Articles | Volume 19, issue 18
https://doi.org/10.5194/amt-19-5871-2026
© Author(s) 2026. 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-19-5871-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An AI-based algorithm for retrieving aerosol optical depth and single scattering albedo using All-Sky Imager observations
Heyang Ni
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Institute of Carbon Neutrality, Peking University, Beijing, China
Center for Environment and Health, Peking University, Beijing, China
Liang Chang
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Yueming Dong
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Guanghao Du
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Muqian Li
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Qiurui Li
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Guanyu Liu
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Yuebo Sun
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Angnuo Tian
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Sheng Yue
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Chongzhao Zhang
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Zhenyu Zhang
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Atmos. Chem. Phys., 26, 11491–11523, https://doi.org/10.5194/acp-26-11491-2026, https://doi.org/10.5194/acp-26-11491-2026, 2026
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We present a fast, interpretable machine learning method to retrieve key aerosol parameters from ground-based Sun-sky photometer measurements. Trained on simulated data covering diverse aerosol and atmospheric conditions, ensuring robustness and physical consistency. Applied to real observations, it agrees well with AERONET products and reduces computation time by orders of magnitude, offering a practical tool for monitoring aerosols and their effects on air quality and climate.
Zhenyu Zhang, Jing Li, Yueming Dong, Chongzhao Zhang, Qiurui Li, and Ling Gao
Atmos. Meas. Tech., 19, 2555–2574, https://doi.org/10.5194/amt-19-2555-2026, https://doi.org/10.5194/amt-19-2555-2026, 2026
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This study developed a numerical algorithm to simultaneously retrieve aerosol and surface parameters from multi-angle polarimetric observations from space. Our research shows that accurate single scattering albedo retrieval requires degree of linear polarization observation uncertainties below 0.01. The retrieval results agrees with ground-based measurements well, and successfully characterize regional pollution events. We also generate global maps of retrieved aerosol and surface parameters.
Guanyu Liu, Jing Li, Sheng Yue, Lulu Zhang, and Chongzhao Zhang
Atmos. Meas. Tech., 18, 6705–6725, https://doi.org/10.5194/amt-18-6705-2025, https://doi.org/10.5194/amt-18-6705-2025, 2025
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This study introduces a novel method to retrieve aerosol optical depth (AOD) at night using ground-based microwave radiometers, overcoming the limitation of traditional shortwave-based techniques that cannot operate in darkness. This result enables continuous aerosol monitoring and highlights microwave radiometry's under-utilized potential in atmospheric research.
Chong Li, Oleg Dubovik, Jing Li, David Fuertes, Anton Lopatin, Pavel Litvinov, Tatsiana Lapyonok, Lukas Bindreiter, Christian Matar, Yiqi Chu, and Wangshu Tan
Atmos. Meas. Tech., 18, 6609–6643, https://doi.org/10.5194/amt-18-6609-2025, https://doi.org/10.5194/amt-18-6609-2025, 2025
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Yueming Dong, Jing Li, Zhenyu Zhang, Chongzhao Zhang, and Qiurui Li
Earth Syst. Sci. Data, 17, 3873–3892, https://doi.org/10.5194/essd-17-3873-2025, https://doi.org/10.5194/essd-17-3873-2025, 2025
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This study develops two merged global land aerosol single-scattering albedo (SSA) datasets by combining AERONET ground observations and two satellite datasets using an ensemble Kalman filter data synergy method. The merged datasets exhibit significantly improved accuracy compared to the original satellite data. These results can provide more reliable estimates of aerosol scattering and absorption properties, essential for improving climate modeling and assessing aerosol climate effects.
