Evaluation of a Support Vector Machine–Based Single-Doppler Wind Retrieval Algorithm

Evaluation of a Support Vector Machine–Based Single-Doppler Wind Retrieval Algorithm AbstractWind retrieval algorithms are required for Doppler weather radars. In this article, a new wind retrieval algorithm of single-Doppler radar with a support vector machine (SVM) is analyzed and compared with the original algorithm with the least squares technique. Through an analysis of coefficient matrices of equations corresponding to the optimization problems for the two algorithms, the new algorithm, which contains a proper penalization parameter, is found to effectively reduce the condition numbers of the matrices and thus has the ability to acquire accurate results, and the smaller the analysis volume is, the smaller the condition number of the matrix. This characteristic makes the new algorithm suitable to retrieve mesoscale and small-scale and high-resolution wind fields. Afterward, the two algorithms are applied to retrieval experiments to implement a comparison and a discussion. The results show that the penalization parameter cannot be too small, otherwise it may cause a large condition number; it cannot be too large either, otherwise it may change the properties of equations, leading to retrieved wind direction along the radial direction. Compared with the original algorithm, the new algorithm has definite superiority with the appropriate penalization parameters for small analysis volumes. When the suggested small analysis volume dimensions and penalization parameter values are adopted, the retrieval accuracy can be improved by 10 times more than the traditional method. As a result, the new algorithm has the capability to analyze the dynamical structures of severe weather, which needs high-resolution retrieval, and the potential for quantitative applications such as the assimilation in numerical models, but the retrieval accuracy needs to be further improved in the future. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

Evaluation of a Support Vector Machine–Based Single-Doppler Wind Retrieval Algorithm

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
D.O.I.
10.1175/JTECH-D-16-0199.1
Publisher site
See Article on Publisher Site

Abstract

AbstractWind retrieval algorithms are required for Doppler weather radars. In this article, a new wind retrieval algorithm of single-Doppler radar with a support vector machine (SVM) is analyzed and compared with the original algorithm with the least squares technique. Through an analysis of coefficient matrices of equations corresponding to the optimization problems for the two algorithms, the new algorithm, which contains a proper penalization parameter, is found to effectively reduce the condition numbers of the matrices and thus has the ability to acquire accurate results, and the smaller the analysis volume is, the smaller the condition number of the matrix. This characteristic makes the new algorithm suitable to retrieve mesoscale and small-scale and high-resolution wind fields. Afterward, the two algorithms are applied to retrieval experiments to implement a comparison and a discussion. The results show that the penalization parameter cannot be too small, otherwise it may cause a large condition number; it cannot be too large either, otherwise it may change the properties of equations, leading to retrieved wind direction along the radial direction. Compared with the original algorithm, the new algorithm has definite superiority with the appropriate penalization parameters for small analysis volumes. When the suggested small analysis volume dimensions and penalization parameter values are adopted, the retrieval accuracy can be improved by 10 times more than the traditional method. As a result, the new algorithm has the capability to analyze the dynamical structures of severe weather, which needs high-resolution retrieval, and the potential for quantitative applications such as the assimilation in numerical models, but the retrieval accuracy needs to be further improved in the future.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Aug 13, 2017

References

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