Rain effect and lack of in situ validation data are two main causes of tropical cyclone wind retrieval errors. The National Oceanic and Atmospheric Administration＇s Climate Prediction Center Morphing technique （CMO...Rain effect and lack of in situ validation data are two main causes of tropical cyclone wind retrieval errors. The National Oceanic and Atmospheric Administration＇s Climate Prediction Center Morphing technique （CMORPH） rain rate is introduced to a match-up dataset and then put into a rain correction model to remove rain effects on ＂Jason-1＂ normalized radar cross section （NRCS）; Hurricane Research Division （HRD） wind sPeed, which integrates all available surface weather observations, is used to substitute in situ data for establishing this relationship with ＂Jason-l＂ NRCS. Then, an improved ＂Jason-l＂ wind retrieval algorithm under tropical cyclone conditions is proposed. Seven tropical cyclones from 2003 to 2010 are studied to validate the new algorithm. The experimental results indicate that the standard deviation of this algorithm at C-band and Ku-band is 1.99 and 2.75 m/s respectively, which is better than the existing algorithms. In addition, the C-band algorithm is more suitable for sea surface wind retrieval than Ku-band under tropical cyclone conditions.展开更多
基金The National Natural Science Foundation of China under Nos 41201350 and 41228007the International Scientific and Technological Cooperation Projects of State Oceanic Adminstration under contact No.2011DFA22260the Knowledge Innovation Program of the Chinese Academy of Sciences under contact No.Y0S04300KB
文摘Rain effect and lack of in situ validation data are two main causes of tropical cyclone wind retrieval errors. The National Oceanic and Atmospheric Administration＇s Climate Prediction Center Morphing technique （CMORPH） rain rate is introduced to a match-up dataset and then put into a rain correction model to remove rain effects on ＂Jason-1＂ normalized radar cross section （NRCS）; Hurricane Research Division （HRD） wind sPeed, which integrates all available surface weather observations, is used to substitute in situ data for establishing this relationship with ＂Jason-l＂ NRCS. Then, an improved ＂Jason-l＂ wind retrieval algorithm under tropical cyclone conditions is proposed. Seven tropical cyclones from 2003 to 2010 are studied to validate the new algorithm. The experimental results indicate that the standard deviation of this algorithm at C-band and Ku-band is 1.99 and 2.75 m/s respectively, which is better than the existing algorithms. In addition, the C-band algorithm is more suitable for sea surface wind retrieval than Ku-band under tropical cyclone conditions.
