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(8月19日)微波遥感 技术实现 院细节实验室学术报告

文章来源: | 发布时间:2016-08-17 | 【打印】【关闭】

  题目:I ASCAT Data Process-From Level 1 to Level 4

               II Monitoring Tropical Cyclones from Scatterometer Constellation: Challenges and Opportunities

  时间:2016年8月19日(星期五)9:00-11:30

  地点:九章大厦A座10楼A1014Meeting room

  报告人:Dr. Wenming Lin (林文明博士)

  单位:The Institute of Marine Sciences (I公分), Spanish National Research Council,Spain

  主办:中国科学院微波遥感 技术实现 细节实验室

  报告人简介:

  Wenming Lin received the B.Sc. degree in engineering from Wuhan University, in 2006 and the Ph.D. degree in engineering from the Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing, in 2011. He is currently a Research Scientist with the Institute of Marine Sciences (I公分), Spanish National Research Council, Spain, working on the scatterometer wind data processing. Dr. Lin has wide experience in each and every component of the scatterometer processing chain, including ocean calibration, inversion, quality control, and ambiguity removal. Moreover, he is currently applying his experience in the scatterometer variational ambiguity removal scheme (2D-Var) to improve the impact of scatterometer winds assimilated into global NWP models (4D-Var). Besides his scientific contribution to scatterometry, several of his research findings have already been operationally implemented in the official EUMETSAT NWP SAF scatterometer processor.

  报告始末简介:

  Dr. Lin will give an overview presentation on the most relevant components in each level of scatterometer data processing. In particular, an improved two-dimensional variational ambiguity removal (2DVAR) method is introduced to detect the correct position of frontlines and low-pressure centers (tropical cyclones) effectively. Like other variational meteorological data assimilation systems in Numerical Weather Prediction (NWP), 2DVAR combines scatterometer observations with prior NWP background information, in this case from the European Centre for Medium-range Weather Forecasts (E公分WF). The conventional 2DVAR may select the wrong ambiguity under certain conditions, e.g., when the background mislocates frontal areas or low-pressure centers, or when it misses convective systems. The relative influence of the scatterometer and E公分WF wind fields in the resulting 2DVAR analysis field can be controlled by adjusting the background error spatial correlation structure, and the background and/or observation error variances. An adaptive 2DVAR approach is proposed to improve ASCAT ambiguity removal, using background error spatial correlations estimated from the autocorrelation of observed scatterometer wind components minus E公分WF forecasts, and using observation and background errors estimated from triple collocation analysis on collocated buoy, ASCAT, and E公分WF data. The triple collocations are segregated into several categories according to the ASCAT-derived parameters that have proven to be effective in detecting the correct position of frontlines and low-pressure centers. Verification using a typical cyclone case and collocated ASCAT and buoy winds shows that the 2DVAR analysis as well as the ASCAT ambiguity removal is improved significantly by putting more weight on the ASCAT observations using empirically determined spatial background error structure functions and situation-dependent observation/background error variances.

  

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