HINet： 一种面向冰雹识别的多源数据融合网络

冰雹天气具有突发性和局地性强，以及破坏力大的特点。尽管地面自动站、雷达和卫星等获取的观测资料在冰雹识别中发挥了一定的作用，但单一观测资料的局限性导致冰雹识别虚警率较高和准确率较低。因此，亟需构建基于多源高分辨率观测的冰雹识别技术。本文提出了一种面向冰雹识别的多源数据融合网络，该深度学习方法利用时空特征提取模块、多源数据特征融合模块和UCUNet（U Connection Unet，U形连接卷积神经网络）识别模块，充分挖掘冰雹发生时FY4B（风云四号B星）、天气雷达和数值模式等多源数据的时空特征，并创新地加入地形高度、坡度、坡向等作为冰雹识别因子。为评估所提网络方法的性能，本文进行了系列实验，并将实验结果与真实标签数据进行对比。结果显示，HINet（Hail Identification Net，冰雹识别网络）能够充分利用多源数据，在复杂地形条件下有效改善冰雹识别结果，在冰雹研究和识别中具有较高的准确性和实用性。

HINet： A Multi-source Data Fusion Network for Hail Identification

Hailstorms are characterized by their suddenness, localized nature and high destructive power. Although observations acquired by ground-based automatic stations, radars and satellites play a certain role in hail identification, the limitation of single observation data leads to a high false alarm rate and low accuracy rate in hail identification. Therefore, there is an urgent need to construct a hail identification technology based on multi-source high-resolution observation. In this paper, a multi-source data fusion network for hail recognition is proposed. The deep learning method utilizes the spatio-temporal feature extraction module, the multi-source data feature fusion module, and the UCUNet (U Connection Unet) recognition module to fully exploit the spatio-temporal features of the multi-source data such as FY4B (FengYun-4B star) satellites, weather radar, and numerical models when hail occurs, and innovatively adds the topographic height, slope, and slope direction as hail recognition factors. In order to evaluate the performance of the proposed network method, this paper conducts a series of experiments and compares the experimental results with real labeled data. The results show that HINet (Hail Identification Net) can make full use of multi-source data and effectively improve the hail identification results under complex terrain conditions. The network model proposed in this paper has high accuracy and practicality in hail research and identification.