It is not denied that real-time monitoring of radar products is an important part in actual meteorological operations. But the weather radar often brings out abnormal radar echoes due to various factors, such as climate and hardware failure. So it is of great practical significance and research value to realize automatic identification of radar anomaly products. However, the traditional algorithms to identify anomalies of weather radar echo images are not the most accurate and efficient. In order to improve the efficiency of the anomaly identification, a novel method combining the theory of classical image processing and deep learning was proposed. The proposed method mainly includes three parts: coordinate transformation, integral projection, and classification using deep learning. Furthermore, extensive experiments have been done to validate the performance of the new algorithm. The results show that the recognition rate of the proposed method can reach up to more than 95%, which can successfully achieve the goal of screening abnormal radar echo images; also, the computation speed of it is fairly satisfactory.

不可否认，雷达产品的实时监控是实际气象业务中的重要组成部分。但是由于天气和硬件故障等多种因素，天气雷达经常会发出异常的雷达回波。因此，实现雷达异常产品的自动识别具有重要的现实意义和研究价值。但是，传统的识别天气雷达回波图像异常的算法并不是最准确，最有效的算法。为了提高异常识别的效率，提出了一种结合经典图像处理和深度学习理论的新方法。该方法主要包括三个部分：坐标变换，积分投影和使用深度学习进行分类。此外，已经进行了广泛的实验，以验证新算法的性能。结果表明，该方法的识别率可以达到95％以上，可以成功达到筛选异常雷达回波图像的目的。而且，它的计算速度还算令人满意。