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Proceedings Paper

Correction model for the temperature of numerical weather prediction by SVM
Author(s): Jing Zeng; Changjiang Zhang; Huiyuan Wang; Hai Chu
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Paper Abstract

In order to improve the accuracy of numerical weather prediction(NWP) temperature, a support vector machine (SVM) model based on LASSO feature analysis is proposed to revise the predicted temperature for the next 12 hours. In this paper, high-resolution mode prediction data that include 2m temperature and related meteorological factors forecasted by the European Center of Medium range Weather Forecast ( ECMWF) , and the temperature data of the automatic stations in East China and coastal areas provided by the Shanghai Meteorological Bureau are used to build the proposed model. , In this paper, The results show that the root mean square error, absolute error and accuracy are greatly improved by the proposed prediction model. The comprehensive performance of the proposed method is better than that of the traditional linear regression technology.

Paper Details

Date Published: 31 January 2020
PDF: 7 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270Z (31 January 2020); doi: 10.1117/12.2550788
Show Author Affiliations
Jing Zeng, Zhejiang Normal Univ. (China)
Changjiang Zhang, Zhejiang Normal Univ. (China)
Huiyuan Wang, Zhejiang Normal Univ. (China)
Hai Chu, Shanghai Meteorological Ctr. (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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