Please wait a minute...
Chin. Phys. B, 2011, Vol. 20(3): 034301    DOI: 10.1088/1674-1056/20/3/034301
CLASSICAL AREAS OF PHENOMENOLOGY Prev   Next  

Application of the Tikhonov regularization method to wind retrieval from scatterometer data I. Sensitivity analysis and simulation experiments

Zhong Jian(钟剑), Huang Si-Xun(黄思训), Du Hua-Dong(杜华栋), and Zhang Liang(张亮)
Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China
Abstract  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.
Keywords:  scatterometer      variational optimization analysis      wind retrieval      regularization method  
Received:  17 July 2009      Revised:  27 June 2010      Accepted manuscript online: 
PACS:  43.28.We (Measurement methods and instrumentation for remote sensing and for inverse problems)  
  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)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 40775023).

Cite this article: 

Zhong Jian(钟剑), Huang Si-Xun(黄思训), Du Hua-Dong(杜华栋), and Zhang Liang(张亮) Application of the Tikhonov regularization method to wind retrieval from scatterometer data I. Sensitivity analysis and simulation experiments 2011 Chin. Phys. B 20 034301

[1] Zhang L 2007 Sea Surface Winds Retrieval From Satellite-Borne Scatterometer Data (Nanjing: PLA University of Science and Technology of China) p5 (in Chinese)
[2] Feng Q 2004 Study of Sea Surface Wind Remote Sensing by Satellite Multi-Sensor Data (Qingdao: Ocean University of China) p8 (in Chinese)
[3] Zhang L, Huang S X, Liu Y D and Zhong J 2010 Acta Phys. Sin. 59 2889 (in Chinese)
[4] Marcos P A 2002 Wind Field Retrieval from Satellite Radar Systems (Amsterdam: University of Barcelona) p4
[5] Stoffelen A 1996 KNMI Tech. Rep. 193 44
[6] Stoffelen A 1998 J. Geophys. Res. 103 7755
[7] Lin M S 2000 J. of Remote Sensing 4 61 (in Chinese)
[8] Li Y C, Sun Y, Lin M S and Zheng S Q 1999 J. Oceanography in Taiwan Strait 18 42 (in Chinese)
[9] Henderson J M, Hoffman R N, Leidner S M, Atlas R, Brin E and Ardizzone J V 2003 J. Geophys. Res. 108 3176
[10] Vogelzang J, Stoffelen A, Verhoef A, Veries J D and Bonekamp H 2009 J. Atmos. Oceanic Technol 26 1229
[11] Vogelzang J 2008 SDP2.0 Validation (Netherlands: KNMI) p2
[12] Huang S X and Wu R S 2001 Mathematical and Physical Problems in Atmospheric Sciences (Beijing: Chinese Meteorological Press) p411 (in Chinese)
[13] Huang S X and Sheng Z 2006 Acta Phys. Sin. 55 514 (in Chinese)
[14] Cai Q F, Huang S X and Gao S T 2008 Acta Phys. Sin. 57 3913 (in Chinese)
[15] Huang S X, Xu D H, Lan W R and Zhang M 2005 J. Hydrodynamics (B) 17 459
[16] Stoffelen A 1998 Scatterometry (Netherlands: KNMI) p10
[17] Kirsch A 1996 An Introduction to the Mathematical Theory of Inverse Problems (New York: Springer-Verlag) p15 endfootnotesize
[1] A new algorithm for reconstructing the three-dimensional flow field of the oceanic mesoscale eddy
Chao Yan(颜超), Jing Feng(冯径), Ping-Lv Yang(杨平吕), and Si-Xun Huang(黄思训). Chin. Phys. B, 2021, 30(12): 120204.
[2] Variational regularization method of solving the Cauchy problem for Laplace’s equation: Innovation of the Grad-Shafranov (GS) reconstruction
Yan Bing (颜冰), Huang Si-Xun (黄思训). Chin. Phys. B, 2014, 23(10): 109402.
[3] 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). Chin. Phys. B, 2011, 20(6): 064301.
[4] Variational assimilation in combination with a regularization method for sea level pressure retrieval from QuikSCAT scatterometer data II: simulation experiment and actual case study
Zhang Liang(张亮), Huang Si-Xun(黄思训), Shen Chun(沈春), and Shi Wei-Lai(施伟来) . Chin. Phys. B, 2011, 20(12): 129201.
[5] Variational assimilation in combination with the regularization method for sea level pressure retrieval from QuikSCAT scatterometer data I: Theoretical frame construction
Zhang Liang(张亮), Huang Si-Xun(黄思训), Shen Chun(沈春), and Shi Wei-Lai(施伟来) . Chin. Phys. B, 2011, 20(11): 119201.
No Suggested Reading articles found!