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

Author:

Xiong Taisong,He Jianxing,Wang HaoORCID,Tang Xiaowen,Shi Zhao,Zeng QiangyuORCID

Funder

National Key Research and Development Plan

Education Department of Sichuan Province

Department of Science and Technology of Sichuan Province

Sichuan Science and Technology program

Special Funds for the Central Government to Guide Local Technological Development

Open Project for the Research Center for meteorological disaster prediction, early warning, and emergency management

2018 China Meteorological Administration Soft Science Independent Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Novel Transformer Network With Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

2. Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning;Geoscientific Model Development;2023-10-20

3. Adaptive Attentions Fusion Model for Weather Recognition;2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI);2023-08-18

4. Research on Jacket Digital Twin Prediction Technology Based on LSTM;2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA);2023-08-11

5. MMSTP: Multi-modal Spatiotemporal Feature Fusion Network for Precipitation Prediction;2023 6th International Symposium on Autonomous Systems (ISAS);2023-06-23

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