Preprints
https://doi.org/10.5194/amt-2021-179
https://doi.org/10.5194/amt-2021-179

  09 Jul 2021

09 Jul 2021

Review status: this preprint is currently under review for the journal AMT.

24-hour cloud cover calculation using ground-based imager with machine learning

Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang Bu-Yo Kim et al.
  • Convergence Meteorological Research Department, National Institute of Meteorological Sciences, Seogwipo, Jeju 63569, Republic of Korea

Abstract. 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 tenth, 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 agreement 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.

Bu-Yo Kim et al.

Status: open (until 31 Aug 2021)

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Bu-Yo Kim et al.

Bu-Yo Kim et al.

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Short summary
This study investigates a method for 24-h cloud cover calculation using a camera-based imager and supervised machine learning methods. The cloud cover is calculated by learning the statistical characteristics of the ratio, difference, and luminance using RGB channel of the image with a machine learning model. The proposed approach is suitable for nowcasting because it has higher learning and prediction speed than the method in which the many pixels of a 2D image are learned.