Expanded four-dimensional variatiaonal data assimilation method to optimize model physical parameters

Abstract

A critical error in numerical simulation stems from the physical parameters in the model. To better assess the accuracy of the numerical stimulation, a mthod of impoving physical parameters is urgently desired. By modifying the four-dimensional variatiaonal data assimilation 4DVAR technique, in this paper a new method is proposed based on the use of observational data to optimize initial field and subsequent physical model. Ekman boundary layer model and Lorenz model are taken for example to conduct numerical experiment. The results show that through the variations in observational data, physical parameters and initial field are improved, thus effectively enhancing the accuracy of the model. This method improves the numerical model and physical parameters.