[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124379-en":3,"doc-seo-124379-105":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},124379,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Predicting Optical Turbulence using Machine Learning Methodology","Project work focuses on measuring and predicting optical turbulence for high energy laser performance, where turbulence levels vary in space and time and direct point measurements can be unreliable in harsh coastal conditions. The Navy Atmospheric Vertical Surface Layer Model (NAVSLaM) uses robust atmospheric sensors to estimate turbulence within ~100 m. The Physics Department develops machine-learning models trained from atmospheric measurements at two heights, then compares prediction accuracy against baseline sonic anemometer data using root-mean-square error. Results assess reliability across nonhomogeneous environments and motivate physics-based ML future work.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| NPS Scholarship | Publications |\n| --- | --- |\n\n2023-10-21  \nPredicting Optical Turbulence using Machine Learning Methodology  \nBlau, Joseph  \nMonterey, CA; Naval Postgraduate School  \n[https://hdl.handle.net/10945/74235](https://hdl.handle.net/10945/74235)  \nThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.  \nDownloaded from NPS Archive: Calhoun  \nMONTEREY, CALIFORNIA  \nPREDICTING OPTICAL TURBULENCE USING MACHINE  \nLEARNING METHODOLOGY  \nEXECUTIVE SUMMARY  \nPrincipal Investigator (PI): Dr. Joseph Blau, Physics (PH)  \nAdditional Researcher(s): Dr. Keith Cohn, PH; Ms. Amanda Coleman, PH; Mr. Paul Frederickson, Meteorology  \nStudent Participation: LCDR Marthen Tamus, Indonesian Navy, PH  \nPrepared for:  \nTopic Sponsor Lead Organization: ASN(RDA) -Research, Development, and Acquisition  \nTopic Sponsor Organization(s): Office of Naval Research (ONR)  \nTopic Sponsor Name(s): ONR Code 333, Sarwat Chappell  \nTopic Sponsor Contact Information: [sarwat.chappell@navy.mil](sarwat.chappell@navy.mil); 703-696-4224  \nProject Summary  \nMeasuring and predicting optical turbulence is difficult and requires specialized equipment. The Naval Postgraduate School (NPS) Meteorology Department developed the Navy Atmospheric Vertical Surface Layer Model (NAVSLaM) to predict optical turbulence in the surface layer (up to ~100 m above the ocean or land) based upon atmospheric measurements using simple, robust sensors. On the other hand, the Physics Department has developed machine-learning (ML) models of optical turbulence using atmospheric measurements. The goal of this project is to collect atmospheric data from two different heights in a coastal environment to develop an ML model that can accurately predict turbulence. Furthermore, we want to directly compare the predictive performance of the ML model to NAVSLaM in nonhomogeneous environments. This research involves ongoing measurements of optical turbulence using sonic anemometers that served as the baseline to compare prediction from the models. Atmospheric parameters such as air temperature, wind speed, humidity at two different heights, solar flux, and ground temperature were simultaneously collected. Three months of these data were used as inputs for NAVSLaM and the ML models to predict optical turbulence. We then compared the performance of these prediction models to each other by calculating the root-mean-square error with respect to the baseline data from the sonic anemometers. The ML models generally performed better, although their predictions are specific to the location where their training data was obtained, whereas NAVSLaM results are generally applicable to any location that satisfies the model assumptions. The results from this research will help determine which model is more reliable for a given environment. Future work could include the development of physics-based ML models that combine the accuracy of ML models with the generality of physical models.  \nKeywords: high energy lasers, HELs, atmospheric propagation, optical turbulence, machine learning, ML, Monin-Obukhov similarity theory, MOST  \nBackground  \nOptical turbulence has various effects on laser beam propagation, including beam breakup, wander, and scintillation. Therefore, estimating the level of turbulence along the beam path is essential to predict high energy laser (HEL) performance and to determine appropriate turbulence mitigation measures (Titterton, 2015). However, measuring or predicting turbulence levels is not easy apart from the fact that the strength of the turbulence along the beam path can vary spatially and temporally. Also, under certain environmental conditions, such as at sea, the delicate equipment required to measure point turbulence often does not work correctly. Under these environmental conditions, estimating","cbCaiv7UYTW8U6Af","https://ap.wps.com/l/cbCaiv7UYTW8U6Af","pdf",297966,1,6,"English","en",105,"# Executive Summary\n## Project Summary\n## Background\n## Prior Work","[{\"question\":\"Why is predicting optical turbulence important for high energy laser systems?\",\"answer\":\"Optical turbulence affects laser beam propagation through beam breakup, wander, and scintillation. Estimating turbulence strength along the beam path is needed to predict HEL performance and select mitigation measures.\"},{\"question\":\"How does NAVSLaM predict optical turbulence?\",\"answer\":\"NAVSLaM estimates turbulence levels in the surface layer (up to about 100 m above ocean or land) using meteorological inputs such as temperature, wind speed, and humidity.\"},{\"question\":\"What is the main goal of using machine learning in this project?\",\"answer\":\"Collect atmospheric data at two heights in a coastal environment to build an ML model that accurately predicts turbulence, then directly compare its predictive performance to NAVSLaM in nonhomogeneous settings.\"}]","Predicting Optical Turbulence using Machine Learning Methodology | PDF",1785821898,15,{"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},"predicting-optical-turbulence-using-machine-learning-methodology","",{"@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/predicting-optical-turbulence-using-machine-learning-methodology/124379/",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 predicting optical turbulence important for high energy laser systems?","Question",{"text":75,"@type":76},"Optical turbulence affects laser beam propagation through beam breakup, wander, and scintillation. Estimating turbulence strength along the beam path is needed to predict HEL performance and select mitigation measures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does NAVSLaM predict optical turbulence?",{"text":80,"@type":76},"NAVSLaM estimates turbulence levels in the surface layer (up to about 100 m above ocean or land) using meteorological inputs such as temperature, wind speed, and humidity.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main goal of using machine learning in this project?",{"text":84,"@type":76},"Collect atmospheric data at two heights in a coastal environment to build an ML model that accurately predicts turbulence, then directly compare its predictive performance to NAVSLaM in nonhomogeneous settings.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]