中国物理B ›› 2011, Vol. 20 ›› Issue (6): 64301-064301.doi: 10.1088/1674-1056/20/6/064301

• CLASSICAL AREAS OF PHENOMENOLOGY • 上一篇    下一篇

Application of Tikhonov regularization method to wind retrieval from scatterometer data II: cyclone wind retrieval with consideration of rain

钟剑, 黄思训, 费建芳, 杜华栋, 张亮   

  1. Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China
  • 收稿日期:2010-07-13 修回日期:2010-11-02 出版日期:2011-06-15 发布日期:2011-06-15
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 40775023).

Application of Tikhonov regularization method to wind retrieval from scatterometer data II: cyclone wind retrieval with consideration of rain

Zhong Jian(钟剑), Huang Si-Xun(黄思训)†, Fei Jian-Fang(费建芳) Du Hua-Dong(杜华栋), and Zhang Liang(张亮)   

  1. Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China
  • Received:2010-07-13 Revised:2010-11-02 Online:2011-06-15 Published:2011-06-15
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 40775023).

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摘要： According to the conclusion of the simulation experiments in paper I, the Tikhonov regularization method is applied to cyclone wind retrieval with a rain-effect-considering geophysical model function (called GMF+Rain). The GMF+Rain model which is based on the NASA scatterometer-2 (NSCAT2) GMF is presented to compensate for the effects of rain on cyclone wind retrieval. With the multiple solution scheme (MSS), the noise of wind retrieval is effectively suppressed, but the influence of the background increases. It will cause a large wind direction error in ambiguity removal when the background error is large. However, this can be mitigated by the new ambiguity removal method of Tikhonov regularization as proved in the simulation experiments. A case study on an extratropical cyclone of hurricane observed with SeaWinds at 25-km resolution shows that the retrieved wind speed for areas with rain is in better agreement with that derived from the best track analysis for the GMF+Rain model, but the wind direction obtained with the two-dimensional variational (2DVAR) ambiguity removal is incorrect. The new method of Tikhonov regularization effectively improves the performance of wind direction ambiguity removal through choosing appropriate regularization parameters and the retrieved wind speed is almost the same as that obtained from the 2DVAR.

Abstract: According to the conclusion of the simulation experiments in paper I, the Tikhonov regularization method is applied to cyclone wind retrieval with a rain-effect-considering geophysical model function (called GMF+Rain). The GMF+Rain model which is based on the NASA scatterometer-2 (NSCAT2) GMF is presented to compensate for the effects of rain on cyclone wind retrieval. With the multiple solution scheme (MSS), the noise of wind retrieval is effectively suppressed, but the influence of the background increases. It will cause a large wind direction error in ambiguity removal when the background error is large. However, this can be mitigated by the new ambiguity removal method of Tikhonov regularization as proved in the simulation experiments. A case study on an extratropical cyclone of hurricane observed with SeaWinds at 25-km resolution shows that the retrieved wind speed for areas with rain is in better agreement with that derived from the best track analysis for the GMF+Rain model, but the wind direction obtained with the two-dimensional variational (2DVAR) ambiguity removal is incorrect. The new method of Tikhonov regularization effectively improves the performance of wind direction ambiguity removal through choosing appropriate regularization parameters and the retrieved wind speed is almost the same as that obtained from the 2DVAR.

Key words: scatterometer, Tikhonov regularization, cyclone wind retrieval, rain effects

中图分类号:  (Measurement methods and instrumentation for remote sensing and for inverse problems)

  • 43.28.We
43.30.Pc (Ocean parameter estimation by acoustical methods; remote sensing; imaging, inversion, acoustic tomography) 42.68.Wt (Remote sensing; LIDAR and adaptive systems) 92.60.Gn (Winds and their effects)