Effect of Data Assimilation Using WRF-3DVAR for Heavy Rain Prediction on the Northeastern Edge of the Tibetan Plateau

Abstract

The numerical weather prediction (NWP) is gaining more attention in providing high-resolution rainfall forecasts in the arid and semiarid region. However, the modeling accuracy is negatively affected by errors in the initial conditions. Here we investigate the potential of data assimilation in improving the NWP rainfall forecasts in the northeastern Tibetan Plateau. Three of three-dimensional variational (3DVar) data assimilation experiments were designed on running the advanced research weather research forecast (WRF) model. Two heavy rain events selected with different rainfall distribution in space and time are utilized to examine the improvement for rainfall forecast after data assimilation. For the spatial distribution, the improvement of rainfall accumulation and area is obvious for the both two events. But for the temporal variation, the improvement is more obvious for the event with even rainfall distribution in time, while the effect of data assimilation is not ideal for the rainfall event with uneven distribution in space and time. It is noteworthy that, for both the spatial and temporal distribution of rainfall, satellite radiances have greater effect on rainfall forecasts than surface and upper-air meteorological observations in this high-altitude region. Moreover, the data assimilation experiments provide more detail information to the initial fields.