HPRNN: A Hierarchical Sequence Prediction Model for Long-Term Weather Radar Echo Extrapolation
Jinrui Jing, Qian Li, Xuan Peng, Qiang Ma, Shaoen Tang
Abstract
Weather radar echo extrapolation has been one of the most important means for weather forecasting and precipitation nowcasting. However, the effective forecasting time of the most current extrapolation methods is usually short. In this paper, to meet the demand for long-term extrapolation in actual forecasting practice, we propose a hierarchical prediction recurrent neural network (HPRNN) for long-term radar echo extrapolation. HPRNN is composed of hierarchically stacked RNN modules and a refinement module, it employs both a hierarchical prediction strategy and a recurrent coarse-to-fine mechanism to alleviate the accumulation of prediction error with time and contribute to making long-term extrapolation. The extrapolation experiments conducted on the HKO-7 radar echo dataset demonstrate the effectiveness of our model.