A Forecast Error Correction Method in Numerical Weather Prediction by Using Recent Multiple-time Evolution Data  

作　　者：薛海乐 沈学顺 丑纪范 

机构地区：[1]School of Atmospheric Sciences, Lanzhou University [2]State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences [3]Center for Numerical Prediction, China Meteorological Administration

出　　处：《Advances in Atmospheric Sciences》2013年第5期1249-1259,共11页大气科学进展（英文版）

基　　金：funded by the Special Scientific Research Project for Public Interest (GYHY201206009);the National Key Technologies Research and Development Program (Grant No. 2012BAC22B02);the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064);the Special Scientific Research Project for Public Interest (Grant No. GYHY201006013);the National Natural Science Foundation of China (Grant No. 41105070 )

摘　　要：The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction （NWP）. By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polyno- mial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria： （1） the length of past data sufficient for estimation of the model errors, （2） a proper method of estimating the term ＂model integration with the exact solution＂ when solving the inverse problem, and （3） the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that （1） a few data limited by the order of the corrector can be used, （2） trapezoidal approximation can be employed to estimate the ＂term＂ in this study; however, a more accurate method should be explored for an operational NWP model, and （3） the correction is sensitive to observational error.The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction （NWP）. By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polyno- mial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria： （1） the length of past data sufficient for estimation of the model errors, （2） a proper method of estimating the term ＂model integration with the exact solution＂ when solving the inverse problem, and （3） the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that （1） a few data limited by the order of the corrector can be used, （2） trapezoidal approximation can be employed to estimate the ＂term＂ in this study; however, a more accurate method should be explored for an operational NWP model, and （3） the correction is sensitive to observational error.

关 键 词：numerical weather prediction past data model error inverse problem 

分 类 号：P456.7[天文地球—大气科学及气象学] TN957.51[电子电信—信号与信息处理]