Articles | Volume 16, issue 21
https://doi.org/10.5194/amt-16-5403-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-5403-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network
Research Applications Department, National Institute of Meteorological Sciences, Seogwipo, Jeju, 63568, South Korea
Joo Wan Cha
Research Applications Department, National Institute of Meteorological Sciences, Seogwipo, Jeju, 63568, South Korea
Yong Hee Lee
Research Applications Department, National Institute of Meteorological Sciences, Seogwipo, Jeju, 63568, South Korea
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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.
A camera-based imager and convolutional neural network (CNN) were used to estimate ground cloud...