[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118225-en":3,"doc-seo-118225-105":30,"detail-sidebar-cat-0-en-105":95},{"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},118225,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning-Based Parameterisation of Photolysis in GEOS-Chem - Version 14.2.2 - Masters of Science by Research","Photolysis schemes are an essential but computationally expensive component of chemical transport models, where GEOS-Chem uses the Fast-JX formulation. This thesis develops a proof-of-concept machine-learning parameterisation for Fast-JX using an ensemble of XGBoost models. The trained models are integrated into the GEOS-Chem Fortran code and evaluated quantitatively against Fast-JX. It further assesses how photolysis prediction changes impact GEOSChem concentrations of key species such as O3 and NO2 during simulation.","Machine Learning-Based Parameterisation of Photolysis in GEOS-Chem: Version 14.2.2  \nMarcus Eliot Brady  \nMasters of Science by Research  \nUniversity of York  \nChemistry  \nSeptember, 2024  \nAbstract  \nPhotolysis schemes are an integral part of chemical transport models. However, they are computationally intensive. The chemical transport model GEOS-Chem uses the photolysis scheme Fast-JX, which takes a significant amount of time to run. Through the developments of machine learning, parameterised approaches for physical processes have become a common way of speeding up calculations. This study looks to develop a proof-of-concept approach for a machine learning-based parameterisation of the Fast-JX photolysis calculations using a collection of XGBoost models. The machine learning models were integrated into the GEOS-Chem Fortran code base and were quantitatively evaluated against the standard Fast-JX scheme. This work additionally determines the wider impact the photolysis predictions had on the GEOSChem simulation in regards to the calculated concentration of key components such as O 3 and NO2.  \nResults show high accuracy for most species, with 103 out of 105 unique photolysis rates maintaining an R2 greater than 0 .95 throughout a six month simulation period. While the current implementation is minimally optimised, and hence computationally slower than Fast-JX, it successfully demonstrates that a machine learning parameterisation of photolysis rates is feasible in Fortran based chemical transport models and provides a foundation for future optimisations.  \nAcknowledgments  \nFirst and foremost, I would like to thank to thank my supervisor Prof. Mat Evans. I am additionally grateful to have made acquaintance with everyone in the Evans group: Ryan Pound, Amy Lees, Hansen Cao, Matthew Rowlinson, and Killian Murphy. All the support, ranging from learning Fortran and plotting guidance to the photos of various trips at the end of the Thursday slides, has been greatly appreciated over this year. You have all greatly helped me develop my skills as a researcher.  \nIt would be inappropriate to not thank my family for their continued support, if I mentioned everyone, it would be a very long section of text so I will just say I have an appreciation for all. However, a very special thank you to my Grandpa and my Grandma, whose generosity led me to this masters and hence paved the way for all future research that I will take. Aside from my family there are a couple names I must mention. I must thank Lily, for your discussions, patience, and support throughout the past year not just academically but personally-I’m incredibly grateful. Also Immy and Willow, for keeping my company during my writing period. For compute power, the Viking cluster was used during this project, which is a high performance compute facility provided by the University of York. I am grateful for computational support from the University of York, IT Services, and the Research IT team. Through everyone’s support and following the completion of this thesis I can now go on to the next steps of my research career of completing my PhD over the next four years at UCL using the skills I have built throughout this year.  \nDeclaration  \nI, Marcus Brady, declare that this thesis is a presentation of original work and I am the sole author. This work has not previously been presented for a degree or other qualification at this University or elsewhere. All sources are acknowledged as references in the Bibliography.  \nContents  \nAbstract i  \nAcknowledgments ii  \nDeclaration iii  \nTable of Contents v  \nList of Tables vi  \nList of Figures ix  \n1 Introduction 1  \n1.1 Atmospheric Chemistry, Environmental Problems, and Chemical Transport Models ......................................... 