Articles | Volume 24, issue 8
https://doi.org/10.5194/acp-24-5025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-5025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Apr 2024
Research article |
| 29 Apr 2024
| 29 Apr 2024
Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data
Xiaoli Wei
Shanghai Meteorological Service, Shanghai, 200030, China
Shanghai Qi Zhi Institute, Shanghai, 200232, China
Qian Cui
Wuhan Meteorological Bureau, Wuhan, 430000, China
Leiming Ma
Shanghai Meteorological Service, Shanghai, 200030, China
Feng Zhang
CORRESPONDING AUTHOR
Shanghai Qi Zhi Institute, Shanghai, 200232, China
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China
Wenwen Li
Shanghai Qi Zhi Institute, Shanghai, 200232, China
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China
Peng Liu
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Related authors
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Lingxiao Zhao, Feng Zhang, Jingwei Li, Feng Lu, and Zhijun Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-425, https://doi.org/10.5194/essd-2025-425, 2025
Preprint under review for ESSD
Short summary
Short summary
Clouds drive extreme weather and climate patterns, yet global observations remain fragmented with day-night inconsistencies. We solved this by creating the first high-resolution global cloud dataset covering 23 years (2000–2022). It delivers consistent day-night of cloud height, thickness, and composition worldwide. Validation confirms high accuracy. This breakthrough empowers researchers and to reliably analyze clouds' roles in climate change, weather patterns, and extreme events.
Cited articles
Bahadur, R., Praveen, P. S., Xu, Y., and Ramanathan, V.: Solar absorption by elemental and brown carbon determined from spectral observations, P. Natl. Acad. Sci. USA, 109, 17366–17371, https://doi.org/10.1073/pnas.1205910109, 2012.
Bian, Y., Zhao, C., Xu, W., Zhao, G., Tao, J., and Kuang, Y.: Development and validation of a CCD-laser aerosol detective system for measuring the ambient aerosol phase function, Atmos. Meas. Tech., 10, 2313–2322, https://doi.org/10.5194/amt-10-2313-2017, 2017.
Boselli, A., Caggiano, R., Cornacchia, C., Madonna, F., Mona, L., Macchiato, M., Pappalardo, G., and Trippetta, S.: Multi year sun-photometer measurements for aerosol characterization in a Central Mediterranean site, Atmos. Res., 104–105, 98–110, https://doi.org/10.1016/j.atmosres.2011.08.002, 2012.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Che, H., Bing, Q., Zhao, H., Xia, X., and Zhang, X.: Aerosol optical properties and direct radiative forcing based on measurements from the China Aerosol Remote Sensing Network (CARSNET) in eastern China, Atmos. Chem. Phys., 18, 405–425, https://doi.org/10.5194/acp-18-405-2018, 2018.
Choi, W., Lee, H., and Park, J.: A first approach to aerosol classification using space-borne measurement data: Machine learning-based algorithm and evaluation, Remote Sens., 13, 1–21, https://doi.org/10.3390/rs13040609, 2021a.
Choi, W., Lee, H., Kim, D., and Kim, S.: Improving spatial coverage of satellite aerosol classification using a random forest model, Remote Sens., 13, 1268, https://doi.org/10.3390/rs13071268, 2021b.
Dubovik, O. and King, M. D.: A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res.-Atmos., 105, 20673–20696, https://doi.org/10.1029/2000JD900282, 2000.
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, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002.
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill, N. T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols, J. Geophys. Res.-Atmos., 104, 31333–31349, https://doi.org/10.1029/1999JD900923, 1999.
Fernandez-Delgado, M., Cernadas, E., Barro, S., and Amorim, D.: Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?, J. Mach. Learn. Res., 15, 3133–3181, https://doi.org/10.5555/2627435.2697065, 2014.
Fu, Q., Thorsen, T. J., Su, J., Ge, J., and Huang, J.: Test of Mie-based single-scattering properties of non-spherical dust aerosols in radiative flux calculations, J. Quant. Spectrosc. Ra., 110, 1640–1653, https://doi.org/10.1016/j.jqsrt.2009.03.010, 2009.
Ghasemifar, E.: Climatology of aerosol types and their vertical distribution over Iran using CALIOP dataset during 2007–2021, Remote Sens. Appl., 32, 101053, https://doi.org/10.1016/j.rsase.2023.101053, 2023.
Giles, D. M., Holben, B. N., Eck, T. F., Sinyuk, A., Smirnov, A., Slutsker, I., Dickerson, R. R., Thompson, A. M., and Schafer, J. S.: An analysis of AERONET aerosol absorption properties and classifications representative of aerosol source regions, J. Geophys. Res.-Atmos., 117, 1–16, https://doi.org/10.1029/2012JD018127, 2012.
