融合注意力机制和上下文信息的阵风锋检测与识别

Gust Front Detection and Recognition Integrating Attention Mechanism and Context Information

摘要: 阵风锋是一种常见的中小尺度天气现象，其经过常伴有灾害性天气，对生命财产安全构成严重威胁，因此，精确检测与识别阵风锋对于防灾减灾具有重要的现实意义。然而在实际应用中，阵风锋的检测面临着数据稀缺、区域特异性强、传统方法检测效果不佳、形态与位置难以精确识别、以及泛化能力不足等挑战。此外阵风锋的特征不明显，易与其他大气现象混淆，导致高误判率的发生。针对这些问题，本文提出了一种基于深度学习的创新方法。首先，针对传统检测方法的局限性，本文改进了Mask R-CNN模型，并引入了注意力机制和特征融合模块，显著提升了阵风锋的检测精度。其次，本文通过引入径向速度频道信息并设计上下文分支，有效增强了模型对干扰要素的辨别能力，从而降低了误判率。此外，本文构建了基于新一代多普勒天气雷达数据的三维阵风锋数据集，并通过数据增强技术扩充样本量，为阵风锋的深入研究提供了充足的资源。实验结果表明，所提出的方法在提升阵风锋检测与识别准确性方面具有显著优势，为阵风锋的研究及其业务化应用提供了新的理论基础和技术支持。

Abstract: Gust fronts are common mesoscale meteorological phenomena that are frequently accompanied by severe weather events, posing significant threats to life and property. Accurate detection and identification of gust fronts are therefore of critical importance for disaster prevention and mitigation. However, several challenges persist in the detection of gust fronts, including data scarcity, strong regional specificity, suboptimal performance of traditional mathematical methods, difficulties in accurately identifying their shape and location, and limited generalization capability. Moreover, the subtle characteristics of gust fronts often lead to confusion with other atmospheric phenomena, resulting in high false positive rates. To address these challenges, this paper proposes an innovative approach based on deep learning techniques. First, to overcome the limitations of traditional detection methods, we improve the Mask R-CNN model by incorporating an attention mechanism and a feature fusion module, significantly enhancing the detection accuracy of gust fronts. Second, to reduce false positive rates, we introduce radial velocity channel information and design a context branch to strengthen the model’s ability to distinguish between gust fronts and interfering elements. Additionally, we develop a three-dimensional gust front dataset using next-generation Doppler weather radar data and expand the dataset through data augmentation techniques, thereby providing a robust resource for gust front research. Experimental results validate the effectiveness of the proposed method in enhancing the accuracy of gust front detection and identification, offering new perspectives and tools for both research and operational applications in this domain.