Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-3873-2025
© 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/essd-17-3873-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Aug 2025
Data description paper |
| 11 Aug 2025
| 11 Aug 2025
Aerosol single-scattering albedo derived by merging OMI/POLDER satellite products and AERONET ground observations
Yueming Dong
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, 210044, Nanjing, China
Zhenyu Zhang
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Chongzhao Zhang
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Qiurui Li
Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Related authors
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
Short summary
Short summary
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.
Qiurui Li, Zhongxia Sun, Meijing Liu, Huizheng Che, Yu Zheng, and Jing Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-4936, https://doi.org/10.5194/egusphere-2025-4936, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
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.
Chong Li, Oleg Dubovik, Jing Li, David Fuertes, Anton Lopatin, Pavel Litvinov, Tatsiana Lapyonok, Lukas Bindreiter, Christian Matar, Yiqi Chu, and Wangshu Tan
EGUsphere, https://doi.org/10.5194/egusphere-2025-2694, https://doi.org/10.5194/egusphere-2025-2694, 2025
Short summary
Short summary
Using observational data from Japan’s geostationary satellite – Himawari-8 , this study improved how we track air pollution (aerosols) across East Asia and the Western Pacific. By applying an advanced aerosol retrieval algorithm called GRASP, we were able to more accurately observe both atmospheric and ground conditions and their dynamics over time. The results closely matched ground-based measurements and showed potential for even better monitoring when combined with ground-based lidar data.
Guanyu Liu, Jing Li, Sheng Yue, Lulu Zhang, and Chongzhao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1871, https://doi.org/10.5194/egusphere-2025-1871, 2025
Short summary
Short summary
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 highlighting microwave radiometry's underutilized potential in atmospheric research.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Liangying Zeng, Yang Yang, Hailong Wang, Jing Wang, Jing Li, Lili Ren, Huimin Li, Yang Zhou, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 21, 10745–10761, https://doi.org/10.5194/acp-21-10745-2021, https://doi.org/10.5194/acp-21-10745-2021, 2021
Short summary
Short summary
Using an aerosol–climate model, the impacts of El Niño with different durations on aerosols in China are examined. The modulation on aerosol concentrations and haze days by short-duration El Niño events is 2–3 times more than that by long-duration El Niño events in China. The frequency of short-duration El Niño has been increasing significantly in recent decades, suggesting that El Niño events have exerted increasingly intense modulation on aerosol pollution in China over the past few decades.
Zhongjing Jiang, Jing Li, Xiao Lu, Cheng Gong, Lin Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 2601–2613, https://doi.org/10.5194/acp-21-2601-2021, https://doi.org/10.5194/acp-21-2601-2021, 2021
Short summary
Short summary
This study demonstrates that the intensity of the western Pacific subtropical high (WPSH), a major synoptic pattern in the northern Pacific during summer, can induce a dipole change in surface ozone pollution over eastern China. Ozone concentration increases in the north and decreases in the south during the strong WPSH phase, and vice versa. The change in chemical processes associated with the WPSH change plays a decisive role, whereas the natural emission of ozone precursors accounts for ~ 30 %.
