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Convective Precipitation Nowcasting Using U-Net Model
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AI
DOI：10.1109/TGRS.2021.3100847.png)
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Abstract
En 中文
Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.
Keywords:
Radar
Radar imaging
Storms
Training
Meteorological radar
Correlation
Predictive models
Deep learning
precipitation nowcasting
U-Net
weather radar
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W