Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6695-2021
https://doi.org/10.5194/amt-14-6695-2021
Research article
 | 
18 Oct 2021
Research article |  | 18 Oct 2021

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

Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang

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Al Banna, M. H., Taher, K. A., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S., and Cho, G. H.: Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges, IEEE Access., 8, 192880–192923, https://doi.org/10.1109/ACCESS.2020.3029859, 2020. 
Al-lahham, A., Theeb, O., Elalem, K., Alshawi, T., and Alshebeili, S.: Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning, Electronics, 9, 1700, https://doi.org/10.3390/electronics9101700, 2020. 
Alonso, J., Batlles, F. J., López, G., and Ternero, A.: Sky camera imagery processing based on a sky classification using radiometric data, Energy, 68, 599–608, https://doi.org/10.1016/j.energy.2014.02.035, 2014. 
Alonso-Montesinos, J.: Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera, Remote Sens., 12, 1382, https://doi.org/10.3390/rs12091382, 2020. 
Azhar, M. A. D. M., Hamid, N. S. A., Kamil, W. M. A. W. M., and Mohamad, N. S.: Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory, Universe, 7, 41, https://doi.org/10.3390/universe7020041, 2021. 
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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 channels 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.
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