孙泓川,吴海英,曾明剑,程丛兰. 2022. 结合中尺度模式物理约束的雷达回波临近外推预报方法研究. 气象学报,80(2):257-268. DOI: 10.11676/qxxb2022.020
引用本文: 孙泓川,吴海英,曾明剑,程丛兰. 2022. 结合中尺度模式物理约束的雷达回波临近外推预报方法研究. 气象学报,80(2):257-268. DOI: 10.11676/qxxb2022.020
Sun Hongchuan, Wu Haiying, Zeng Mingjian, Cheng Conglan. 2022. A study on radar echo extrapolation nowcasting method combined with physical constraints of mesoscale model. Acta Meteorologica Sinica, 80(2):257-268. DOI: 10.11676/qxxb2022.020
Citation: Sun Hongchuan, Wu Haiying, Zeng Mingjian, Cheng Conglan. 2022. A study on radar echo extrapolation nowcasting method combined with physical constraints of mesoscale model. Acta Meteorologica Sinica, 80(2):257-268. DOI: 10.11676/qxxb2022.020

结合中尺度模式物理约束的雷达回波临近外推预报方法研究

A study on radar echo extrapolation nowcasting method combined with physical constraints of mesoscale model

  • 摘要: 研究设计了一种结合中尺度模式物理约束的雷达回波临近智能外推预报方法,该方法在外推预报时效(0—2 h)内即利用中尺度高分辨率模式信息对外推进行约束。首先将模式风场和雷达回波轨迹风场融合成融合风场,然后利用融合风场光流外推形成动力约束外推;并在此基础上利用模式诊断产品和雷达历史资料通过投票回归器集成多种深度学习算法构建回波强度频率分布的预测模型,最终基于预测模型结果利用降水频率匹配订正技术对外推预测的原始回波强度进行订正形成物理约束外推方法。通过2个典型个例,以及2年主汛期的长期检验对原始光流法、动力约束外推方法和物理约束外推方法进行综合评估,结果表明:动力约束外推通过改善光流法回波在边缘的堆积扭曲从而改进了预报性能,物理约束外推通过基于模式信息预测的回波频率分布调整回波强度实现回波的增强和减弱来改善预报性能,随着时效延长改善越来越明显,整体而言物理约束外推是其中最优的方案。

     

    Abstract: This study develops an intelligent extrapolation nowcasting method combined with physical constraints of mesoscale numerical model. The method uses the high-resolution mesoscale model information to constrain the extrapolation within 2 h. Based on optical flow (OF) method extrapolation, the dynamic constraint extrapolation optical flow (DCOF) method is applied to reconstruct the motion vector; the mesoscale numerical model diagnostic products and radar historical data are used to train the echo intensity frequency prediction model, which integrates multiple deep learning algorithms through the voting regressor. Finally, a physical constraint optical flow (PCOF) method is used to correct the original echo intensity of the OF method by using the frequency-matching method. The performance of the OF method, DCOF method and PCOF method in 2 typical cases and cases over a long-term period are evaluated. The results show that the DCOF method improves the prediction by improving the unrealistic accumulation and distortion produced by the OF method at the edge of the echoes. The PCOF method improves the prediction by adjusting the echo intensity frequency distribution prediction based on the model information. With increasing forecast lead time, the improvements caused by the two methods are becoming more obvious. Overall, the PCOF method is the best among the three methods.

     

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