High spatio-temporal resolution precipitation forecast based on dual-polarization radar

doi:10.3785/j.issn.1008-973X.2026.03.013

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (3): 574-584 DOI: 10.3785/j.issn.1008-973X.2026.03.013

High spatio-temporal resolution precipitation forecast based on dual-polarization radar

Leping LIN1,2(

),Liang LI1,Ning OUYANG1,2,*(

)

1. Ministry of Education Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin 541004, China
2. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China

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Abstract

The DPRF-UNet model, a multimodal precipitation forecasting model that integrated dual-polarization radar variables, was proposed aiming at the problems that existing algorithms had limitations in extrapolating complex and rapidly changing precipitation and the Z-R conversion led to the error accumulation. Dual-polarization radar variables such as reflectivity, differential reflectivity, and specific differential phase were used to directly predict precipitation. The error introduced by empirical conversion was avoided by incorporating microphysical characteristics and employing an end-to-end precipitation forecasting framework, enabling direct precipitation prediction. The research results show that the proposed DPRF-UNet model demonstrates superior performance compared with mainstream models that solely utilize radar reflectivity as input. The average critical success index, Heidke skill score and probability of detection of the DPRF-UNet model across all precipitation thresholds were increased by 2.6%, 2.7% and 1.0%, reaching 0.64, 0.72 and 0.74.

Key words： precipitation forecast dual-polarization radar deep learning spatio-temporal prediction spatio-temporal resolution

Received: 10 January 2025 Published: 04 February 2026

CLC:

TP 391

Fund: 国家自然科学基金资助项目(62001133)；广西科技基地和人才专项资助项目(桂科 AD19110060)；广西自然科学基金资助项目(2017GXNSFBA198212)；广西无线宽带通信与信号处理重点实验室基金资助项目(GXKL06200114).

Corresponding Authors: Ning OUYANG E-mail: linleping@guet.edu.cn;ynou@guet.edu.cn

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Cite this article:

Leping LIN,Liang LI,Ning OUYANG. High spatio-temporal resolution precipitation forecast based on dual-polarization radar. Journal of ZheJiang University (Engineering Science), 2026, 60(3): 574-584.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.03.013 OR https://www.zjujournals.com/eng/Y2026/V60/I3/574

基于双偏振雷达的高时空分辨率临近降水预报

针对现有算法在复杂快变降水外推中的局限性及Z-R转换带来的误差叠加问题，提出联合双偏振雷达变量的多模态临近降水预报模型DPRF-UNet. 该预报模型利用双偏振雷达变量中的反射率因子、差分反射率和比差分相移，直接对降水量进行预测. 通过纳入降水粒子微物理特性的信息，采用端到端的降水预报框架，该模型避免了因经验性转换带来的误差，实现了降水的直接预测. 研究结果表明，与仅使用雷达反射率因子作为输入的主流模型相比，提出DPRF-UNet模型在所有降水阈值下的临界成功指数、Heidke技巧评分和命中率的平均值分别提高了2.6%、2.7%和1.0%，达到0.64、0.72和0.74.

关键词： 临近降水预报, 双偏振雷达, 深度学习, 时空预测, 时空分辨率

Fig.1 Optimization process diagram of DPRF-UNet model parameter

Fig.2 Schematic diagram of cross-attention spatial-frequency fusion module

Fig.3 Schematic diagram of integrated precipitation generation module

Fig.4 Schematic diagram of multi-scale attention convolution module

Fig.5 Schematic diagram of cross-layer fusion module

Tab.1 Hardware platform and software environment for deep learning experiment

Tab.2 Rain rate statistics in NJU-CPOL dataset

Tab.3 Comparison of experimental result for different models at 0.5 hour ahead time

Tab.4 Comparison of experimental result for different models at 1 hour ahead time

Tab.5 Comparison of experimental result for different models at 1.5 hour ahead time

Fig.6 Comparison of predictive scatterplot of different models in continuous precipitation process

Fig.7 Comparison of model prediction visualization result at different lead time

Fig.8 Comparison of model prediction visualization result at different lead time

Tab.6 Comparison of ablation experiment result at 1 hour ahead time


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