The optimization of error covariances in the KMA UM 4D-Var system using the forecast sensitivity to error covariance weightings
In addition, this study estimated the optimized error covariances using the multiple linear regression of the sensitivity data corresponding to the summer and winter months, and investigated the forecast error reduction when the optimized error covariances were used in the KMA UM 4D-Var system for August 2011 and February 2012. The predictors (i.e., weightings) were estimated using the preconditioned conjugate gradient algorithm. The multiple linear regression method diagnosed that to reduce the forecast error the inflation of the background error covariance is be necessary by 30 %, whereas the deflation of most observation error covariances is necessary. More detailed results will be presented in the conference.