Summer precipitation estimat ion is one of the key and difficult tasks in short -term climate prediction because of the largeamount of convective precipitation in summer which is characterized by uneven distribution, large intensity, shortduration and rapid change with time. In order to improve the accuracy of summer precipitation estimation, an efficientmethod by mult i-t ime scale Support Vector Machine (SVM) with quantum optics inspired optimization (QOIO) isproposed in this paper. And the performance of the proposed method is verified by radar reflect ivity and precipitationdata of automatic weather stations (AWSs) in Shanghai. Using radar reflectivity and precipitat ion in the most relevanttime 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 andordinary SVM, the results show the higher Threat Score and lower root mean square error can be obtained by theproposed method in summer precipitation estimation.
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