[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122526-en":3,"doc-seo-122526-105":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":20,"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},122526,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Physics Guided Machine Learning algorithm for MAX-DOAS retrieval","Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a passive remote-sensing method used to infer aerosol extinction coefficient profiles and trace gas concentrations. Its inverse problem is ill-posed, making it difficult to construct a reliable inversion algorithm. This dissertation formulates MAX-DOAS retrieval as supervised learning, training physics-consistent models on synthetic radiative transfer datasets. A CNN+LSTM feasibility study extracts aerosol optical and scattering parameters from single scans, while a physics-guided approach retrieves aerosol and trace gas jointly. The PGML model uses physical constraints and a pseudo-inverse layer and is evaluated on synthetic data and Pandora MAX-DOAS measurements, with comparisons to existing inversion algorithms.","Physics Guided Machine Learning algorithm for MAX-DOAS  \nretrieval  \nYun Dong  \nDissertation submitted to the Faculty of the  \nVirginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nElectrical Engineering  \nElena Spinei Lind, Chair  \nAnuj Karpatne  \nScott M. Bailey  \nScott Leslie England  \nXiaoting Jia  \nOct.7, 2022  \nBlacksburg, Virginia  \nKeywords: MAX-DOAS, Physics-guided machine learning, aerosol and trace gas retrieval  \nCopyright 2023, Yun Dong  \nPhysics Guided Machine Learning algorithm for MAX-DOAS retrieval  \nYun Dong  \n(ABSTRACT)  \nMulti Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a passive remote sensing technique that has been widely used to derive aerosol extinction coefficient profilesand trace gas concentrations. The ill-posed nature of the MAX-DOAS inversion problem makes it almost impossible to design an inversion algorithm providing a definite solution. A possible way to find a low-error inversion algorithm is incorporating the machine learning (ML) technique into the MAX-DOAS retrieval.  \nThis dissertation serves as the author’s exploration of designing such an ML-based inversion algorithm. The inversion problem is formulated as a supervised learning problem and the ML models are trained on synthetic datasets simulated by radiative transfer models. By starting with a feasibility study, it is first shown that a ML model with appropriate architecture (CNN+LSTM) is capable of extracting aerosol extinction coefficient profile, single scattering albedo and asymmetry factor from one MAX-DOAS scan.  \nThen more realistic atmosphere states were used for generating the training set. Due to the high time cost of radiative transfer simulations, a data augmentation strategy was put forward to increase the number of samples in the training set. A physics-guided machine learning (PGML) algorithm was designed to retrieve aerosol information and trace gas concentrations simultaneously. The model is named as PGML model because: (1) its prediction is based on the physical laws it has learnt from the radiative transfer simulations and (2)  \nintroduction of the physical constraints and the pseudo-inverse layer.  \nThe PGML model was tested on both a synthetic test set and real MAX-DOAS measurements from Pandora instruments. Evaluation on the synthetic dataset suggests that with similar data distribution, the PGML model is capable of retrieving aerosol extinction coefficient profile, trace gas concentration profile and the box-AMFs with good accuracy. Validation on real data was done via comparisons with inversion results given by other algorithms. Generally, moderate linear correlation were found between the inversion results. Limitation of current version of the PGML model and factors might lead to the discrepancies between inversion results given by the PGML model and other algorithms were discussed.  \nPhysics Guided Machine Learning algorithm for MAX-DOAS retrieval  \nYun Dong  \n(GENERAL AUDIENCE ABSTRACT)  \nMulti Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a passive remote sensing technique for deriving aerosol and trace gas information in the lower atmosphere. A MAX-DOAS instrument is a ground-based system consists of a scanning telescope, a stepping motor and a spectrometer. It collects scattered solar photons at multiple elevation angles. And from spectrum analysis and inversion algorithms, aerosol properties such as aerosol extinction coefficient profile (a vertical profile describing how much the solar radiation is weakened by the atmosphere), single scattering albedo (the ratio of scattered light to incoming light) and trace gas concentrations can be retrieved. The ill-posed nature of the MAX-DOAS inversion problem makes it almost impossible to design an inversion algorithm providing a definite solution. A possible way to find a low-error inversion algorithm is incorporating the machine learning","cbCaik5HVlz5k8lU","https://ap.wps.com/l/cbCaik5HVlz5k8lU","pdf",12584800,1,247,"English","en",105,"# Abstract\n## MAX-DOAS background and inverse problem\n## Supervised learning with synthetic radiative transfer datasets\n## Feasibility: CNN+LSTM for single-scan retrieval\n## Data augmentation for realistic atmospheres\n## Physics-guided ML (PGML) model design\n## Evaluation on synthetic and real Pandora measurements\n## Limitations and discrepancy analysis","[{\"question\":\"Why is MAX-DOAS retrieval challenging?\",\"answer\":\"MAX-DOAS inversion is ill-posed, so designing an algorithm that yields a definite and stable solution is difficult. This motivates alternative strategies to reduce retrieval error.\"},{\"question\":\"How is the dissertation’s machine learning training data created?\",\"answer\":\"Training uses synthetic datasets generated by radiative transfer models, because reliable real-world datasets combining aerosol and trace gas properties with real MAX-DOAS observations are limited.\"},{\"question\":\"What makes the proposed PGML approach “physics-guided”?\",\"answer\":\"The PGML model’s predictions rely on physical laws learned from simulations and incorporate physical constraints plus a pseudo-inverse layer to guide the inversion.\"}]","Physics Guided Machine Learning algorithm for MAX-DOAS retrieval | PDF",1785811096,622,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"physics-guided-machine-learning-algorithm-for-max-doas-retrieval","",{"@graph":36,"@context":85},[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/physics-guided-machine-learning-algorithm-for-max-doas-retrieval/122526/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is MAX-DOAS retrieval challenging?","Question",{"text":75,"@type":76},"MAX-DOAS inversion is ill-posed, so designing an algorithm that yields a definite and stable solution is difficult. This motivates alternative strategies to reduce retrieval error.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dissertation’s machine learning training data created?",{"text":80,"@type":76},"Training uses synthetic datasets generated by radiative transfer models, because reliable real-world datasets combining aerosol and trace gas properties with real MAX-DOAS observations are limited.",{"name":82,"@type":73,"acceptedAnswer":83},"What makes the proposed PGML approach “physics-guided”?",{"text":84,"@type":76},"The PGML model’s predictions rely on physical laws learned from simulations and incorporate physical constraints plus a pseudo-inverse layer to guide the inversion.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]