{"title":"基于风激光雷达锥形扫描数据的平均风估计方法优化","authors":"Ramdas Makhmanasarov, A. M. Sherstobitov","doi":"10.1117/12.2644849","DOIUrl":null,"url":null,"abstract":"The calculation time and the error of the estimates of the horizontal wind velocity from the data of the conical scan are compared. Various implementations of algorithms for direct and filtered sinusoidal wave fitting, and machine learning algorithms based on boosted decision trees (BDT), are being tested. The paper presents the advantages and disadvantages of these algorithms in numerical simulation and experimental data, obtained during measurements with pulse coherent Doppler lidar.","PeriodicalId":217776,"journal":{"name":"Atmospheric and Ocean Optics","volume":"242 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of mean wind estimation methods from wind lidar's conical scan data\",\"authors\":\"Ramdas Makhmanasarov, A. M. Sherstobitov\",\"doi\":\"10.1117/12.2644849\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The calculation time and the error of the estimates of the horizontal wind velocity from the data of the conical scan are compared. Various implementations of algorithms for direct and filtered sinusoidal wave fitting, and machine learning algorithms based on boosted decision trees (BDT), are being tested. The paper presents the advantages and disadvantages of these algorithms in numerical simulation and experimental data, obtained during measurements with pulse coherent Doppler lidar.\",\"PeriodicalId\":217776,\"journal\":{\"name\":\"Atmospheric and Ocean Optics\",\"volume\":\"242 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric and Ocean Optics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2644849\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric and Ocean Optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2644849","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization of mean wind estimation methods from wind lidar's conical scan data
The calculation time and the error of the estimates of the horizontal wind velocity from the data of the conical scan are compared. Various implementations of algorithms for direct and filtered sinusoidal wave fitting, and machine learning algorithms based on boosted decision trees (BDT), are being tested. The paper presents the advantages and disadvantages of these algorithms in numerical simulation and experimental data, obtained during measurements with pulse coherent Doppler lidar.