Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning

• Published in: Atmospheric measurement techniques Vol. 14; no. 10; pp. 6695 - 6710
• Main Authors: Kim, Bu-Yo, Cha, Joo Wan, Chang, Ki-Ho
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 18.10.2021; Copernicus Publications
• Subjects: Accuracy; Agreements; Analysis; Artificial neural networks; Cloud cover; Clouds; Correlation coefficient; Correlation coefficients; Digital cameras; Learning algorithms; Machine learning; Meteorological satellites; Methods; Neural networks; Nowcasting; Observational learning; Regression models; Statistical analysis; Support vector machines; Test sets; Training
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-14-6695-2021

In this study, image data features and machine learning methods were used to calculate 24 h continuous cloud cover from image data obtained by a camera-based imager on the ground. The image data features were the time (Julian day and hour), solar zenith angle, and statistical characteristics of the red–blue ratio, blue–red difference, and luminance. These features were determined from the red, green, and blue brightness of images subjected to a pre-processing process involving masking removal and distortion correction. The collected image data were divided into training, validation, and test sets and were used to optimize and evaluate the accuracy of each machine learning method. The cloud cover calculated by each machine learning method was verified with human-eye observation data from a manned observatory. Supervised machine learning models suitable for nowcasting, namely, support vector regression, random forest, gradient boosting machine, k-nearest neighbor, artificial neural network, and multiple linear regression methods, were employed and their results were compared. The best learning results were obtained by the support vector regression model, which had an accuracy, recall, and precision of 0.94, 0.70, and 0.76, respectively. Further, bias, root mean square error, and correlation coefficient values of 0.04 tenths, 1.45 tenths, and 0.93, respectively, were obtained for the cloud cover calculated using the test set. When the difference between the calculated and observed cloud cover was allowed to range between 0, 1, and 2 tenths, high agreements of approximately 42 %, 79 %, and 91 %, respectively, were obtained. The proposed system involving a ground-based imager and machine learning methods is expected to be suitable for application as an automated system to replace human-eye observations.