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Advancing Radar Nowcasting Through Deep Transfer Learning
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DOI：10.1109/TGRS.2021.3056470.png)
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Deep learning is emerging as a powerful tool in scientific applications, such as radar-based convective storm nowcasting. However, it is still a challenge to extend the application of a well-trained deep learning nowcasting model, which demands to incorporate the learned knowledge at a certain location to other locations characterized by different precipitation features. This article designs a transfer learning framework to tackle this problem. A convolutional neural network (CNN)-based nowcasting method is utilized as the benchmark, based on which two transfer learning models are constructed through fine-tune and maximum mean discrepancy (MMD) minimization. The base CNN model is trained using radar data in the source study domain near Beijing, China, whereas the transferred models are applied to the target domain near Guangzhou, China, with only a small amount of data in the target area. The influence of a varying number of target data samples on the nowcasting performance is quantified. The experimental results demonstrate that the deep transfer learning models can improve the nowcasting skills.
Keyword:
Radar
Data models
Transfer learning
Deep learning
Training
Computational modeling
Task analysis
Convective storm nowcasting
deep learning
transfer learning
weather radar
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IF:
8.6
论文数:
2.1W
被引数:
10.7W
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