Correction model for the temperature of numerical weather prediction by SVM

Jing Zeng; Changjiang Zhang; Huiyuan Wang; Hai Chu

doi:10.1117/12.2550788

31 January 2020 Correction model for the temperature of numerical weather prediction by SVM

Jing Zeng, Changjiang Zhang, Huiyuan Wang, Hai Chu

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270Z (2020) https://doi.org/10.1117/12.2550788
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Jing Zeng, Changjiang Zhang, Huiyuan Wang, and Hai Chu "Correction model for the temperature of numerical weather prediction by SVM", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270Z (31 January 2020); https://doi.org/10.1117/12.2550788

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