DB-RNN: An RNN for Precipitation Nowcasting Deblurring

Abstract

Precipitation nowcasting based on artificial intelligence has garnered widespread attention in the meteorological and computer communities in recent years. While new models are continuously proposed to refresh the forecasting performance, the problem of gradual blurring of forecast maps as the forecast period extends is still serious. Most models use the mean loss and the recursive prediction structure [such as multiscale recurrent neural network (MS-RNN)]. The mean loss always results in an average of future states, visually appearing as a blur. The recursive prediction method brings the accumulation of error (blur), causing the error (blur) of long-term predictions to increase exponentially. In this study, we add the adversarial loss and gradient loss to penalize the network to ease the blur caused by the averaging loss, and we introduce an additional deblurring network (composed of MS-RNN) behind the forecasting network (composed of MS-RNN) to alleviate the blur caused by the recursive structure, which reduces the blur of the current frame and then recursively and incrementally reduces the blur of subsequent frames. We name the proposed model DB-RNN, which can slow down the error accumulation and alleviate the blurring dilemma. Like MS-RNN, DB-RNN is compatible with multiple recurrent neural network models, such as ConvLSTM, TrajGRU, PredRNN, PredRNN++, MIM, MotionRNN, PrecipLSTM, etc. Experiments on two large radar datasets named HKO-7 and DWD-12 indicate that DB-RNN's predictions are more accurate and clear than those from MS-RNN.

Keywords

• Deblurring
• precipitation nowcasting
• radar video prediction
• recurrent neural network (RNN)