Version 1
: Received: 13 December 2023 / Approved: 14 December 2023 / Online: 14 December 2023 (05:47:29 CET)
Version 2
: Received: 7 March 2024 / Approved: 7 March 2024 / Online: 8 March 2024 (04:08:19 CET)
Version 3
: Received: 3 April 2024 / Approved: 3 April 2024 / Online: 3 April 2024 (08:30:57 CEST)
Version 4
: Received: 15 April 2024 / Approved: 15 April 2024 / Online: 16 April 2024 (02:39:14 CEST)
How to cite:
Luo, J.; Pan, Y.; Su, D.; Zhong, J.; Wu, L.; Zhao, W.; Hu, X.; Qi, Z.; Lu, D.; Wang, Y. Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging. Preprints2023, 2023121052. https://doi.org/10.20944/preprints202312.1052.v2
Luo, J.; Pan, Y.; Su, D.; Zhong, J.; Wu, L.; Zhao, W.; Hu, X.; Qi, Z.; Lu, D.; Wang, Y. Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging. Preprints 2023, 2023121052. https://doi.org/10.20944/preprints202312.1052.v2
Luo, J.; Pan, Y.; Su, D.; Zhong, J.; Wu, L.; Zhao, W.; Hu, X.; Qi, Z.; Lu, D.; Wang, Y. Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging. Preprints2023, 2023121052. https://doi.org/10.20944/preprints202312.1052.v2
APA Style
Luo, J., Pan, Y., Su, D., Zhong, J., Wu, L., Zhao, W., Hu, X., Qi, Z., Lu, D., & Wang, Y. (2024). Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging. Preprints. https://doi.org/10.20944/preprints202312.1052.v2
Chicago/Turabian Style
Luo, J., Daren Lu and Yinan Wang. 2024 "Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging" Preprints. https://doi.org/10.20944/preprints202312.1052.v2
Abstract
Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.
Keywords
cloud quantification; deep neural networks; adaptive segmentation; finite element clustering; whole sky image
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
