Recent Progress on the Objective and Quantifiable Forecast of Summer Precipitation Based on Dynamical-statistical Method

Short-term climatic prediction, which mainly aims at monthly, seasonal and annual time scales, is very important for the public and government decision making. The trend of summer flood and drought distribution is one of the most important contents in operational forecast. Generally, there are two types of forecasting methods, including statistical method and dynamical method, which both have advantages and disadvantages. Therefore, the general consensus is to let them learn from each other, merging and developing. During recent 50 years, the Dynamical-Statistical Integration Forecasting Method (DSIFM) has made great progresses in dealing with the complex scientific issue of summer precipitation forecasting in China and abroad.The research results in early period and the development about DSIFM are briefly reviewed, as well as the two forms of dynamical-statistical integration forecasting method. And then, the principle, processes and programs of Dynamical-Statistical Objective Quantitative Forecasting (DSOQF) in recent operational forecast are systematically introduced. Based on the Coupled Global Circulation Model (CGCM) of National Climate Center and two types of prediction scheme of DSOQF, a dynamical-statistical integrated forecasting system for seasonal precipitation (FODAS1.0) is set up, which fully assimilates existing research and profession achievements, especially forecaster diagnostic techniques and forecasting experience from national, regional and provincial climate centers. Suitable regional climate characteristics prediction scheme is also developed based on the theory and methods of DSOQF. By now, FODAS1.0 achieves quasi-operational trial in National Climate Center, 8 regional climate centers and Guangxi, Shandong and other provincial climate centers.Experimental predictions are carried out for the summer rainfall in China from 2009 to 2012 with the method of DSOQF. The predictive score (PS) from 2009 to 2012 are 79, 72, 70 and 70, respectively. The anomaly correlation coefficient (ACC) from 2009 to 2012 are 0.38, 0.10, 0.12 and 0.03. For abnormal years such as 2010 and 2011, diagnostic analysis is performed. Overall, the forecast results are ideal, but it still needs further improving.The problems in DSIFM and the solutions are also discussed. The forecasting skills can be improved by strengthening diagnostic analysis of the relationship between precipitation and its main factors, improving the physics processes and parameterization scheme of short-term climate models, and developing the targeted regional climate models. The DSIFM will be more useful in the future.