Prediction of Radar Echo Space-Time Sequence Based on Improving TrajGRU Deep-Learning Model

Qiangyu Zeng, Haoran Li, Tao Zhang, Jianxin He, Fugui Zhang, Hao Wang, Zhipeng Qing, Qiu Yu, Bangyue Shen

Remote Sensing · 09 October 2022 · China Meteorological Administration (CMA)

In one sentence. T-UNet combines UNet with TrajGRU for radar echo extrapolation, improving CSI and HSS by up to 10.57% and 7.80% over 60-minute nowcasts.

Abstract

Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the utilization of radar historical data as well as overlooking the nonlinear motion of small- to medium-sized convective systems in radar echoes. To solve this, we propose a deep-learning model combining CNN and RNN. The model T-UNet proposed in this paper uses an efficient convolutional neural network of UNet architecture with a residual network, where the encoder and decoder networks are connected by nested dense skip paths, while a TrajGRU recurrent neural network is added at each layer, to achieve the perceptual capability of time series. The quantitative statistical evaluation showed that the use of T-UNet could improve the nowcasting skill (CSI score, HSS score) by a maximum of 10.57% and 7.80% over a 60 min prediction cycle. Further evaluation showed that T-UNet also improved the prediction accuracy and prediction performance in the strong echo region.

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