Variational method study to correct the forecast error of GRAPES model

In this study, forecast error is devived into two components, i.e., systematic forecast error and non-systematic forecast error. After the correction of systematic forecast error, variational method is used to estimate and correct the non-systematic forecast error by building the mapping relationship between the state variable and non-systematic forecast error. The mapping relationship can be built by two schemes. One is by using the historical samples of the same period (DEM), and the other is by using "similarity samples"(SEM). The hind-cast data of 48 h forecast of 500 hPa height field generated by GRAPES model in July from 2002 to 2010 is used to test the effectiveness of the above two methods. The analysis data from NCEP-FNL is used as validation data to assess the result of the methods. The result shows that the correction efficiency is 98.566% for DEM and 100% for SEM. Both the DEM method and SEM method has certain capability to esti-mate the non-systematic forecast error.