Radar Echo Spatiotemporal Sequence Prediction Using an Improved ConvGRU Deep Learning Model

Wei He, Taisong Xiong, Hao Wang, Jianxin He, Xinyue Ren, Yilin Yan, Linyin Tan

Atmosphere · 06 January 2022 · China Meteorological Administration (CMA)

In one sentence. Proposes M-ConvGRU, a convolutional GRU with an encoder-forecaster design for radar echo prediction, cutting MAE 6.29% and MSE 10.25% versus ConvLSTM.

Abstract

Precipitation nowcasting is extremely important in disaster prevention and mitigation, and can improve the quality of meteorological forecasts. In recent years, deep learning-based spatiotemporal sequence prediction models have been widely used in precipitation nowcasting, obtaining better prediction results than numerical weather prediction models and traditional radar echo extrapolation results. Because existing deep learning models rarely consider the inherent interactions between the model input data and the previous output, model prediction results do not sufficiently meet the actual forecast requirement. We propose a Modified Convolutional Gated Recurrent Unit (M-ConvGRU) model that performs convolution operations on the input data and previous output of a GRU network. Moreover, this adopts an encoder–forecaster structure to better capture the characteristics of spatiotemporal correlation in radar echo maps. The results of multiple experiments demonstrate the effectiveness of the proposed model. The balanced mean absolute error (B-MAE) and balanced mean squared error (B-MSE) of M-ConvGRU are slightly lower than Convolutional Long Short-Term Memory (ConvLSTM), but the mean absolute error (MAE) and mean squared error (MSE) of M-ConvGRU are 6.29% and 10.25% lower than ConvLSTM, and the prediction accuracy and prediction performance for strong echo regions were also improved.

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