Applying Classification to Rainfall Nowcasting with Topographical Awareness

Abstract

Rainfall nowcasting provides the estimations of rainfall condition, such as accumulated precipitation, probability of precipitation forecast, and rainfall intensity prediction. Although numerical weather prediction (NWP) can simulate the atmospheric conditions, limited by the computation performance and the initial field data, the NWP does not perform well in short-term forecasting. Since atmosphere environment is a complex non-linear system, we used the deep learning approach to learn and perform the rainfall nowcasting. In this paper, we used the classification model based on a residual network and added the "side path" to input the additional data which could assist our model in acquiring prior knowledge. For the experiment, we input the topographic data to help the model include topographical awareness. In our experiment, the model trained by the additional topographic data achieved the higher accuracy than the model lacking the topographical recognition.