II Monitoring Tropical Cyclones from Scatterometer Constellation: Challenges and Opportunities
报告人：Dr. Wenming Lin （林文明博士）
单位：The Institute of Marine Sciences (ICM)， Spanish National Research Council，Spain
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 (ICM), 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.
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 (ECMWF). 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 ECMWF 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 ECMWF forecasts, and using observation and background errors estimated from triple collocation analysis on collocated buoy, ASCAT, and ECMWF 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.