Zhenyu Zhang, Jing Li, Huizheng Che, Yueming Dong, Oleg Dubovik, Thomas Eck, Pawan Gupta, Brent Holben, Jhoon Kim, Elena Lind, Trailokya Saud, Sachchida Nand Tripathi, and Tong Ying
Atmos. Chem. Phys., 25, 4617–4637, https://doi.org/10.5194/acp-25-4617-2025, https://doi.org/10.5194/acp-25-4617-2025, 2025
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We used ground-based remote sensing data from the Aerosol Robotic Network to examine long-term trends in aerosol characteristics. We found aerosol loadings generally decreased globally, and aerosols became more scattering. These changes are closely related to variations in aerosol compositions, such as decreased anthropogenic emissions over East Asia, Europe, and North America; increased anthropogenic sources over northern India; and increased dust activity over the Arabian Peninsula.
Hongfei Hao, Kaicun Wang, Guocan Wu, Jianbao Liu, and Jing Li
Earth Syst. Sci. Data, 16, 4051–4076, https://doi.org/10.5194/essd-16-4051-2024, https://doi.org/10.5194/essd-16-4051-2024, 2024
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In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine learning method at more than 5000 terrestrial sites in the Northern Hemisphere based on hourly atmospheric visibility data, which are extracted from the Meteorological Terminal Aviation Routine Weather Report (METAR).
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li
Earth Syst. Sci. Data, 16, 3233–3260, https://doi.org/10.5194/essd-16-3233-2024, https://doi.org/10.5194/essd-16-3233-2024, 2024
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In this study, we employed a machine learning technique to derive daily aerosol optical depth from hourly visibility observations collected at more than 5000 airports worldwide from 1959 to 2021 combined with reanalysis meteorological parameters.
Liang Chang, Jing Li, Jingjing Ren, Changrui Xiong, and Lu Zhang
Atmos. Meas. Tech., 17, 2637–2648, https://doi.org/10.5194/amt-17-2637-2024, https://doi.org/10.5194/amt-17-2637-2024, 2024
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We described a modified lidar inversion algorithm to retrieve aerosol extinction and size distribution simultaneously from two-wavelength elastic lidar measurements. Its major advantage is that the lidar ratio of each layer is determined iteratively by a lidar ratio–Ångström exponent lookup table. The algorithm was applied to the Raman lidar and CALIOP measurements. The retrieved results by our method are in good agreement with those achieved by Raman method.
Guanyu Liu, Jing Li, and Tong Ying
Atmos. Chem. Phys., 23, 9217–9228, https://doi.org/10.5194/acp-23-9217-2023, https://doi.org/10.5194/acp-23-9217-2023, 2023
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Fires in Australia are positively correlated with the El Niño–Southern Oscillation (ENSO). However, the correlation between ENSO and the Australian Fire Weather Index (FWI) increases from 0.17 to 0.70 when the Atlantic Multidecadal Oscillation (AMO) shifts from a negative to positive phase. This is explained by the teleconnection effect through which the warmer AMO generates Rossby wave trains and results in high pressures and a weather condition conducive to wildfires.
Zhongjing Jiang and Jing Li
Atmos. Chem. Phys., 22, 7273–7285, https://doi.org/10.5194/acp-22-7273-2022, https://doi.org/10.5194/acp-22-7273-2022, 2022
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This study investigates the changes of tropospheric ozone in China associated with EP and CP El Niño, using satellite observations and the GEOS-Chem model. We found that El Niño generally leads to lower tropospheric ozone (LTO) decrease over most parts of China; La Niña acts the opposite. The difference between LTO changes during EP and CP El Niño primarily lies in southern China. Regional transport and chemical processes play the leading and secondary roles in driving the LTO changes.
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Short summary
This study develops a new artificial intelligence method to retrieve daytime aerosol optical parameters using low-cost All-Sky Imagers. By training a model with high-precision data, we successfully estimated the aerosol optical depth and single scattering albedo simultaneously. Our results show high accuracy across different global regions. This approach is much faster than traditional complex calculations, making it easier to deploy dense observation networks to improve climate research.
This study develops a new artificial intelligence method to retrieve daytime aerosol optical...