A Climate Prediction Method Based on EMD and Ensemble Prediction Technique |
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| Authors: | Shuoben Bi Shengjie Bi Xuan Chen Han Ji Ying Lu |
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| Institution: | 1.School of Geographic Sciences,Nanjing University of Information Science & Technology,Nanjing,People’s Republic of China;2.Department of Computer Science,Dartmouth College,Hanover,USA;3.School of Computer & Software,Nanjing University of Information Science & Technology,Nanjing,China |
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| Abstract: | Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction. |
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