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| DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL | |
| 作者姓名： | 何慧 金龙 覃志年 袁丽军 |
| 作者单位： | Guangxi Climate Center，Guangxi Research Institute of Meteorological Disasters Mitigation，Guangxi Climate Center，Guangxi Meteorological Science and Technology Information Center Nanning 530022 China，Nanning 530022 China，Nanning 530022 China，Nanning 530022 China |
| 摘 要： | Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast. |
| 关 键 词： | 天气预报 月份 预报方法 网络 |
DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL |
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| HE Hui,JIN Long,QIN Zhi-nian,YUAN Li-jun.DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL[J].Journal of Tropical Meteorology,2007,13(1):97-100. | |
| Authors: | HE Hui JIN Long QIN Zhi-nian YUAN Li-jun |
| Institution: | 1. Guangxi Climate Center, Nanning 530022 China 2. Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022 China 3. Guangxi Meteorological Science and Technology Information Center, Nanning 530022 China |
| Abstract: | Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast. |
| Keywords: | monthly dynamic extended range forecast neural network model downscaling forecast prediction error |
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