[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127270-en":3,"doc-seo-127270-105":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},127270,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","A Machine Learning Approach to Quantifying Observation Error of Airborne Radio Occultation in Atmospheric Rivers","This thesis presents a machine-learning framework to quantify observational error in airborne radio occultation (ARO) measurements deployed over atmospheric rivers (ARs). It examines the synoptic environment relevant to ARs, describes the ARO data collected during AR reconnaissance, and reviews ARO retrieval techniques along with major sources of observation error and quality-control procedures. The study compares ARO results with ERA5 reanalysis, then uses clustering and Gaussian mixture model methods to characterize vertical AR structure, assign cluster labels, and evaluate ARO–ERA5 differences across clusters and geographic regimes.","UC San Diego  \nUC San Diego Electronic Theses and Dissertations  \nTitle  \nA Machine Learning Approach to Quantifying Observation Error of Airborne Radio Occultation in Atmospheric Rivers  \nPermalink  \n[https://escholarship.org/uc/item/2pr2z3g8](https://escholarship.org/uc/item/2pr2z3g8)  \nAuthor  \nBarton, Noah Jeffery  \nPublication Date  \n2025  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA SAN DIEGO  \nA Machine Learning Approach to Quantifying Observation Error of Airborne Radio Occultation  \nin Atmospheric Rivers  \nA Thesis submitted in partial satisfaction of the  \nrequirements for the degree Master of Science  \nin  \nOceanography  \nby  \nNoah Jeffery Barton  \nCommittee in charge:  \nProfessor Jennifer S. Haase, Chair  \nProfessor Joel Norris  \nProfessor Duncan Watson-Parris  \nProfessor Shang-Ping Xie  \nCopyright  \nNoah Jeffery Barton, 2025 All rights reserved.  \nThe Thesis of Noah Jeffery Barton is approved, and it is acceptable in quality and form for publication on microfilm and electronically.  \nUniversity of California San Diego  \n2025  \nTABLE OF CONTENTS  \nThesis Approval Page ......................................................... iii  \nTable of Contents ............................................................ iv  \n[List of Figures ............................................................... vi](List of Figures ............................................................... vi)  \n[List of Tables ................................................................ vii](List of Tables ................................................................ vii)  \n[Acknowledgements ........................................................... viii](Acknowledgements ........................................................... viii)  \n[Abstract of the Thesis ........................................................ xi](Abstract of the Thesis ........................................................ xi)  \n[Chapter 1 Introduction ..................................................... 1](Chapter 1 Introduction ..................................................... 1)  \n[Chapter 2 Synoptic environment in and near ARs .............................. 5](Chapter 2 Synoptic environment in and near ARs .............................. 5)  \n[Chapter 3 ARO data collected during AR Recon ............................... 9](Chapter 3 ARO data collected during AR Recon ............................... 9)  \n[Chapter 4 Methods ........................................................ 15](Chapter 4 Methods ........................................................ 15)  \n[4.1 ARO Retrieval Techniques ............................................. 15](4.1 ARO Retrieval Techniques ............................................. 15)  \n[4.2 Sources of ARO Observation Error ...................................... 16](4.2 Sources of ARO Observation Error ...................................... 16)  \n[4.3 Quality Control for the Purposes of this Study ............................. 17](4.3 Quality Control for the Purposes of this Study ............................. 17)  \n[4.4 Simulation of Refractivity from Model Products ........................... 18](4.4 Simulation of Refractivity from Model Products ........................... 18)  \n[4.5 Simulation of bending angle from model products ......................... 20](4.5 Simulation of bending angle from model products ......................... 20)  \n[Chapter 5 Comparing ARO and ERA5 reanalysis ............................... 21](Chapter 5 Comparing ARO and ERA5 reanalysis ............................... 21)  \n[5.1 Aggregate statistics of ARO-ERA5 differences ........................... 21](5.1 Aggregate statistics of ARO-ERA5 differences ........................... 21)  \n[5.2 ARO-ERA5 differences as a function of latitude .......................... 24](5.2 ARO-ERA5 differences as a function of latitude","cbCair0MVOea3XLv","https://ap.wps.com/l/cbCair0MVOea3XLv","pdf",30506749,1,85,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 Synoptic environment in and near ARs\n# Chapter 3 ARO data collected during AR Recon\n# Chapter 4 Methods\n## 4.1 ARO Retrieval Techniques\n## 4.2 Sources of ARO Observation Error\n## 4.3 Quality Control for the Purposes of this Study\n## 4.4 Simulation of Refractivity from Model Products\n## 4.5 Simulation of bending angle from model products\n# Chapter 5 Comparing ARO and ERA5 reanalysis\n## 5.1 Aggregate statistics of ARO-ERA5 differences\n## 5.2 ARO-ERA5 differences as a function of latitude\n## 5.3 ARO-ERA5 differences near the tracking limit\n# Chapter 6 Utilizing machine learning to characterize the vertical structure of the AR environment\n# Chapter 7 Results of selected GMM method\n# Chapter 8 Discussion","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To quantify observation error in airborne radio occultation measurements over atmospheric rivers using machine-learning methods and comparative analysis with reanalysis data.\"},{\"question\":\"How does the thesis assess observation error?\",\"answer\":\"It identifies sources of ARO observation error, applies quality control, simulates refractivity and bending angles from model products, and compares ARO with ERA5 reanalysis.\"},{\"question\":\"What machine-learning approach is used to characterize the atmospheric river environment?\",\"answer\":\"The study applies clustering and Gaussian mixture model techniques to learn vertical structure patterns, assign ARO profiles to clusters, and relate cluster membership to ARO–ERA5 differences across locations and regimes.\"}]","A Machine Learning Approach to Quantifying Observation Error of Airborne Radio Occultation in Atmospheric Rivers | 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is the main goal of the thesis?","Question",{"text":75,"@type":76},"To quantify observation error in airborne radio occultation measurements over atmospheric rivers using machine-learning methods and comparative analysis with reanalysis data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis assess observation error?",{"text":80,"@type":76},"It identifies sources of ARO observation error, applies quality control, simulates refractivity and bending angles from model products, and compares ARO with ERA5 reanalysis.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine-learning approach is used to characterize the atmospheric river environment?",{"text":84,"@type":76},"The study applies clustering and Gaussian mixture model techniques to learn vertical structure patterns, assign ARO profiles to clusters, and relate cluster membership to ARO–ERA5 differences across locations and 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