Cited articles
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. a
Chatterjee, A., Michalak, A. M., Kahn, R. A., Paradise, S. R., Braverman, A. J., and Miller, C. E.: A geostatistical data fusion technique for merging remote sensing and ground based observations of aerosol optical thickness, J. Geophys. Res.-Atmos., 115, D20207, https://doi.org/10.1029/2009JD013765, 2010. a
Chen, C., Dubovik, O., Fuertes, D., Litvinov, P., Lapyonok, T., Lopatin, A., Ducos, F., Derimian, Y., Herman, M., Tanré, D., Remer, L. A., Lyapustin, A., Sayer, A. M., Levy, R. C., Hsu, N. C., Descloitres, J., Li, L., Torres, B., Karol, Y., Herrera, M., Herreras, M., Aspetsberger, M., Wanzenboeck, M., Bindreiter, L., Marth, D., Hangler, A., and Federspiel, C.: Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring, Earth Syst. Sci. Data, 12, 3573–3620, https://doi.org/10.5194/essd-12-3573-2020, 2020. a, b, c
Chen, C., Dubovik, O., Schuster, G. L., Chin, M., Henze, D. K., Lapyonok, T., Li, Z., Derimian, Y., and Zhang, Y.: Multi-angular polarimetric remote sensing to pinpoint global aerosol absorption and direct radiative forcing, Nat. Commun., 13, 7459, https://doi.org/10.1038/s41467-022-35147-y, 2022. a
Devi, A. and Satheesh, S. K.: Global maps of aerosol single scattering albedo using combined CERES-MODIS retrieval, Atmos. Chem. Phys., 22, 5365–5376, https://doi.org/10.5194/acp-22-5365-2022, 2022. a
Dong, Y.: Merged-OMI and Merged-POLDER aerosol single scattering albedo, Zenodo [code], https://doi.org/10.5281/zenodo.14294462, 2025. a, b
Dong, Y., Li, J., Yan, X., Li, C., Jiang, Z., Xiong, C., Chang, L., Zhang, L., Ying, T., and Zhang, Z.: Retrieval of aerosol single scattering albedo using joint satellite and surface visibility measurements, Remote Sens. Environ., 294, 113654, https://doi.org/10.1016/j.rse.2023.113654, 2023. a, b, c
Dong, Y., Li, J., Zhang, Z., Zheng, Y., Zhang, C., and Li, Z.: Machine learning based retrieval of aerosol and surface properties over land from the Gaofen-5 Directional Polarimetric Camera measurements, IEEE T. Geosci. Remote, 62, 4106315, https://doi.org/10.1109/TGRS.2024.3419169, 2024. a
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanré, D., and Slutsker, I.: Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590–608, 2002. a
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011. a, b, c
Engelstaedter, S., Tegen, I., and Washington, R.: North African dust emissions and transport, Earth-Sci. Rev., 79, 73–100, 2006. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, 1994. a
Fougnie, B.: Improvement of the PARASOL radiometric in-flight calibration based on synergy between various methods using natural targets, IEEE T. Geosci. Remote, 54, 2140–2152, https://doi.org/10.1109/TGRS.2015.2496322, 2016. a
Fu, G. and Hasekamp, O.: Retrieval of aerosol microphysical and optical properties over land using a multimode approach, Atmos. Meas. Tech., 11, 6627–6650, https://doi.org/10.5194/amt-11-6627-2018, 2018. a
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999. a
Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776–2790, 2001. a
Hansen, J., Sato, M., and Ruedy, R.: Radiative forcing and climate response, J. Geophys. Res.-Atmos., 102, 6831–6864, 1997. a
Hasekamp, O. P. and Landgraf, J.: Retrieval of aerosol properties over land surfaces: capabilities of multiple-viewing-angle intensity and polarization measurements, Appl. Optics, 46, 3332–3344, 2007. a
Hasekamp, O., Litvinov, P., Fu, G., Chen, C., and Dubovik, O.: Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols, Atmos. Meas. Tech., 17, 1497–1525, https://doi.org/10.5194/amt-17-1497-2024, 2024. a
Holben, B. N., Eck, T. F., Slutsker, I. a., Tanre, D., Buis, J., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y., and Nakajima, T.: AERONET – a federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16, 1998. a
Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123–137, 2001. a
