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Boundary Layer Convergence Line Identification Algorithm for Weather Radar Based on R2CNN
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摘要: 边界层辐合线是触发对流的中尺度天气系统之一,边界层辐合线的精细化识别对于揭示其形成、演变及与其他系统相互作用机制至关重要。目前自动识别技术在适应边界层辐合线多样性(如尺度、强度和形状)方面存在局限。旋转区域卷积神经网络(R2CNN)可提高识别准确性、鲁棒性和泛化能力。综合考虑天气雷达型号和分辨率的多样性,针对性构建识别数据集用于模型训练,调整相应参数得到识别模型,并利用交并比和置信度评估检验识别效果。结果表明:基于R2CNN的边界层辐合线识别算法在使用较低交并比阈值时命中率更高且空报率更低,当置信度为0.7时,TS(threat score)评分最高。与现有的阵风锋识别算法(Machine Intelligence Gust Front Algorithm,MIGFA)效果相比,R2CNN在减少误报、提升命中率及平衡识别频率等关键性能方面优势显著,适用于业务应用与推广。Abstract:
Boundary layer convergence lines are recognized as one of the critical mesoscale weather systems triggered convection, and also affect low-altitude flight safety. The accurate and detailed identification of these lines is considered essential for revealing their formation, evolution, and interaction mechanisms with other weather systems. However, existing automatic identification technologies are limited in their ability to adapt the diverse characteristics of these lines, such as scale, intensity, and shape. The rotational region-based convolutional neural network (R2CNN) is employed to enhance the accuracy, robustness, and generalization of the identification process. A comprehensive identification dataset has been constructed for model training, considering the diversity of weather radar models and resolutions. Relevant parameters are adjusted to derive the optimized recognition model. The intersection over union (IoU) with confidence levels are employed to comprehensively assess and validate the identification results. Results indicate that the boundary layer convergence line recognition algorithm developed achieves a higher hit rate and a lower false alarm rate at lower IoU thresholds. At a confidence level of 0.7, the threat score (TS) reaches its maximum value.
Compared to the existing Machine Intelligence Gust Front Algorithm (MIGFA), the model proposed in this study demonstrates significant advantages in reducing false alarms, improving hit rates, and achieving a balanced recognition frequency. Therefore, it is more suitable for operational applications and dissemination. This research not only provides a more effective method for identifying boundary layer convergence lines but also contributes to the improvement of low-altitude flight safety and advances meteorological detection technologies. The proposed method addresses limitations of existing technologies by effectively managing the diverse characteristics of boundary layer convergence lines. By incorporating rotational bounding boxes in the detection process, R2CNN model enhances the detection accuracy for objects with arbitrary orientations, which is particularly beneficial for meteorological phenomena that do not align with the standard axis. The constructed dataset includes a diverse collection of radar images from various models and resolutions, ensuring that the model is trained on a wide range of data and can generalize effectively to new, unseen data. Extensive experiments are conducted to evaluate the model’s performance under different IoU thresholds and confidence levels. Findings demonstrate that at lower IoU thresholds, the model maintains high detection performance, indicating its robustness in practical applications where precise localization may be challenging. Furthermore, the superior performance of the proposed model compared to MIGFA indicates its potential for widespread adoption by meteorological agencies for better monitoring and forecasting. -
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