台风北冕 (0809) 数值预报敏感初始误差研究

基于WRF四维变分伴随模式建立数值预报敏感初始误差计算流程并对台风北冕 (0809) 进行了分析。结果表明:基于线性化近似的伴随敏感分析方法对台风系统在24 h内适用。构造敏感初始误差的参考系数存在一个合理的取值范围，参考系数取为0.08效果最好。在初始场中去除敏感初始误差能够有效减少预报误差，改善台风路径预报效果，依据24 h预报误差计算出的敏感初始误差订正对24 h后台风数值预报效果也有明显影响。另外，敏感初始误差分布在台风中心附近，伴随台风系统环流且各物理量分布形态相似。对流层下层和中上层的敏感初始误差均对数值预报效果有所影响，对流层中上层的作用略大于对流层下层。敏感初始误差中各物理量对数值预报改善的贡献各不相同，相对而言，风场的贡献最大。

Influences of Sensitive Initial Error on the Numerical,Forecast of Typhoon Kammuri(0809)

Initial error is one of the key factors that have great effects on the accuracy of numerical forecast. To study the characteristics of initial error and its influence on the numerical prediction, an analysis procedure of the sensitive initial error of numerical forecast is developed based on WRF adjoint model and is used in the investigation of typhoon Kammuri(0809). The validity of the linear assumption on the study of typhoon case is firstly assessed prior to discussing any adjoint analysis results. It is done by evaluating the evolution differences of the perturbation between linear and nonlinear development, showing that the nonlinear perturbation evolution is well represented by the linear assumption during 24 h forecast. The sensitive initial error is then constructed using the information derived from adjoint sensitivity analysis, finding that the reference coefficient from 0.01 to 0.08 is proper to build the sensitive initial error. The result of 0.08 is the best in this case study. The numerical forecast error could be reduced and the prediction bias of typhoon trace could be improved greatly by removing the sensitive initial error from the initial field. This effect of the sensitive initial error derived from 24 h numerical forecast error affects the numerical forecast even within 60 hours. In addition, the analysis reveals that the sensitive initial error of regional short term numerical forecast concentrates mainly around the weather system. It goes with typhoon circle and the pattern is almost consistent for all physical variables. The sensitive initial error in the middle upper troposphere has slightly more contribution to the forecast than that in lower troposphere. Comparing the contribution of different physical variable, it is found that wind is the main contributor with pressure and humidity following.