An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models

Short-term heavy rainfall can have a significant impact on people's production, life and travel. Numerical Weather Prediction (NWP) is complex. It can predict weather conditions for the next week or even two weeks, but cannot predict the weather in the near hours in a timely manner. The TREC and Optical Flow methods used in the meteorological field are only good at translational transformations, and it is difficult to predict the generation and dissipation of cloud systems. ConvLSTM, PredRNN, and other deep RNN-based methods input only one map at each time step, and do not make good use of the spatial information of several neighboring moments. Inspired by schedule sampling, a new sampling scheme is proposed to allow the model to effectively use the spatial information of the past few times. Also, the advantages of MSE and MAE are combined to change the loss function.