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.
Summer precipitation estimation is one of the key and difficult tasks in short-term climate prediction because of the large amount of convective precipitation in summer which is characterized by uneven distribution, large intensity, short duration and rapid change with time. In order to improve the accuracy of summer precipitation estimation, an efficient method by multi-time scale Support Vector Machine (SVM) with quantum optics inspired optimization (QOIO) is proposed in this paper. And the performance of the proposed method is verified by radar reflectivity and precipitation data of automatic weather stations (AWSs) in Shanghai. Using radar reflectivity and precipitat ion in the most relevant time scale, a rainfall estimation model based on multi-time scale SVM is established for each AWS to estimate next 6-minute precipitation. Compared with the traditional single Z-R relationship, linear regression, K-nearest neighbor and ordinary SVM, the results show the higher Threat Score and lower root mean square error can be obtained by the proposed method in summer precipitation estimation.
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