Alamgir Hossan, M. Jacob, W. Linwood Jones, Harriet Medrozo
{"title":"一种从TRMM推断海洋矢量风的主动/被动微波检索算法","authors":"Alamgir Hossan, M. Jacob, W. Linwood Jones, Harriet Medrozo","doi":"10.1109/MICRORAD.2018.8430715","DOIUrl":null,"url":null,"abstract":"This paper describes a novel ocean vector wind (OVW) retrieval algorithm that uses Ku-band Precipitation Radar (PR) and the multi-frequency TRMM Microwave Imager (TMI), both on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The basis of this algorithm is the anisotropic nature of ocean backscatter (sig-0) and brightness temperature (Tb), which are used in a maximum likelihood estimation procedure to infer wind speed and wind direction. For this paper, we leverage from previous research that characterized the Geophysical Model Functions (GMF) for both TMI and PR observations. NOAA Numerical Weather Product (GDAS) was used as a nature run, to perform a Monte Carlo simulation to conduct trade studies and predict the OVW retrieval performance over the TRMM orbit. Examples of retrieved ocean winds and statistics of WS and WD differences are presented.","PeriodicalId":423162,"journal":{"name":"2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"An Active/Passive Microwave Retrieval Algorithm for Inferring Ocean Vector Winds from TRMM\",\"authors\":\"Alamgir Hossan, M. Jacob, W. Linwood Jones, Harriet Medrozo\",\"doi\":\"10.1109/MICRORAD.2018.8430715\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a novel ocean vector wind (OVW) retrieval algorithm that uses Ku-band Precipitation Radar (PR) and the multi-frequency TRMM Microwave Imager (TMI), both on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The basis of this algorithm is the anisotropic nature of ocean backscatter (sig-0) and brightness temperature (Tb), which are used in a maximum likelihood estimation procedure to infer wind speed and wind direction. For this paper, we leverage from previous research that characterized the Geophysical Model Functions (GMF) for both TMI and PR observations. NOAA Numerical Weather Product (GDAS) was used as a nature run, to perform a Monte Carlo simulation to conduct trade studies and predict the OVW retrieval performance over the TRMM orbit. Examples of retrieved ocean winds and statistics of WS and WD differences are presented.\",\"PeriodicalId\":423162,\"journal\":{\"name\":\"2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MICRORAD.2018.8430715\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MICRORAD.2018.8430715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Active/Passive Microwave Retrieval Algorithm for Inferring Ocean Vector Winds from TRMM
This paper describes a novel ocean vector wind (OVW) retrieval algorithm that uses Ku-band Precipitation Radar (PR) and the multi-frequency TRMM Microwave Imager (TMI), both on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The basis of this algorithm is the anisotropic nature of ocean backscatter (sig-0) and brightness temperature (Tb), which are used in a maximum likelihood estimation procedure to infer wind speed and wind direction. For this paper, we leverage from previous research that characterized the Geophysical Model Functions (GMF) for both TMI and PR observations. NOAA Numerical Weather Product (GDAS) was used as a nature run, to perform a Monte Carlo simulation to conduct trade studies and predict the OVW retrieval performance over the TRMM orbit. Examples of retrieved ocean winds and statistics of WS and WD differences are presented.