Hamill, P., Giordano, M., Ward, C., Giles, D., and Holben, B.: An AERONET-based aerosol classification using the Mahalanobis distance, Atmos. Environ., 140, 213–233, https://doi.org/10.1016/j.atmosenv.2016.06.002, 2016.
Kalapureddy, M. C. R., Kaskaoutis, D. G., Ernest Raj, P., Devara, P. C. S., Kambezidis, H. D., Kosmopoulos, P. G., and Nastos, P. T.: Identification of aerosol type over the Arabian Sea in the premonsoon season during the Integrated Campaign for Aerosols, Gases and Radiation Budget (ICARB), J. Geophys. Res.-Atmos., 114, 1–12, https://doi.org/10.1029/2009JD011826, 2009.
Kaskaoutis, D. G., Kharol, S. K., Sinha, P. R., Singh, R. P., Badarinath, K. V. S., Mehdi, W., and Sharma, M.: Contrasting aerosol trends over South Asia during the last decade based on MODIS observations, Atmos. Meas. Tech. Discuss., 4, 5275–5323, https://doi.org/10.5194/amtd-4-5275-2011, 2011.
Kiehl, J. T. and Briegleb, B. P.: The relative roles of sulfate aerosols and greenhouse gases in climate forcing, Science, 260, 311–314, https://doi.org/10.1126/science.260.5106.311, 1993.
Kumar, K. R., Kang, N., and Yin, Y.: Classification of key aerosol types and their frequency distributions based on satellite remote sensing data at an industrially polluted city in the Yangtze River Delta, China, Int. J. Climatol., 38, 320–336, https://doi.org/10.1002/joc.5178, 2018.
Lee, J., Kim, J., Song, C. H., Kim, S. B., Chun, Y., Sohn, B. J., and Holben, B. N.: Characteristics of aerosol types from AERONET sunphotometer measurements, Atmos. Environ., 44, 3110–3117, https://doi.org/10.1016/j.atmosenv.2010.05.035, 2010.
Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.: Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, J. Geophys. Res.-Atmos., 112, D13211, https://doi.org/10.1029/2006JD007811, 2007.
Li, K., Bai, K., Ma, M., Guo, J., Li, Z., Wang, G., and Chang, N.-B.: Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics, ISPRS J. Photogram. Remote Sens., 193, 45–59, https://doi.org/10.1016/j.isprsjprs.2022.09.001, 2022.
Lin, J., Zheng, Y., Shen, X., Xing, L., and Che, H.: Global aerosol classification based on aerosol robotic network (Aeronet) and satellite observation, Remote Sens., 13, 1–23, https://doi.org/10.3390/rs13061114, 2021.
Lin, M.: Measurement of aerosol size distribution function using Mie scattering – Mathematical considerations, J. Aerosol Sci., 38, 1150–1162, https://doi.org/10.1016/j.jaerosci.2007.08.003, 2007.
Lopez-Pineiro, A., Cabrera, D., Albarran, A., and Pefia, D.: Influence of two-phase olive mill waste application to soil on terbuthylazine behaviour and persistence under controlled and field conditions, J. Soils Sediments, 11, 771–782, https://doi.org/10.1007/s11368-011-0362-3, 2011.
Lu, F., Chen, S., Hu, Z., Han, Z., Alam, K., Luo, H., Bi, H., Chen, J., and Guo, X.: Sensitivity and uncertainties assessment in radiative forcing due to aerosol optical properties in diverse locations in China, Sci. Total Environ., 860, 160447, https://doi.org/10.1016/j.scitotenv.2022.160447, 2023.
Michael, I., Mishchenko, and, Larry, D., and Travis: Light scattering by polydisperse, rotationally symmetric nonspherical particles: Linear polarization, J. Quant. Spectrosc. Ra., 51, 759–778, https://doi.org/10.1016/0022-4073(94)90130-9, 1994.
Moraes, C. P. A., Fantinato, D. G., and Neves, A.: Epanechnikov kernel for PDF estimation applied to equalization and blind source separation, Signal Process., 189, 108251, https://doi.org/10.1016/j.sigpro.2021.108251, 2021.
Nandan, R., Ratnam, M. V., Kiran, V. R., Madhavan, B. L., and Naik, D. N.: Estimation of Aerosol Complex Refractive Index over a tropical atmosphere using a synergy of in-situ measurements, Atmos. Res., 257, 105625, https://doi.org/10.1016/J.ATMOSRES.2021.105625, 2021.
