[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121785-en":3,"doc-seo-121785-105":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},121785,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","HYBRID OPTICAL TURBULENCE MODELS USING MACHINE LEARNING AND LOCAL MEASUREMENTS - A PREPRINT","Accurate atmospheric optical turbulence prediction in localized environments is crucial for assessing free-space optical system performance. Macro-meteorological models trained for one environment often lose accuracy when transferred to new microclimates, yet fully retraining with local measurements can be time- and resource-intensive and may require many observations. A machine-learning informed hybrid framework combines a baseline macro-meteorological model with local meteorological and scintillometer readings, training a Gradient Boosted Decision Tree to learn residual corrections. Hybrid models improve accuracy over baseline models and data-only learning, even with very limited observation time.","HYBRID OPTICAL TURBULENCE MODELS USING MACHINE LEARNING AND LOCAL MEASUREMENTS*  \nA PREPRINT  \narXiv:2310.17829v1 [[physics. ao-ph](physics. ao-ph)] 27 Oct 2023  \n Christopher Jellen†  \nMechanical Engineering Department Untied States Naval Academy Annapolis, MD 21402 [cdjellen@gmail.com](cdjellen@gmail.com)  \nCharles Nelson  \nElectrical Engineering Department Untied States Naval Academy Annapolis, MD 21402  \nJohn Burkhardt  \nMechanical Engineering Department Untied States Naval Academy Annapolis, MD 21402  \nCody Brownell  \nMechanical Engineering Department Untied States Naval Academy Annapolis, MD 21402  \nApril 27, 2023  \nABSTRACT  \nAccurate prediction of atmospheric optical turbulence in localized environments is essential for estimating the performance of free-space optical systems. Macro-meteorological models developed to predict turbulent effects in one environment may fail when applied in new environments. However, existing macro-meteorological models are expected to offer some predictive power. Building a new model from locally-measured macro-meteorology and scintillometer readings can require significant time and resources, as well as a large number of observations. These challenges motivate the development of a machine-learning informed hybrid model framework. By combining some baseline macro-meteorological model with local observations, hybrid models were trained to improve upon the predictive power of each baseline model. Comparisons between the performance of the hybrid models, the selected baseline macro-meteorological models, and machine-learning models trained only on local observations highlight potential use cases for the hybrid model framework when local data is expensive to collect. Both the hybrid and data-only models were trained using the Gradient Boosted Decision Tree (GBDT) architecture with a variable number of in-situ meteorological observations.  \nThe hybrid and data-only models were found to outperform three baseline macro-meteorological models, even for low numbers of observations, in some cases as little as one day. For the first baseline macro-meteorological model investigated, the hybrid model achieves an estimated 29% reduction in mean absolute error (MAE) using only one days-equivalent of observation, growing to 41% after only two days, and 68% after 180 days-equivalent training data. The data-only model generally showed similar but slightly lower performance as compared to the hybrid model. Notably, the hybrid model’s performance advantage over the data-only model dropped below 2% near the 24 days-equivalent observation mark and trended towards 0% thereafter. The number of days-equivalent training data required by both the hybrid model and the data-only model is potentially indicative of the seasonal variation in the local microclimate and its propagation environment.  \n1 Introduction  \nAtmospheric optical turbulence degrades the performance of free-space optics (FSO) and other optical systems, especially at low altitudes and in the near-maritime environment [1][2][3][4] . These effects are characterized by the  \n∗ Cite as: Applied Optics 62(18) 4880-4890, doi: 10 . 1364/AO.487280 †Corresponding Author  \nrefractive index structure parameter, C2n . For horizontal propagation, under the assumption of isotropy and path-wise homogeneity, fluctuations in C2n are dominated by temperature fluctuations [1] . The impact of atmospheric factors on C2n led to the development of models which predict local turbulent effects from macro-meteorological features [2] [5]  \n[6][7] .  \nExisting macro-meteorological models are often extended to new microclimates in an attempt to generate optical turbulence predictions using local atmospheric feature measurements. These models may generate predictions with higher error when applied to these new microclimates than in the environment in which the model was originally developed [2] [8] [9] [10] . Some state-of-the-art models have performed well across similar microclim","cbCaiivv8vHGo95d","https://ap.wps.com/l/cbCaiivv8vHGo95d","pdf",2528437,1,15,"English","en",105,"# Abstract\n# 1 Introduction\n## Motivation for hybrid modeling\n## Hybrid framework design\n## Local data collection and model comparison","[{\"question\":\"Why do macro-meteorological models struggle when applied to new microclimates?\",\"answer\":\"They may produce higher error in new environments than in the conditions where the models were originally developed.\"},{\"question\":\"How does the hybrid model framework combine baseline modeling with machine learning?\",\"answer\":\"It uses a baseline macro-meteorological model and trains a machine-learning model on the baseline residual error using locally acquired meteorological and C2n data.\"},{\"question\":\"What measurement data is used to train and evaluate the models?\",\"answer\":\"The study uses locally collected scintillometer and weather-station measurements gathered over the Severn River for about 31 months.\"}]","HYBRID OPTICAL TURBULENCE MODELS USING MACHINE LEARNING AND LOCAL MEASUREMENTS - A PREPRINT | PDF",1785806831,38,{"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},"hybrid-optical-turbulence-models-using-machine-learning-and-local-measurements-a-preprint","",{"@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/hybrid-optical-turbulence-models-using-machine-learning-and-local-measurements-a-preprint/121785/",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 do macro-meteorological models struggle when applied to new microclimates?","Question",{"text":75,"@type":76},"They may produce higher error in new environments than in the conditions where the models were originally developed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the hybrid model framework combine baseline modeling with machine learning?",{"text":80,"@type":76},"It uses a baseline macro-meteorological model and trains a machine-learning model on the baseline residual error using locally acquired meteorological and C2n data.",{"name":82,"@type":73,"acceptedAnswer":83},"What measurement data is used to train and evaluate the models?",{"text":84,"@type":76},"The study uses locally collected scintillometer and weather-station measurements gathered over the Severn River for about 31 months.","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"]