临近预报滚动融合外推方法及其适用性评估
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山西省气象局重点项目(SXKZDTC20236303)、山西省研发项目(CXFZ2024J011)共同资助


Rolling Fusion Extrapolation Method of Nowcast and Its Applicability Assessment
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    摘要:

    为实现多种临近预报外推方法的有效融合,提升临近外推的准确性。本文提出了一种将光流法(Optical Flow, OF)与深度学习(Deep Learning, DL)滚动融合的雷达组合反射率(Composite Reflectivity,CR)外推方法(Rolling Fusion Neural Network, RFNet),以提升临近预报的准确性。RFNet采用两层卷积神经网络,并通过粒子群算法(Particle Swarm Optimization, PSO)优化网络参数,解决CR强度不平衡问题。RFNet在训练中使用OF和DL外推的10个时次CR预测未来10个时次的CR,训练后的RFNet作为下一次训练的预训练模型,以提升训练效率。结果表明,RFNet有效缓解了DL的强度衰减和回波结构模糊问题。在20和30 dBz阈值处,DL和RFNet外推效果相近,均优于OF。在40 dBz阈值处,0~30 min内DL效果最好,30 min后RFNet表现最佳。在50 dBz阈值处,RFNet在42 min内显著优于DL和OF。RFNet在40 dBz以上的外推效果随CREF强度增大而提升。

    Abstract:

    In the realm of meteorological forecasting, the integration of various nowcasting extrapolation methods is critical for enhancing accuracy and reliability. This paper introduces an innovative method for radar echo extrapolation called Rolling Fusion (RF), specifically designed to improve radar composite reflectivity (CREF) extrapolation. RF represents a novel synthesis of Optical Flow (OF) and Deep Learning (DL) methodologies, targeting the enhancement of nowcasting weather predictions. Central to the RF approach is RFNet, a sophisticated tool that employs a two-layer convolutional neural network. This network is optimised using Particle Swarm Optimisation (PSO), a computational methodology inspired by the social behaviour of birds and fish. PSO is particularly valuable in refining the network’s parameters to tackle the prevalent issue of CREF intensity imbalance, which can skew forecasting results. By optimising these parameters, RFNet ensures a balanced and accurate representation of various intensity levels, crucial for predicting severe weather conditions. The training process for RFNet is meticulously structured, utilising 10 steps of CREF data extrapolated from both OF and DL methods to anticipate the subsequent 10 steps. This dynamic approach not only enables high accuracy in nowcasting predictions but also enhances training efficiency by using the initially trained RFNet as a pre-trained model for further training cycles. This layered training process reduces computational demands, making the system both time-efficient and resource-efficient. Empirical results from this study reveal that RFNet effectively mitigates common drawbacks associated with deep learning predictions, specifically intensity attenuation and echo structure blurring. These enhancements allow RFNet to provide clearer and more accurate forecasts. Performance assessments across various intensity thresholds from 20 to 50 dBz demonstrate the method’s robustness. At lower thresholds, such as 20 and 30 dBz, RFNet and DL exhibit comparable performance, both of which surpass the capabilities of OF. In these scenarios, RFNet’s advanced integration of methodologies ensures superior forecasting precision. At a 40 dBz threshold, DL initially excels within the first 30 minutes of forecast duration. However, RFNet outperforms DL beyond this timeframe, highlighting its strength in extended forecasting scenarios. Notably, at the 50 dBz threshold, RFNet displays a significant performance advantage over both DL and OF, maintaining superior forecasting ability for up to 42 minutes. This capability is particularly valuable in predicting high-intensity weather events, where rapid changes necessitate agile and accurate forecasting models. Additionally, the research indicates a trend where RFNet’s extrapolation performance improves as CREF intensity surpasses 40 dBz. This improvement underscores the system’s adaptability and effectiveness in handling severe weather conditions, ultimately contributing to more reliable and actionable nowcasting weather forecasts.

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郭文昕,李蓉,于万荣,李建强,郑宇,陈霄健,刘鑫,刘思辰,牛刘敏,杨杰,车慧正.临近预报滚动融合外推方法及其适用性评估[J].气象科技,2024,52(6):807~815

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  • 收稿日期:2024-01-16
  • 定稿日期:2024-09-18
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  • 在线发布日期: 2024-12-25
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