Multiscale attentional coding-dynamic decoding network for short-range precipitation nowcasting

Du Xianjun; Guo Hangfei; Cheng Shengyi

doi:10.12284/hyxb2024119

Volume 46 Issue 12

Dec. 2024

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Du Xianjun，Guo Hangfei，Cheng Shengyi. Multiscale attentional coding-dynamic decoding network for short-range precipitation nowcasting[J]. Haiyang Xuebao，2024, 46(12)：122–134 doi: 10.12284/hyxb2024119

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Multiscale attentional coding-dynamic decoding network for short-range precipitation nowcasting

doi: 10.12284/hyxb2024119

College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Received Date: 2024-06-19
Rev Recd Date: 2024-10-17
Publish Date: 2024-12-06

Abstract

Abstract

Short-term precipitation nowcasting is a critical task in both meteorology and hydrology. However, current deep learning methods often yield ambiguous prediction results and exhibit significant cumulative errors. To address the limitations associated with these predictive methods, particularly the challenges of cumulative error and lack of clarity in prediction sequences, we propose a novel approach based on a Multi-scale Attention Encoding-Dynamic Decoding Network (MAEDDN) for short-term precipitation nowcasting. This method leverages the learning of spatiotemporal features from input data to accurately predict future precipitation scenarios. To obtain richer feature information from the input sequences, the encoding process employs convolutional blocks with spatial and channel attention for encoding. And a multi-scale fusion module is introduced to address the challenge of capturing both small-scale and large-scale information in precipitation distribution simultaneously. To enhance the clarity of the predicted sequences, the model needs to better understand the precipitation process. Therefore, in the decoding process, a dynamic decoding network is proposed in response to the generation and dissipation processes accompanying short-term precipitation. This network flexibly filters the decoding process by learning the intensity distribution and change trends of past input data. Experiments are conducted by using the precipitation data from the open-source SEVIR dataset, and comparisons are made with the best methods reported so far. The experimental results reveal that: (1) MAEDDN enhances the forecasting capability in areas with high-intensity precipitation, and (2) The clarity of the predicted image sequences by MAEDDN is significantly better than that of other models. The constructed multi-scale attention encoding captures the complex relationships in meteorological data more effectively, while the dynamic decoding adapts the decoding process based on different scenarios, resulting in more accurate prediction outcomes.
- precipitation nowcasting,
- dynamic decoding network,
- spatiotemporal features,
- Multi-scale fusion

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References

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