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

• Published in: Advances in atmospheric sciences 2014-11, Vol.31 (6), p.1386-1394
• Main Authors: Yang, Lu, Wang, Zhenhui, Chu, Yanli, Zhao, Hang, Tang, Min
• Format: Article
• Language: English
• Subjects: Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Latitude; Meteorology; NCEP再分析资料; Radiative transfer; Water vapor; Wind; 中纬度地区; 信号提取; 图片; 技术; 时间; 红外窗口; 运动矢量
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/s00376-014-3165-9

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.