Use of the Mesoscale Self-Memorization Model in the Heavy Rainfall Forecasting Experiments
中尺度自忆模式在强降水预报中的应用

In view of the fact that the atmospheric motion is an irreversible process, a memory function which can recall the observation data in the past has been introduced. Retrospective time integration scheme contains historical information so this scheme adapts to mesoscale weather forecast. The purpose of this paper is to apply this scheme to MM5 model and validate the efficiency of this scheme. Based on the atmospheric self-memorization principle, the retrospective time integration scheme in a mesoscale numerical model is developed which is called SMM5, and the experimental results are compared with the kernel model MM5. It shows that because of using information of several history fields, SMM5 can improve the prediction accuracy. As to the rainfall field, both the precipitation areas and precipitation intensities of SMM5 is more similar to the observed field than that of MM5.