Contextual Sa-Attention Convolutional LSTM for Precipitation Nowcasting: A Spatiotemporal Sequence Forecasting View

Taisong Xiong, Jianxing He, Hao Wang, Xiaowen Tang, Zhao Shi, Qiangyu Zeng

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 01 January 2021

In one sentence. Self-attention ConvLSTM encoder-forecaster for radar-based precipitation nowcasting, evaluated on HKO-7 radar echo sequences with improved visual and quantitative results.

Abstract

Precipitation nowcasting is an important tool for nowcasting weather. In recent years, progress has been achieved in some models based on deep learning for precipitation nowcasting. However, these models do not consider the contextual relationships between the input data and the output of a network and their deficiency in capturing the information of prediction objects. To overcome these shortcomings, in this study, we propose a model that performs convolution operation on input data and the output of a Long short-term memory (LSTM) networks. Secondly, a self-attention operation is added to capture the local and global dependencies of the hidden state of LSTM. The proposed network structure is inserted in an encodingforecasting network framework and applied to spatiotemporal sequence forecasting. Thirdly, the outputs of the precede sequence are also regarded as the inputs of according LSTM layer and this operation effectively captures temporal feature of sequence data. Comprehensive experiments are conducted on the KTH action dataset and HKO-07 radar echo maps dataset. The visual and quantitative prediction results demonstrate the accuracy and efficacy of the proposed model.

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