Convolutional Residual-Attention: A Deep Learning Approach for Precipitation Nowcasting

Qing You Yan, Fuxin Ji, Kaichao Miao, Qi Wu, Yi Xia, Teng Li

Advances in Meteorology · 29 February 2020

In one sentence. Dual-channel CNN with multihead attention and residual connections improves radar-based local precipitation nowcasting, reaching state-of-the-art accuracy on a radar echo dataset.

Abstract

Short-term precipitation forecast in local areas based on radar reflectance images has become a hot spot issue in the meteorological field, which has an important impact on daily life. Recently, deep learning techniques have been applied to this field, and the effect is promoted remarkably compared with traditional methods. However, existing deep learning-based methods have not considered the problem that different areas and channels exert different influence on precipitation. In this paper, we propose to incorporate the multihead attention into a dual-channel neural network to highlight the key areas for precipitation forecast. Furthermore, to solve the problem of excessive loss of global information caused by the attention mechanism, the residual connection is introduced into the proposed model. Quantitative and qualitative results demonstrate that the proposed method achieves the state-of-the-art precipitation forecast accuracy on the radar echo dataset.

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