Articles | Volume 18, issue 20
https://doi.org/10.5194/amt-18-5637-2025
© Author(s) 2025. 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-18-5637-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cloud fraction estimation using random forest classifier on sky images
Sougat Kumar Sarangi
Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
Chandan Sarangi
CORRESPONDING AUTHOR
Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India
School of Sustainability, Indian Institute of Technology Madras, Chennai, India
Niravkumar Patel
Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
Bomidi Lakshmi Madhavan
Aerosols, Radiation and Trace Gases Group, National Atmospheric Research Laboratory, Gadanki, India
Shantikumar Singh Ningombam
Sun and Solar System Group, Indian Institute of Astrophysics, Bangalore, India
Belur Ravindra
Sun and Solar System Group, Indian Institute of Astrophysics, Bangalore, India
Madineni Venkat Ratnam
Aerosols, Radiation and Trace Gases Group, National Atmospheric Research Laboratory, Gadanki, India
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This preprint is open for discussion and under review for The Cryosphere (TC).
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Atmos. Chem. Phys., 25, 8255–8270, https://doi.org/10.5194/acp-25-8255-2025, https://doi.org/10.5194/acp-25-8255-2025, 2025
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Amit Kumar Pandit, Jean-Paul Vernier, Thomas Duncan Fairlie, Kristopher M. Bedka, Melody A. Avery, Harish Gadhavi, Madineni Venkat Ratnam, Sanjeev Dwivedi, Kasimahanthi Amar Jyothi, Frank G. Wienhold, Holger Vömel, Hongyu Liu, Bo Zhang, Buduru Suneel Kumar, Tra Dinh, and Achuthan Jayaraman
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We show that for air quality, the densely populated eastern US may see even larger impacts of wildfires due to long-distance smoke transport and associated positive climatic impacts, partially compensating the improvements from regulations on anthropogenic emissions. This study highlights the tension between natural and anthropogenic contributions and the non-local nature of air pollution that complicate regulatory strategies for improving future regional air quality for human health.
Hazel Vernier, Neeraj Rastogi, Hongyu Liu, Amit Kumar Pandit, Kris Bedka, Anil Patel, Madineni Venkat Ratnam, Buduru Suneel Kumar, Bo Zhang, Harish Gadhavi, Frank Wienhold, Gwenael Berthet, and Jean-Paul Vernier
Atmos. Chem. Phys., 22, 12675–12694, https://doi.org/10.5194/acp-22-12675-2022, https://doi.org/10.5194/acp-22-12675-2022, 2022
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Varaha Ravi Kiran, Madineni Venkat Ratnam, Masatomo Fujiwara, Herman Russchenberg, Frank G. Wienhold, Bomidi Lakshmi Madhavan, Mekalathur Roja Raman, Renju Nandan, Sivan Thankamani Akhil Raj, Alladi Hemanth Kumar, and Saginela Ravindra Babu
Atmos. Meas. Tech., 15, 4709–4734, https://doi.org/10.5194/amt-15-4709-2022, https://doi.org/10.5194/amt-15-4709-2022, 2022
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Chandan Sarangi, TC Chakraborty, Sachchidanand Tripathi, Mithun Krishnan, Ross Morrison, Jonathan Evans, and Lina M. Mercado
Atmos. Chem. Phys., 22, 3615–3629, https://doi.org/10.5194/acp-22-3615-2022, https://doi.org/10.5194/acp-22-3615-2022, 2022
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Rohit Chakraborty, Arindam Chakraborty, Ghouse Basha, and Madineni Venkat Ratnam
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Saginela Ravindra Babu, Madineni Venkat Ratnam, Ghouse Basha, Shantanu Kumar Pani, and Neng-Huei Lin
Atmos. Chem. Phys., 21, 5533–5547, https://doi.org/10.5194/acp-21-5533-2021, https://doi.org/10.5194/acp-21-5533-2021, 2021
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Short summary
This study introduces a new approach to measure cloud cover from image data taken by ground-based sky observations. Our method used diverse sky images taken from various locations across the globe to train our machine learning model. We achieved a very high accuracy in detecting cloud cover, even in polluted areas. Our model surpasses traditional methods by running efficiently with minimal computational needs.
This study introduces a new approach to measure cloud cover from image data taken by...