PPNet: A Precipitation Nowcasting Model Based on Pre-Prediction

SONG Yi; ZHANG Hanyi; SUN Feng; ZHANG Jinglin; BAI Cong

doi:10.11999/JEIT230547

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 492-502

SONG Yi, ZHANG Hanyi, SUN Feng, ZHANG Jinglin, BAI Cong. PPNet: A Precipitation Nowcasting Model Based on Pre-Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(2): 492-502. doi: 10.11999/JEIT230547

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SONG Yi, ZHANG Hanyi, SUN Feng, ZHANG Jinglin, BAI Cong. PPNet: A Precipitation Nowcasting Model Based on Pre-Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(2): 492-502. doi: 10.11999/JEIT230547

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PPNet: A Precipitation Nowcasting Model Based on Pre-Prediction

doi: 10.11999/JEIT230547 cstr: 32379.14.JEIT230547

1.
PIESAT Information Technology Co., Ltd., Beijing 100195, China
2.
Zhejiang University of Technology, The College of Computer Science and Technology, Hangzhou 310023, China
3.
Shandong University, The School of Control Science and Engineering, Jinan 250100, China

Funds: Zhejiang Provincial Natural Science Foundation of China (LR21F020002), The Major Basic Research Projects of Shandong Province (ZR2022ZD32), The Key Research and Development Program of Jiangsu Province (BE2021093)

Received Date: 2023-06-05
Rev Recd Date: 2023-09-26

Available Online: 2023-10-18

Publish Date: 2024-02-29

Abstract

Abstract

Precipitation nowcasting has always been a hot research topic in weather forecasting. Traditional forecasting methods are based on numerical weather prediction. But recently the radar extrapolation-based methods using deep learning have attracted many researchers' attentions. Among them, the temporal prediction network cannot be calculated in parallel, which causes it to take too long time and has the problem of gradient explosion. The fully convolutional networks can solve the above two problems, but it does not have the ability to extract temporal information. Therefore, based on Taylor frozen hypothesis, a 2D fully convolutional Pre-predicted Precipitation nowcasting Network (PPNet) with a pre-prediction auxiliary inference structure is proposed. The network firstly extracts coarse-grained temporal and spatial information, and then uses the fully convolution structure to refine the feature granularity thereby effectively remitting the drawback that 2-D convolutional networks cannot extract temporal information. In addition, the paper provides a temporal features constraint structure to constrain the pre-predicted features and the structure makes the extracted features more realistic. The ablation experiments prove that the proposed pre-prediction auxiliary inference structure and temporal features constraint structure have excellent ability to extract temporal features and improve the sensitivity of the network to temporal features. Compared with the current best rainfall prediction algorithms and video prediction algorithms, the paper's network achieves better prediction results, especially in the rainstorm area.
- Precipitation nowcasting,
- Fully convolution,
- Pre-prediction,
- Taylor frozen hypothesis,
- Feature constraints

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References(24)

References

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