中文摘要：

Scatterometer is an instrument which provides all-day and large-scale wind field information,and its application especially to wind retrieval always attracts meteorologists.Certain reasons cause large direction error,so it is important to find where the error mainly comes.Does it mainly result from the background field,the normalized radar cross-section(NRCS) or the method of wind retrieval? It is valuable to research.First,depending on SDP2.0,the simulated ’true’ NRCS is calculated from the simulated ’true’ wind through the geophysical model function NSCAT2.The simulated background field is configured by adding a noise to the simulated ’true’ wind with the non-divergence constraint.Also,the simulated ’measured’ NRCS is formed by adding a noise to the simulated ’true’ NRCS.Then,the sensitivity experiments are taken,and the new method of regularization is used to improve the ambiguity removal with simulation experiments.The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS;compared with the two-dimensional variational(2DVAR) ambiguity removal method,the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter,especially for the case of large error in the background.The work will provide important information and a new method for the wind retrieval with real data.

英文摘要：

Scatterometer is an instrument which provides all-day and large-scale wind field information, and its application especially to wind retrieval always attracts meteorologists. Certain reasons cause large direction error, so it is important to find where the error mainly comes. Does it mainly result from the background field, the normalized radar cross-section （NRCS） or the method of wind retrieval？ It is valuable to research. First, depending on SDP2.0, the simulated ＇true＇ NRCS is calculated from the simulated ＇true＇ wind through the geophysical mode] function NSCAT2. The simulated background field is configured by adding a noise to the simulated ＇true＇ wind with the non-divergence constraint. Also, the simulated ＇measured＇ NRCS is formed by adding a noise to the simulated ＇true＇ NRCS. Then, the sensitivity experiments are taken, and the new method of regularization is used to improve the ambiguity removal with simulation experiments. The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS; compared with the two-dimensional variational （2DVAR） ambiguity removal method, the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter, especially for the case of large error in the background. The work will provide important information and a new method for the wind retrieval with real data.