Wei Fang1,2,*, Lin Pang1, Weinan Yi1, Victor S. Sheng3
Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 453-466, 2021, DOI:10.32604/iasc.2021.016589
Abstract Precipitation nowcasting has become an essential technology underlying various public services ranging from weather advisories to citywide rainfall alerts. The main challenge facing many algorithms is the high non-linearity and temporal-spatial complexity of the radar image. Convolutional Long Short-Term Memory (ConvLSTM) is appropriate for modeling spatiotemporal variations as it integrates the convolution operator into recurrent state transition functions. However, the technical characteristic of encoding the input sequence into a fixed-size vector cannot guarantee that ConvLSTM maintains adequate sequence representations in the information flow, which affects the performance of the task. In this paper, we propose Attention ConvLSTM Encoder-Forecaster(AttEF) which allows… More >
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