Articles | Volume 16, issue 21
https://doi.org/10.5194/amt-16-5403-2023
https://doi.org/10.5194/amt-16-5403-2023
Research article
 | 
13 Nov 2023
Research article |  | 13 Nov 2023

Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network

Bu-Yo Kim, Joo Wan Cha, and Yong Hee Lee

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Cited articles

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Geng, Y. A., Li, Q., Lin, T., Yao, W., Xu, L., Zheng, D., Zhou, X., Zheng, L., Lyu, W., and Zhang, Y.: A deep learning framework for lightning forecasting with multi-source spatiotemporal data, Q. J. Roy. Meteor. Soc., 147, 4048–4062, https://doi.org/10.1002/qj.4167, 2021. 
Geng, Z., Zhang, Y., Li, C., Han, Y., Cui, Y., and Yu, B.: Energy optimization and prediction modeling of petrochemical industries: An improved convolutional neural network based on cross-feature, Energy, 194, 116851, https://doi.org/10.1016/j.energy.2019.116851, 2020. 
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Short summary
A camera-based imager and convolutional neural network (CNN) were used to estimate ground cloud cover. Image data from 2019 were used for training and validation, and those from 2020 were used for testing. The CNN model exhibited high performance, with an accuracy of 0.92, RMSE of 1.40 tenths, and 93% agreement with observed cloud cover within ±2 tenths' difference. It also outperformed satellites and ceilometers and proved to be the most suitable for ground-based cloud cover estimation.
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