Deep Learning-Based All-Sky Cloud Image Recognition

Accurate cloud identification is crucial for understanding the rapid evolution of weather systems, improving the accuracy of short-term forecasts, and ensuring aviation safety. Compared with traditional cloud image recognition methods, deep learning technology has advantages such as automatic learning of complex features, high-precision recognition, and strong robustness in changing environments, providing more reliable and detailed cloud information. This study utilized 256 cloud image observation data points collected by an all-sky imager from 3 to 30 November 2023, at the Tunchang County Meteorological Bureau in Hainan Province (19°21′N, 110°06′ E). A Convolutional Neural Network (CNN) model was employed for cloud image recognition. The results show that in terms of cloud recognition, the constructed CNN model achieved an accuracy rate, recall rate, and F1 score of 100%, 91%, and 95%, respectively, for clear skies and stratus clouds, cumulus clouds, and cirrus clouds, with an average recognition accuracy rate of 95%. In terms of cloud cover detection, when comparing the Normalized Red Blue Ratio (NRBR) and K-Means clustering algorithm with the system’s built-in monitoring results, the NRBR method performed optimally in cloud region segmentation, with cloud cover estimates closer to the actual distribution. In summary, deep learning technology demonstrates higher accuracy and strong robustness in all-sky cloud image recognition.