1  \n1.2 Photolysis ..................................... 2  \n1.3 Different Photolysis Mechanisms ......................... 3  \n1.4 Research Aims .................................. 4  \n2 Theoretical Backgroun","cbCaigDjg62XeV9n","https://ap.wps.com/l/cbCaigDjg62XeV9n","pdf",26030795,1,88,"English","en",105,"# Introduction\n## Atmospheric Chemistry, Environmental Problems, and Chemical Transport Models\n## Photolysis\n## Different Photolysis Mechanisms\n## Research Aims\n# Theoretical Background\n## Machine Learning Foundations\n## Key Machine Learning Algorithms\n## XGBoost Algorithm over Random Forest Regression\n## Machine Learning in Atmospheric Chemistry\n## Original Photolysis Scheme in GEOS-Chem\n## Model Description (GEOS-Chem)\n# Data and Methodology\n## Training and Evaluation Data\n## Different Photolysis Rate Machine Learning Models\n## Model Training and Parameters\n## Objective Function and Regularisation\n## Implementation\n# Model Performance on Validation Data\n## Evaluation Metrics\n## The Model Statistics for Validation data\n## Feature Importance\n# Model performance when integrated into GEOS-Chem\n## Prediction of Photolysis Rates\n## Predictive Ability in the Vertical Columns\n## Concentration Calculations using the Predicted Rates\n# Discussion\n## Hardware and Software Optimisations\n## Limitations\n# Conclusions\n# Appendix","[{\"question\":\"Why is photolysis parameterisation important in GEOS-Chem?\",\"answer\":\"Photolysis schemes are integral to chemical transport models but are computationally intensive. GEOS-Chem’s Fast-JX photolysis component significantly increases runtime, motivating faster parameterised alternatives.\"},{\"question\":\"How does the thesis implement the machine learning approach?\",\"answer\":\"XGBoost models are trained to parameterise Fast-JX photolysis calculations and then integrated into the GEOS-Chem Fortran codebase. Model outputs are evaluated against the standard Fast-JX scheme using quantitative metrics.\"},{\"question\":\"How accurate are the predicted photolysis rates compared with Fast-JX?\",\"answer\":\"Results show high accuracy for most species, with 103 out of 105 unique photolysis rates keeping an R2 above 0.95 throughout a six-month simulation period.\"},{\"question\":\"What impact does the new photolysis prediction have on GEOS-Chem concentrations?\",\"answer\":\"The thesis evaluates how updated photolysis predictions affect simulated concentrations of key components, including O3 and NO2, using the GEOS-Chem results driven by the ML-predicted rates.\"}]","Machine Learning-Based Parameterisation of Photolysis in GEOS-Chem - Version 14.2.2 - Masters of Science by Research | PDF",1785682406,222,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-based-parameterisation-of-photolysis-in-geos-chem-version-1422-masters-of-science-by-research","",{"@graph":36,"@context":89},[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/machine-learning-based-parameterisation-of-photolysis-in-geos-chem-version-1422-masters-of-science-by-research/118225/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is photolysis parameterisation important in GEOS-Chem?","Question",{"text":75,"@type":76},"Photolysis schemes are integral to chemical transport models but are computationally intensive. GEOS-Chem’s Fast-JX photolysis component significantly increases runtime, motivating faster parameterised alternatives.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis implement the machine learning approach?",{"text":80,"@type":76},"XGBoost models are trained to parameterise Fast-JX photolysis calculations and then integrated into the GEOS-Chem Fortran codebase. Model outputs are evaluated against the standard Fast-JX scheme using quantitative metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the predicted photolysis rates compared with Fast-JX?",{"text":84,"@type":76},"Results show high accuracy for most species, with 103 out of 105 unique photolysis rates keeping an R2 above 0.95 throughout a six-month simulation period.",{"name":86,"@type":73,"acceptedAnswer":87},"What impact does the new photolysis prediction have on GEOS-Chem concentrations?",{"text":88,"@type":76},"The thesis evaluates how updated photolysis predictions affect simulated concentrations of key components, including O3 and NO2, using the GEOS-Chem results driven by the ML-predicted rates.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]