Predictive reliability of summer precipitation in China based on error distribution of numerical model

Abstract

In this paper, we made some statistical analyses on precipitation prediction errors of coupled global climate model of National Climate Center based on CPC merged analysis of precipitation. Through the statistics of the forecast results of summer precipitation in many years and the errors, we find that the distribution of the model prediction errors satisfies the Gaussian distribution. Based on the Gaussian distribution characteristics, the ability to predict the mode for summer precipitation in China can be analyzed and compared. The forecast performance of the dynamic-statistics scheme is significantly better than that of system error correction scheme of the model. The dynamic-statistics optimal combination of factor revise is corrected, relative to error distribution pattern of the system revised forecast, and there are obtained two kinds of error distribution characteristics: 1) improvement of the amplitude; 2) improvement of the displacement. Based on the statistical characteristics of the prediction error, measure of the credibility of model forecast results is proposed to quantitatively assess the credibility of the numerical model forecast results in different regions. Taking the summer precipitations in 2012 and 2013 for example, we analyze the credibility of actual observations, prediction and drought or flood level. Comparing actual observations and prediction, we find that there is a good consistency where the credibility of them is larger, while there are many bad predictions in the region where the credibility of them is smaller. What is more, compared with other reliability test method, the credibility of the abnormal precipitation forecast by this method is more accurate, showing the effectiveness of the method.