmospheric dynamics (e.g. Rossby waves) . The ML-only model is able to outperform persistence and climatology for the first three forecast days in the midlatitudes. When compared to a simplified atmospheric general circulation model (AGCM), the ML-only model performs best for variables most heavily influenced by parameterizations in the AGCM (e.g. low level specific humidity) .  \nNext, we combine a parallel, machine learning algorithm with a coarse resolution AGCM (SPEEDY) to create a hybrid atmospheric model. The hybrid model produces more accurate forecasts for all variables for at least the first 7 forecast days when compared to the host AGCM. Applications of the hybrid model for climate research are explored with a 11-year free run. The hybrid model is free of instability and can simulate the past climate with substantially smaller systematic errors and more realistic variability than the host AGCM.  \nLastly, we show potential of ML for Earth System modeling by dynamically coupling a hybrid atmospheric model and a ML-based ocean model trained to predict the sea surface temperature (SST) . The ML-only ocean model is able to reproduce SST dynamics with minimal biases for the past and present climate. The coupled model can simulate long-term variability in both the atmosphere and ocean (e.g. El Niño–Southern Oscillation) . During a 70-year free run, we find that the coupled model does not exhibit climate drift and able to conserve total atmospheric mass and  \nwater vapor mass.  \nDEDICATION  \nI dedicate this dissertation to my loving wife and parents. Without your support I would not be  \nwhere I am today.  \nACKNOWLEDGEMENTS  \nI would like to first thank my committee chair and advisor, Dr. Istvan Szunyogh, for all of his support, guidance, and help during my graduate studies. He challenged me to be a better scientist and without him I would not be where I am today.  \nI would also like to thank my committee members: Dr. Ping Chang, Dr. Craig Epifanio, and Dr. Ramalingam Saravanan, for providing valuable and insightful suggestions on my research and the dissertation.  \nLastly, I want to thank my friends and family. Rachel, my wife, I am forever grateful for all of your support. Whether it was taking care of the dogs when I had to work late nights at the office or helping me proofread papers, you were always there for me thank you. I thank my parents for always believing me and encouraging me to pursue a career in atmospheric science. I would also like to thank my friends I’ve met at the Texas A&M; Dr. Kyle Wodzicki, Dr. Kevin Smalley, Judy Dickey, and many more.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supported by a dissertation committee consisting of Professors Istvan Szunyogh, Ramalingam Saravanan, and Craig Epifanio of the Department of Atmospheric Sciences and Professor Ping Chang of the Department of Oceanography.  \nThe studies shown in Section 2, 3, and 4 were conducted in collaboration with professors and graduate students from the University of Maryland (UMD","cbCairUnmUYjdROV","https://ap.wps.com/l/cbCairUnmUYjdROV","pdf",7905393,1,103,"English","en",105,"# Abstract\n# Dedication\n# Acknowledgements\n# Contributors and Funding Sources\n# Nomenclature\n# Table of Contents","[{\"question\":\"What is the main goal of this dissertation?\",\"answer\":\"The dissertation studies how machine learning can be applied to weather and climate modeling, including atmospheric and coupled Earth system simulations.\"},{\"question\":\"How does the ML-only atmospheric model perform compared with simpler baselines?\",\"answer\":\"The ML-only model is stable during 21-day forecasts and outperforms persistence and climatology for the first three forecast days in the midlatitudes.\"},{\"question\":\"What improvements does the hybrid atmospheric model provide?\",\"answer\":\"The hybrid model combines a parallel ML algorithm with a coarse-resolution AGCM (SPEEDY) and yields more accurate forecasts for all variables for at least the first seven forecast days, with reduced errors and realistic variability in an 11-year free run.\"}]","Machine Learning Applications for Weather and Climate Modeling - 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