[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118056-en":3,"doc-seo-118056-105":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},118056,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Predicting Optical Turbulence using Machine Learning Methodology","Measuring and predicting optical turbulence is difficult and requires specialized equipment. The Naval Postgraduate School Meteorology Department previously developed NAVSLaM to predict optical turbulence in the surface layer (up to about 100 m above ocean or land) using atmospheric measurements with simple sensors. Physics Department machine learning models also predict optical turbulence from atmospheric inputs. This study uses multi-month measurements with sonic anemometers as the baseline, comparing model outputs via root-mean-square error. Results show the ML model performed better for observed conditions, while NAVSLaM remains more general across environments. Improved optical turbulence prediction can enhance directed energy weapon effectiveness forecasting and enable real-time operational predictions.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| 2023-10\u003Cbr>Predicting Optical Turbulence using Machine Learning Methodology\u003Cbr>Blau, Joseph; Coleman, Amanda; Tamus, Marthen\u003Cbr>Monterey, California. Naval Postgraduate School |\n| --- |\n| [https://hdl.handle.net/10945/72447](https://hdl.handle.net/10945/72447) |\n\nNPS Scholarship Reports  \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  \nNPS-PH-23-003  \nNAVAL POSTGRADUATE  \nSCHOOL  \nMONTEREY, CALIFORNIA  \nPREDICTING OPTICAL TURBULENCE USING MACHINE  \nLEARNING METHODOLOGY  \nby  \nJoseph Blau, Amanda Coleman, and Marthen Tamus  \nOctober 2023  \nApproved for public release. Distribution is unlimited.  \nPrepared for: Office of Naval Research  \nThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098) . NRP Project ID: NPS-23-N106-A  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. |  |  |  |  |  |  |  |  |  |  |\n| 1. REPORT DATE\u003Cbr>21 Oct 2023 |  | 2. REPORT TYPE\u003Cbr>Technical Report |  |  |  | 3. DATES COVERED |  |  |  |  |\n|  |  |  |  |  |  | START DATE 24 Oct 2022 |  |  |  | END DATE\u003Cbr>21 Oct 2023 |\n| 4. TITLE AND SUBTITLE\u003Cbr>Predicting Optical Turbulence using Machine Learning Methodology |  |  |  |  |  |  |  |  |  |  |\n| 5a. CONTRACT NUMBER |  |  | 5b. GRANT NUMBER |  |  |  | 5c. PROGRAM ELEMENT NUMBER\u003Cbr>0605853N/2098 |  |  |  |\n| 5d. PROJECT NUMBER\u003Cbr>NPS-23-N106-A; W2324 |  |  | 5e. TASK NUMBER |  |  |  | 5f. WORK UNIT NUMBER |  |  |  |\n| 6. AUTHOR(S)\u003Cbr>Blau, Joseph, A. ; Coleman, Amanda, R. ; Tamus, Marthen |  |  |  |  |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>1 University Circle\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  |  |  |  |  | 8. PERFORMING ORGANIZATION REPORT NUMBER\u003Cbr>NPS-PH-23-003 |  |\n| 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School, Naval Research Program; Office of Naval Research |  |  |  |  |  | 10. SPONSOR/MONITOR’S ACRONYM(S)\u003Cbr>NRP; ONR |  |  |  | 11. SPONSOR/MONITOR’S REPORT NUMBER(S) NPS-23-N106-A |\n| 12. DISTRIBUTION/AVAILABILITY STATEMENT\u003Cbr>Approved for public release. Distribution is unlimited. |  |  |  |  |  |  |  |  |  |  |\n| 13. SUPPLEMENTARY NOTES |  |  |  |  |  |  |  |  |  |  |\n| 14. ABSTRACT\u003Cbr>Measuring and predicting optical turbulence is difficult and requires specialized equipment. The NPS Meteorology Department has previously developed a 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 models of optical turbulence using atmospheric measurements. This research involves measurements of optical turbulence over many months 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 as well as solar flux and ground temperature were simultaneously collected. Those data were used as inputs for NAVSLaM and the machine learning 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 results from this research will help determine which model is more reliable for the given environment. Overall, the ML model appeared to work better than NAVSLaM for predicting the optical turbulence values that we observed. However, NAVSLaM is a more general model that should work we","cbCaioMMIU7PVkkQ","https://ap.wps.com/l/cbCaioMMIU7PVkkQ","pdf",4484721,1,39,"English","en",105,"# Abstract\n# Report Documentation\n## Background and Prior Work\n## Data Collection and Method Inputs\n## Model Comparison and Evaluation Metrics\n## Findings and Operational Implications\n# Subject Terms","[{\"question\":\"What is the main goal of the research on optical turbulence?\",\"answer\":\"To measure and predict optical turbulence and determine which prediction approach is more reliable for the studied environment, supporting improved directed energy weapon forecasting.\"},{\"question\":\"What baseline data was used to evaluate the prediction models?\",\"answer\":\"Multi-month measurements of optical turbulence using sonic anemometers served as the baseline for comparison.\"},{\"question\":\"How were the prediction models compared?\",\"answer\":\"The study fed atmospheric parameters into NAVSLaM and machine learning models, then compared their predictions using root-mean-square error against the sonic anemometer baseline data.\"}]","Predicting Optical Turbulence using Machine Learning Methodology | 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