Abstract
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.
Highlights
Doppler weather radar is a kind of monitoring tool for small and medium catastrophic weather, so its measurement accuracy is very important for weather forecast
We propose a new method combining the theory of classical image processing and deep learning to realize automatic identification of radar anomaly products, and this method is suitable for all weather radars
Afterwards, the pictures in log-polar coordinates will be conducted integration projection, the results of which will be as the inputs of stacked auto-encoder (SAE), a deep learning model
Summary
Doppler weather radar is a kind of monitoring tool for small and medium catastrophic weather, so its measurement accuracy is very important for weather forecast. Because of the external electromagnetic interference and the failure of transmitterreceiver system, the weather radar usually outputs erroneous data and abnormal echo images. 143 operational new generation weather radars are running in China, so it will be a heavy workload to recognize anomalies from the huge amounts of radar data artificially. For these reasons, it is of great significance to achieve the automatic identification of abnormal echo images from the radar data. Chen et al [1] put forward a set of method to deal with the abnormal radar echo through extracting feature of texture. Nan and Chong [4] accomplished the automatic recognition of radar echo by means of traditional machine learning, but its recognition efficiency is not very high
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