NASA: AERONET, NASA [data set], https://aeronet.gsfc.nasa.gov/ (last access: 1 November 2020), 2020.
Nicolae, D., Vasilescu, J., Talianu, C., Binietoglou, I., Nicolae, V., Andrei, S., and Antonescu, B.: A neural network aerosol-typing algorithm based on lidar data, Atmos. Chem. Phys., 18, 14511–14537, https://doi.org/10.5194/acp-18-14511-2018, 2018.
Omar, A. H., Won, J. G., Winker, D. M., Yoon, S. C., 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, 1–14, https://doi.org/10.1029/2004JD004874, 2005.
Papadakis, G. Z., Megaritis, A. G., and Pandis, S. N.: Effects of olive tree branches burning emissions on PM2.5 concentrations, Atmos. Environ., 112, 148–158, https://doi.org/10.1016/j.atmosenv.2015.04.014, 2015.
Pathak, B., Bhuyan, P. K., Gogoi, M., and Bhuyan, K.: Seasonal heterogeneity in aerosol types over Dibrugarh-North-Eastern India, Atmos. Environ., 47, 307–315, https://doi.org/10.1016/j.atmosenv.2011.10.061, 2012.
Pawar, G. V., Devara, P. C. S., and Aher, G. R.: Identification of aerosol types over an urban site based on air-mass trajectory classification, Atmos. Res., 164–165, 142–155, https://doi.org/10.1016/j.atmosres.2015.04.022, 2015.
Puxbaum, H., Caseiro, A., Sánchez-Ochoa, A., Kasper-Giebl, A., Claeys, M., Gelencsér, A., Legrand, M., Preunkert, S., and Pio, C.: Levoglucosan levels at background sites in Europe for assessing the impact of biomass combustion on the European aerosol background, J. Geophys. Res., 112, D23S05, https://doi.org/10.1029/2006JD008114, 2007.
Quirantes, A., Guerrero-Rascado, J. L., Pérez-Ramírez, D., Foyo-Moreno, I., Ortiz-Amezcua, P., Benavent-Oltra, J. A., Lyamani, H., Titos, G., Bravo-Aranda, J. A., Cazorla, A., Valenzuela, A., Casquero-Vera, J. A., Bedoya-Velásquez, A. E., Alados-Arboledas, L., and Olmo, F. J.: Extinction-related Angström exponent characterization of submicrometric volume fraction in atmospheric aerosol particles, Atmos. Res., 228, 270–280, https://doi.org/10.1016/j.atmosres.2019.06.009, 2019.
Ramanathan, V., Crutzen, P. J., Lelieveld, J., Mitra, A. P., Althausen, D., Anderson, J., Andreae, M. O., Cantrell, W., Cass, G. R., and Chung, C. E.: Indian Ocean Experiment: An integrated analysis of the climate forcing and effects of the great Indo-Asian haze, J. Geophys. Res.-Atmos., 106, 28371–28398, https://doi.org/10.1029/2001JD900133, 2001.
Raut, J. C. and Chazette, P.: Radiative budget in the presence of multi-layered aerosol structures in the framework of AMMA SOP-0, Atmos. Chem. Phys., 8, 6839–6864, https://doi.org/10.5194/acp-8-6839-2008, 2008.
Reddy, L. A., Glover, T. A., Dudek, C. M., Alperin, A., Wiggs, N. B., and Bronstein, B.: A randomized trial examining the effects of paraprofessional behavior support coaching for elementary students with disruptive behavior disorders: Paraprofessional and student outcomes, J. School Psychol., 92, 227–245, https://doi.org/10.1016/j.jsp.2022.04.002, 2022.
Redemann, J., Turco, R. P., Liou, K. N., Russell, P. B., Bergstrom, R. W., Schmid, B., Hobbs, P. V, Hartley, W. S., Ismail, S., and Ferrare, R. A.: Retrieving the vertical structure of the effective aerosol complex index of refraction from a combination of aerosol in situ and remote sensing measurements during TARFOX, J. Geophys. Res., 105, 9949– 9970, https://doi.org/10.1029/1999JD901044, 2000.
Remer, L. A., Tanré, D., and Kaufman, Y. J.: Algorithm for remote sensing of tropospheric aerosol from MODIS for Collection 005: Revision 2 Products: 04_L2, ATML2, 08_D3, 08_E3, 08_M3, https://modis.gsfc.nasa.gov/data/atbd/atbd_mod02.pdf (last access: 24 April 2024), 2009.