Jinnagara Puttaswamy, S., Nguyen, H. M., Braverman, A., Hu, X., and Liu, Y.: Statistical data fusion of multi-sensor AOD over the continental United States, Geocarto Int., 29, 48–64, 2014. a
Kahn, R. A.: Reducing the uncertainties in direct aerosol radiative forcing, Surv. Geophys., 33, 701–721, 2012. a
Kahn, R. A., Andrews, E., Brock, C. A., Chin, M., Feingold, G., Gettelman, A., Levy, R. C., Murphy, D. M., Nenes, A., and Pierce, J. R.: Reducing aerosol forcing uncertainty by combining models with satellite and within the atmosphere observations: a three way street, Rev. Geophys., 61, e2022RG000796, https://doi.org/10.1029/2022RG000796, 2023. a
Lee, K. H., Li, Z., Wong, M. S., Xin, J., Wang, Y., Hao, W., and Zhao, F.: Aerosol single scattering albedo estimated across China from a combination of ground and satellite measurements, J. Geophys. Res.-Atmos., 112, D22S15, https://doi.org/10.1029/2007JD009077, 2007. a
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013. a
Li, J., Li, X., Carlson, B. E., Kahn, R. A., Lacis, A. A., Dubovik, O., and Nakajima, T.: Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations, J. Geophys. Res.-Atmos., 121, 13609–13627, 2016. a
Li, J., Kahn, R. A., Wei, J., Carlson, B. E., Lacis, A. A., Li, Z., Li, X., Dubovik, O., and Nakajima, T.: Synergy of satellite and ground based aerosol optical depth measurements using an ensemble Kalman filter approach, J. Geophys. Res.-Atmos., 125, e2019JD031884, https://doi.org/10.1029/2019JD031884, 2020. a, b, c, d, e, f, g, h
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., Dubovik, O., Ding, A., Lacis, A., Zhang, L., and Dong, Y.: Scattering and absorbing aerosols in the climate system, Nature Reviews Earth and Environment, 3, 363–379, 2022. a, b, c
Loeb, N. G. and Su, W.: Direct aerosol radiative forcing uncertainty based on a radiative perturbation analysis, J. Climate, 23, 5288–5293, 2010. a
Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP – a comparison with 4D Var, Q. J. Roy. Meteor. Soc., 129, 3183–3203, 2003. a
Mahowald, N. M. and Dufresne, J.: Sensitivity of TOMS aerosol index to boundary layer height: Implications for detection of mineral aerosol sources, Geophys. Res. Lett., 31, L03103, https://doi.org/10.1029/2003GL018865, 2004. a
Mielonen, T., Levy, R. C., Aaltonen, V., Komppula, M., de Leeuw, G., Huttunen, J., Lihavainen, H., Kolmonen, P., Lehtinen, K. E. J., and Arola, A.: Evaluating the assumptions of surface reflectance and aerosol type selection within the MODIS aerosol retrieval over land: the problem of dust type selection, Atmos. Meas. Tech., 4, 201–214, https://doi.org/10.5194/amt-4-201-2011, 2011. a
Mishchenko, M. I., Cairns, B., Kopp, G., Schueler, C. F., Fafaul, B. A., Hansen, J. E., Hooker, R. J., Itchkawich, T., Maring, H. B., and Travis, L. D.: Accurate monitoring of terrestrial aerosols and total solar irradiance: introducing the Glory mission, B. Am. Meteorol. Soc., 88, 677–692, 2007. a
Nguyen, H., Cressie, N., and Braverman, A.: Spatial statistical data fusion for remote sensing applications, J. Am. Stat. Assoc., 107, 1004–1018, 2012. a
Omar, A. H., Won, J., Winker, D. M., Yoon, S., Dubovik, O., and McCormick, M. P.: Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements, J. Geophys. Res.-Atmos., 110, D10S14, https://doi.org/10.1029/2004JD004874, 2005. a
Proestakis, E., Amiridis, V., Marinou, E., Georgoulias, A. K., Solomos, S., Kazadzis, S., Chimot, J., Che, H., Alexandri, G., Binietoglou, I., Daskalopoulou, V., Kourtidis, K. A., de Leeuw, G., and van der A, R. J.: Nine-year spatial and temporal evolution of desert dust aerosols over South and East Asia as revealed by CALIOP, Atmos. Chem. Phys., 18, 1337–1362, https://doi.org/10.5194/acp-18-1337-2018, 2018. a
Ramanathan, V., Crutzen, P., Kiehl, J., and Rosenfeld, D.: Aerosols, climate, and the hydrological cycle, Science, 294, 2119–2124, 2001. a
