Dynamic optimal multi-indexes configuration for estimating the prediction errors of dynamical climate model in North China

Abstract

Based on the idea of using the historical-analogue information to revise the prediction errors of National Climate Centre numerical business model, for North China, based on analysis data of the CMAP from 1983 to 2009, 40 pieces of climate indices from NOAA, 27 years of the season prediction model results from 1983 to 2009 and 74 pieces of circulation characteristics materials provided by Weather Diagnostic Forecasting Room of National Climate Center, using the method of combining data analysis and numerical simulation of diagnostic tests, taking the advantage of the prediction error of the key information of similar model from the historical data, by identifying key factors, optimizing allocation of the different factors of different forecasting years, we established specific multi-factor dynamic optimal portfolios to revise prediction errors in different periods of the power-statistical model in North China, and constructed early environmental factors similar to field multiple objective criteria, to develop new technology of revising prediction errors from the power-statistical model based on dynamic optimal combination of multi-factor, and improved the prediction effect in the summer precipitation in North China and the forecasting skills. Results of independent sample return of 2005—2009 shows that, the score of similarity revised method has improved significantly compared with the score of systematic revised method. The method has a good prospect for summer precipitation forecast in North China, and is going to be put into operation.