A Feasible Method for Categorizing Weather Patterns Using K-Means Clustering Based on Coherent Doppler Wind Lidar - Chen, Yehui; Jin, Xiaomei; Liu, Ying; Weng, Ningquan; Zhu, Wenyue; Liu, Qing (International Conference on Systems and Informatics, 2024)
This study presents a method for categorizing weather patterns using K-Means Clustering, utilizing signal-to-noise ratio (SNR) data obtained from Coherent Doppler Wind Lidar. By integrating atmospheric parameters from meteorological stations and wind radar, the weather patterns are classified into five categories: clear, rainy, few clouds, moderate clouds, and cloudy. The K-Means Clustering analysis showed a 93% consistency with meteorological data from the China Meteorological Information Center by using hourly SNR data from December 2019 to April 2020. This approach offers a simple, effective tool for weather pattern recognition, essential for climate change studies, weather forecasting, and air pollution assessment.