Schutgens, N., Dubovik, O., Hasekamp, O., Torres, O., Jethva, H., Leonard, P. J. T., Litvinov, P., Redemann, J., Shinozuka, Y., de Leeuw, G., Kinne, S., Popp, T., Schulz, M., and Stier, P.: AEROCOM and AEROSAT AAOD and SSA study – Part 1: Evaluation and intercomparison of satellite measurements, Atmos. Chem. Phys., 21, 6895–6917, https://doi.org/10.5194/acp-21-6895-2021, 2021. a
Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., Schafer, J. S., Smirnov, A., Sorokin, M., and Lyapustin, A.: The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2, Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, 2020. a, b, c
Tang, Q., Bo, Y., and Zhu, Y.: Spatiotemporal fusion of multiple satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method, J. Geophys. Res.-Atmos., 121, 4034–4048, 2016. a
Tanré, D., Bréon, F. M., Deuzé, J. L., Dubovik, O., Ducos, F., François, P., Goloub, P., Herman, M., Lifermann, A., and Waquet, F.: Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: the PARASOL mission, Atmos. Meas. Tech., 4, 1383–1395, https://doi.org/10.5194/amt-4-1383-2011, 2011. a
Thorsen, T. J., Winker, D. M., and Ferrare, R. A.: Uncertainty in observational estimates of the aerosol direct radiative effect and forcing, J. Climate, 34, 195–214, 2021. a
Torres, O.: OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo Daily L2 Global Gridded 0.25 degree × 0.25 degree V3, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/Aura/OMI/DATA2005, 2015. a
Torres, O., Bhartia, P., Herman, J., Ahmad, Z., and Gleason, J.: Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: theoretical basis, J. Geophys. Res.-Atmos., 103, 17099–17110, 1998. a
Torres, O., Tanskanen, A., Veihelmann, B., Ahn, C., Braak, R., Bhartia, P. K., Veefkind, P., and Levelt, P.: Aerosols and surface UV products from ozone monitoring instrument observations: an overview, J. Geophys. Res.-Atmos., 112, D24S47, https://doi.org/10.1029/2007JD008809, 2007. a, b
Torres, O., Ahn, C., and Chen, Z.: Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations, Atmos. Meas. Tech., 6, 3257–3270, https://doi.org/10.5194/amt-6-3257-2013, 2013. a
Torres, O., Jethva, H., Ahn, C., Jaross, G., and Loyola, D. G.: TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020, Atmos. Meas. Tech., 13, 6789–6806, https://doi.org/10.5194/amt-13-6789-2020, 2020. a
Wu, L., Hasekamp, O., van Diedenhoven, B., Cairns, B., Yorks, J. E., and Chowdhary, J.: Passive remote sensing of aerosol layer height using near UV multiangle polarization measurements, Geophys. Res. Lett., 43, 8783–8790, 2016. a
Xu, H., Guang, J., Xue, Y., De Leeuw, G., Che, Y., Guo, J., He, X., and Wang, T.: A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products, Atmos. Environ., 114, 48–56, 2015. a
Zhang, L. and Li, J.: Variability of major aerosol types in China classified using AERONET measurements, Remote Sens.-Basel, 11, 2334, https://doi.org/10.3390/rs11202334, 2019. a
Zhang, Z., Li, J., Dong, Y., Zhang, C., Ying, T., and Li, Q.: Long-term trends in aerosol single scattering albedo cause bias in MODIS aerosol optical depth trends, IEEE T. Geosci. Remote, 62, 4109209, https://doi.org/10.1109/TGRS.2024.3424981, 2024. a
Zhao, A., Li, Z., Zhang, Y., Zhang, Y., and Li, D.: Merging modis and ground-based fine mode fraction of aerosols based on the geostatistical data fusion method, Atmosphere, 8, 117, https://doi.org/10.3390/atmos8070117, 2017. a
Zhu, A., Ramanathan, V., Li, F., and Kim, D.: Dust plumes over the Pacific, Indian, and Atlantic oceans: climatology and radiative impact, J. Geophys. Res.-Atmos., 112, D16208, https://doi.org/10.1029/2007JD008427, 2007. a
Zhu, H., Cheng, T., Li, X., Ye, X., Fan, D., Tang, T., and Zhang, L.: Fusion of multisource satellite AOD products via Bayesian maximum entropy with explicit a priori knowledge, IEEE T. Geosci. Remote, 61, 1–12, 2023. a
Short summary
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.
This study develops two merged global land aerosol single-scattering albedo (SSA) datasets by...
Altmetrics
Final-revised paper
Preprint