ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 43 Issue 9 > 2017,43(9):1110-1116. DOI:10.7519/j.issn.1000-0526.2017.09.008 Prev Next
The MediumTerm MultiModel Integration Forecast Experimentation for Heavy Rain Based on Support Vector Machine
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Abstract:
This paper establishes a multimode integrated dynamicstatistical objective forecast model (SVM multimodel integration forecast) based on the European Centres for MediumRange Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction Center (NCEP) ensemble forecast data and support vector machine regression method, then carries out a forecast test for heavy rain process that occurred in the Huaihe River Basin and its south of China during the period from May to September in 2012, and finally the forecast results are compared with the control forecast and ensemble average forecast of ECMWF. The results show that in the mediumterm forecasting timescale (4-7 days), the SVM multimodel integrated forecast method is the best for forecasting heavy rain compared with the control forecast of the ECMWF and the ensemble average forecast during the period from May to September in 2012. Especially for the accuracy of rainstorm forecasting, it is more effective, and the advantage is that its forecast of the distribution and intensity of heavy rain is closer to the observation.
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Clc Number:
P456
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