Traditional variational data assimilation （VDA） with only one regularization parameter constraint cannot produce optimal error tuning for all observations. In this paper, a new data assimilation method of ＂four dim...Traditional variational data assimilation （VDA） with only one regularization parameter constraint cannot produce optimal error tuning for all observations. In this paper, a new data assimilation method of ＂four dimensional variational data assimilation （4D-Var） with multiple regularization parameters as a weak constraint （Tikh-4D-Var）＂ is proposed by imposing different reg- ularization parameters for different observations. Meanwhile, a new multiple regularization parameters selection method, which is suitable for actual high-dimensional data assimilation system, is proposed based on the posterior information of 4D-Var system. Compared with the traditional single regularization parameter selection method, computation of the proposed multiple regularization parameters selection method is smaller. Based on WRF3.3.1 4D-Vat data assimilation system, initiali- zation and simulation of typhoon Chaba （2010） with the new Tikh-4D-Var method are compared with its counterpart 4D-Var to demonstrate the effectiveness of the new method. Results show that the new Tikh-4D-Var method can accelerate the con vergence with less iterations. Moreover, compared with 4D-Var method, the typhoon track, intensity （including center surface pressure and maximum wind speed） and structure prediction are obviously improved with Tikh-4D-Var method for 72-h pre- diction. In addition, the accuracy of the observation error variances can be reflected by the multiple regularization parameters.展开更多
基金supported by National Natural Science Foundation of China(Grants Nos.41230421,41005029,41105012,41375106 and 41105065)National Public Benefit(Meteorology)Research Foundation of China(Grant No.GYHY 201106004)
文摘Traditional variational data assimilation （VDA） with only one regularization parameter constraint cannot produce optimal error tuning for all observations. In this paper, a new data assimilation method of ＂four dimensional variational data assimilation （4D-Var） with multiple regularization parameters as a weak constraint （Tikh-4D-Var）＂ is proposed by imposing different reg- ularization parameters for different observations. Meanwhile, a new multiple regularization parameters selection method, which is suitable for actual high-dimensional data assimilation system, is proposed based on the posterior information of 4D-Var system. Compared with the traditional single regularization parameter selection method, computation of the proposed multiple regularization parameters selection method is smaller. Based on WRF3.3.1 4D-Vat data assimilation system, initiali- zation and simulation of typhoon Chaba （2010） with the new Tikh-4D-Var method are compared with its counterpart 4D-Var to demonstrate the effectiveness of the new method. Results show that the new Tikh-4D-Var method can accelerate the con vergence with less iterations. Moreover, compared with 4D-Var method, the typhoon track, intensity （including center surface pressure and maximum wind speed） and structure prediction are obviously improved with Tikh-4D-Var method for 72-h pre- diction. In addition, the accuracy of the observation error variances can be reflected by the multiple regularization parameters.
