A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation

Jinliang Yao, Feifan Xu, Zheng Ming Qian, Zhipeng Cai

IEEE Access · 01 January 2023

In one sentence. A DyConvGRU and U-Net forecast-refinement network with WGAN training improves high-reflectivity radar echo extrapolation over Shanghai, raising POD and CSI.

Abstract

Precipitation nowcasting is very important for the sectors which critically depend on timely and accurate weather information. One of the challenges of precipitation nowcasting is radar echo extrapolation which predicts the radar echo images accurately. Nowadays, the methods of radar echo extrapolation are mostly based on ConvRNNs. Unfortunately, as lead time increases, these methods unavoidably suffer from the problem that high reflectivity values are underestimated. Therefore, we propose a forecast-refinement neural network based on DyConvGRU and U-Net to improve the predicting ability for high reflectivity during radar echo extrapolation. To improve the model’s ability to predict high reflectivities, dynamic convolution, and the forecast-refinement architecture are applied. And to obtain more realistic results, the WGAN’s training strategy is adopted to train the forecast module and the refinement module. Through experiments on a radar dataset from Shanghai, China, the results show that our proposed method obtains higher Probability of Detection (POD), Critical Success Index (CSI), Heidke Skill Score (HSS), and lower False Alarm Rate(FAR).

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