Abstract:To address the issue of high-concealed abnormal wind directions in automatic weather station (AWS) data, this study establishes an abnormal wind direction identification method based on the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Historical wind direction data from 16 weather events affecting Guangzhou between 2016 and 2022, including cold waves, cold air masses, and typhoons, as well as observed wind direction data from AWSs during the impact of Typhoon Saola (No.2309), are used to detect abnormal wind directions. The analysis results reveal that the proportion of AWSs with suspicious wind directions in historical cases ranges from 0.46% to 5.56%, while the proportion of AWSs with erroneous wind directions varies from 0.25% to 2.05%. During the case of Typhoon Saola, the method identifies 13 AWSs with significantly deviating wind directions from the dominant surface wind direction, which is primarily due to wind direction sensor malfunctions and environmental impacts on AWS observations. Compared to that by the traditional method, the accuracy of wind direction error identification has improved by 20.32 percentage point. The new method provides a novel approach for the quality control of historical wind direction data from AWSs and offers an effective reference for the operational monitoring and on-site verification of AWS equipment.