[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125745-en":3,"doc-seo-125745-105":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},125745,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Improving The Modeling and Analysis of Tropical Convection and Precipitation through Machine Learning Methods - Dissertation","This dissertation improves modeling and analysis of tropical convection and precipitation using machine learning methods. It develops neural-network emulations for sub-grid parameterizations, evaluates spatial and temporal variability, and performs formal hyperparameter tuning with attention to physical constraints such as the diurnal cycle. It further investigates generative modeling of atmospheric convection via architectures like VAEs, then compares storm-resolving models and climates using clustering and latent-space analysis to yield physically interpretable convection structures.","UC Irvine  \nUC Irvine Electronic Theses and Dissertations  \nTitle  \nImproving The Modeling and Analysis of Tropical Convection and Precipitation through Machine Learning Methods  \nPermalink  \n[https://escholarship.org/uc/item/3sz018pc](https://escholarship.org/uc/item/3sz018pc)  \nAuthor  \nMooers, Griffin Stuart  \nPublication Date  \n2023  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, availalbe at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA,  \nIRVINE  \nImproving The Modeling and Analysis of Tropical Convection and Precipitation through  \nMachine Learning Methods  \nDISSERTATION  \nsubmitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin Earth System Science  \nby  \nGriffin Mooers  \nDissertation Committee:  \nAssociate Professor Mike Pritchard, Chair Associate Professor Stephan Mandt Assistant Professor Tom Beucler Professor Jin-Yi Yu  \nProfessor Jim Randerson  \n© 2023 Griffin Mooers  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES v  \nLIST OF TABLES xiv  \nACKNOWLEDGMENTS xvii  \nVITA xix  \nABSTRACT OF THE DISSERTATION xxi  \n1 Introduction 1  \n1.1 Background .................................... 1  \n1.1.1 Our Cloud-Climate Deadlock ...................... 1  \n1.1.2 The Tropical Atmosphere ........................ 3  \n1.1.3 Machine Learning ............................. 5  \n1.1.4 Outline .................................. 8  \n2 Neural-Network Emulation of Sub-grid Parameterizations 12  \n2.1 Abstract ...................................... 12  \n2.2 Introduction .................................... 13  \n2.3 Methods ...................................... 16  \n2.3.1 Climate Simulation Data ......................... 16  \n2.3.2 Neural Network Design .......................... 19  \n2.3.3 Performance Analysis and Postprocessing ................ 20  \n2.3.4 Formal Hyperparameter Tuning ..................... 24  \n2.4 Results ....................................... 27  \n2.4.1 Spatial Structures ............................. 28  \n2.4.2 Temporal Variability ........................... 32  \n2.4.3 Hyperparameter Optimization vs. Physical Constraints for Emulating the Diurnal Cycle ............................. 35  \n2.4.4 Towards Interactive Land Coupling ................... 40  \n2.5 Conclusion .................................... 42  \n2.6 Appendix A: Performance Comparison with Existing Literature ....... 46  \n2.7 Appendix B: Supporting Tables, Figures, and Movies ............. 46  \n3 Generative Modeling of Atmospheric Convection 66  \n3.1 Abstract ...................................... 66  \n3.2 Introduction .................................... 67  \n3.3 Methods ...................................... 69  \n3.3.1 Architecture ................................ 70  \n3.3.2 VAE Loss Implementation ........................ 70  \n3.3.3 Data & Preprocessing .......................... 72  \n3.3.4 Quantifying Reconstruction Performance ................ 74  \n3.4 Results ....................................... 77  \n3.5 Conclusion ..................................... 79  \n3.6 Appendix A: Additional Figures ......................... 80  \n4 Comparing Storm Resolving Models and Climates 82  \n4.1 Abstract ...................................... 82  \n4.2 Introduction .................................... 83  \n4.3 Methods ...................................... 85  \n4.3.1 Data and Preprocessing ......................... 85  \n4.3.2 Variational Autoencoders ......................... 88  \n4.3.3 Understanding Convection via Vertical Structure ........... 90  \n4.3.4 The Horizontal Extent of Convection .................. 90  \n4.3.5 K-Means Clustering of Tropical Convection ............... 91  \n4.3.6 Vector Quantization ............","cbCaijUjn5TZIRAT","https://ap.wps.com/l/cbCaijUjn5TZIRAT","pdf",17949277,1,198,"English","en",105,"# Introduction\n## Background\n## Outline\n# Neural-Network Emulation of Sub-grid Parameterizations\n## Methods\n## Results\n## Conclusion\n# Generative Modeling of Atmospheric Convection\n## Methods\n## Results\n## Conclusion\n# Comparing Storm Resolving Models and Climates\n## Methods\n## Results\n## Discussion\n# Understanding Extreme Precipitation Changes\n## Methods","[{\"question\":\"What is the dissertation’s main goal regarding tropical convection and precipitation?\",\"answer\":\"To improve how tropical convection and precipitation are modeled and analyzed by using machine learning methods, including neural-network emulation and generative modeling approaches.\"},{\"question\":\"How does the work evaluate neural-network emulations of sub-grid parameterizations?\",\"answer\":\"Through performance analysis and postprocessing, including assessments of spatial structures, temporal variability, and hyperparameter optimization while considering physical constraints for the diurnal cycle.\"},{\"question\":\"What modeling techniques are used to study atmospheric convection and its patterns?\",\"answer\":\"The dissertation uses generative modeling, including VAE-based approaches, and compares model outputs using methods such as clustering and analysis in latent space to reveal differences and interpretable convection structures.\"}]","Improving The Modeling and Analysis of Tropical Convection and Precipitation through Machine Learning Methods - Dissertation | 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