Rosenblatt, M.: Remarks on Some Nonparametric Estimates of a Density Function, Remarks on Some Nonparametric Estimates of a Density Function, in: Selected Works of Murray Rosenblatt. Selected Works in Probability and Statistics, edited by: Davis, R., Lii, K. S., and Politis, D., Springer, New York, NY, https://doi.org/10.1007/978-1-4419-8339-8_13, 2011.
SCATTERLIB: Absorption and Scattering of Light by Small Particles, University of California, San Diego, http://scatterlib.wikidot.com/codes (last access: 1 November 2020), 2020.
Sheridan, P. J., Delene, D. J., and Ogren, J. A.: Four Years of Continuous Surface Aerosol Measurements from the DOE/ARM Southern Great Plains CART Site, J. Geophys. Res.-Atmos., 106, 20735–20747, https://doi.org/10.1029/2001JD000785, 2001.
Shin, S. K., Tesche, M., Noh, Y., and Müller, D.: Aerosol-type classification based on AERONET version 3 inversion products, Atmos. Meas. Tech., 12, 3789–3803, https://doi.org/10.5194/amt-12-3789-2019, 2019.
Siomos, N., Fountoulakis, I., Natsis, A., Drosoglou, T., and Bais, A.: Automated aerosol classification from spectral UV measurements using machine learning clustering, Remote Sens., 12, 1–18, https://doi.org/10.3390/rs12060965, 2020.
Tong, H., Lakey, P. S. J., Arangio, A. M., Socorro, J., Kampf, C. J., Berkemeier, T., Brune, W. H., Pöschl, U., and Shiraiwa, M.: Reactive oxygen species formed in aqueous mixtures of secondary organic aerosols and mineral dust influencing cloud chemistry and public health in the Anthropocene, Faraday Discuss., 200, 251–270, https://doi.org/10.1039/c7fd00023e, 2017.
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen, T. T.: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010.
Wang, J., Liu, Y., Chen, L., Liu, Y., Mi, K., Gao, S., Mao, J., Zhang, H., Sun, Y., and Ma, Z.: Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China, Sci. Total Environ., 10, 166603, https://doi.org/10.1016/j.scitotenv.2023.166603, 2023.
Wu, Y., Li, J., Xia, Y., Deng, Z., Tao, J., Tian, P., Gao, Z., Xia, X., and Zhang, R.: Size-resolved refractive index of scattering aerosols in urban Beijing: A seasonal comparison, Aerosol Sci. Tech., 55, 1070–1083, https://doi.org/10.1080/02786826.2021.1924357, 2021.
Yang, M., Howell, S. G., Zhuang, J., and Huebert, B. J.: Attribution of aerosol light absorption to black carbon, brown carbon, and dust in China – Interpretations of atmospheric measurements during EAST-AIRE, Atmos. Chem. Phys., 9, 2035–2050, https://doi.org/10.5194/acp-9-2035-2009, 2009.
Yokelson, R. J., Urbanski, S. P., Atlas, E. L., Toohey, D. W., Alvarado, E. C., Crounse, J. D., Wennberg, P. O., Fisher, M. E., Wold, C. E., and Campos, T. L.: Emissions from forest fires near Mexico City, Atmos. Chem. Phys., 7, 5569–5584, https://doi.org/10.5194/acp-7-5569-2007, 2007.
Yousefi, R., Wang, F., Ge, Q., and Shaheen, A.: Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types, Sci. Total Environ., 722, 137906, https://doi.org/10.1016/j.scitotenv.2020.137906, 2020.
Zhang, F., Wei, X., and Cui, Q.: Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data [Data set], Zenodo [data set], https://doi.org/10.5281/zenodo.10973114, 2024a.
Zhang, F., Wei, X., and Cui, Q.: Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data, Zenodo [code], https://doi.org/10.5281/zenodo.10972939, 2024b.
Zhang, L. and Li, J.: Variability of major aerosol types in China classified using AERONET measurements, Remote Sens., 11, 2334, https://doi.org/10.3390/rs11202334, 2019.
Zhao, G., Li, F., and Zhao, C.: Determination of the refractive index of ambient aerosols, Atmos. Environ., 240, 117800, https://doi.org/10.1016/j.atmosenv.2020.117800, 2020.
Short summary
A new aerosol-type classification algorithm has been proposed. It includes an optical database built by Mie scattering and a complex refractive index working as a baseline to identify different aerosol types. The new algorithm shows high accuracy and efficiency. Hence, a global map of aerosol types was generated to characterize aerosol types across the five continents. It will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution.
A new aerosol-type classification algorithm has been proposed. It includes an optical database...
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