Water Vapor Motion Signal Extraction from FY-2E Longwave Infrared Window Images for Cloud-free Regions:The Temporal Difference Technique  

作　　者：YANG Lu WANG Zhenhui CHU Yanli ZHAO Hang TANG Min 

机构地区：[1]Beijing Meteorological Office (Da Xing) [2]Collaborative Innovation Center on the Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science & Technology [3]School of Atmospheric Physics, Nanjing University of Information Science & Technology [4]Institute of Urban Meteorology, China Meteorological Administration

出　　处：《Advances in Atmospheric Sciences》2014年第6期1386-1394,共9页大气科学进展（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos.41175035 and 41005005);the National Basic Research Program of China (Grant No.2009CB421502);a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)

摘　　要：The aim of this study is to calculate the low-level atmospheric motion vectors （AMVs） in clear areas with FY-2E IR2 window （11.59-12.79 μm） channel imagery,where the traditional cloud motion wind technique fails.A new tracer selection procedure,which we call the temporal difference technique,is demonstrated in this paper.This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature （TB） differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions.The TB difference corresponding to a 10％ change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission （MODTRAN4） radiative transfer model.The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions：tropical,midlatitude summer,U.S.standard,and midlatitude winter.The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the TB ＂wind＂.This technique is valid over cloudfree ocean areas.The proposed algorithm exhibits encouraging statistical results in terms of vector difference （VD）,speed bias （BIAS）,mean vector difference （MVD）,standard deviation （SD）,and root-mean-square error （RMSE）,when compared with the wind field of NCEP reanalysis data and rawinsonde observations.The aim of this study is to calculate the low-level atmospheric motion vectors （AMVs） in clear areas with FY-2E IR2 window （11.59-12.79 μm） channel imagery,where the traditional cloud motion wind technique fails.A new tracer selection procedure,which we call the temporal difference technique,is demonstrated in this paper.This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature （TB） differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions.The TB difference corresponding to a 10％ change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission （MODTRAN4） radiative transfer model.The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions：tropical,midlatitude summer,U.S.standard,and midlatitude winter.The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the TB ＂wind＂.This technique is valid over cloudfree ocean areas.The proposed algorithm exhibits encouraging statistical results in terms of vector difference （VD）,speed bias （BIAS）,mean vector difference （MVD）,standard deviation （SD）,and root-mean-square error （RMSE）,when compared with the wind field of NCEP reanalysis data and rawinsonde observations.

关 键 词：atmospheric motion vector clear sky radiative transfer temporal difference technique 

分 类 号：P432[天文地球—大气科学及气象学] P407