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Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data

Received: 4 October 2020     Accepted: 19 October 2020     Published: 26 October 2020
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Abstract

This work aimed at detection of aerosols above clouds (AAC). It has been known that AAC has significant potential to change the global radiation budget, namely plays an important role in elucidating climate change. First we examined the advantages of multichannel data from near-UV to thermal infrared (IR) including polarization channels at red and near-IR collected using the GCOM-C/SGLI. The near-UV data at 0.38μm and 0.41μm not only detected absorbing aerosols such as biomass burning aerosols (BBA) or mineral dust (DUST), but were also used to distinguish between BBA and DUST with short wavelength IR measurements at 1.63μm. Because understanding aerosol types facilitates subsequent aerosol characterization, classification algorithms for aerosol types have been dealt with since the previous work. Discriminant verification was performed using ground measurements from NASA/AERONET and practically examined in a case of large forest fire. Then the detection of optically thick clouds was challenged in a similar way to aerosol classification in order to lead such a final goal of this work as detection of aerosols above clouds. Subsequently some scenes concerned with DUST type aerosols or BBA ones above water clouds were detected using GCOM-C/SGLI radiance or polarization measurements, respectively, and validated with Terra/MODIS products.

Published in International Journal of Environmental Monitoring and Analysis (Volume 8, Issue 5)
DOI 10.11648/j.ijema.20200805.16
Page(s) 170-180
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

BBA (Biomass Burning Aerosol), DUST, Multichannel Satellite Data, Near-UV, Color Ratio, Polarization

References
[1] Mukai, S., Sano, I. and Nakata, M. (2019). Algorithms for the classification and characterization of aerosols: Utility verification of near-UV satellite observations, J. Appl. Rem. Sen. 13 (1), 014527, doi: 10.1117/1.JARS.13.014527.
[2] Twomey, S. (1974). Pollution and planetary albedo, Atmos. Environ., 25, 2435-2442.
[3] Albrecht, B.(1989). Aerosols, cloud microphysics, and fractional cloudiness,” Science, 245, 1227-1230, doi: 10.1126/science.245.4923.1227.
[4] Takemura, T., Kaufman, Y. J., Remer, L. A., and Nakajima, T. (2007). Two competing pathway of aerosol effects on cloud and precipitation formation, Geophys. Res. Let., 34, L04802, doi: 10.1029/2006GL028349.
[5] Mukai, M., Nakajima, T. and Takemura, T. (2008). Anthropogenic impacts on the radiation budget and the cloud field in East Asia based on model simulations with GCM, J. Geophys. Res., 113, D12211, doi: 10.1029/2007JD009325.
[6] Takahashi, H. G., Watanabe, S., Nakata, M., and Takemura, T. (2018). Response of the atmospheric hydrological cycle over the tropical Asian monsoon regions to anthropogenic aerosols and its seasonality, Progress in Earth and Planetary Science, 5 (44).
[7] Sokolik, I. and Toon, O. (2018). Direct radiative forcing by anthropogenic airborne aerosols, Nature, 381, 681-683, doi: 10.1038/381681a0.
[8] Liao, H. and J. Seinfeld, J. (1998). Effect of clouds on direct aerosol radiative forcing of climate, J. Geophys. Res., 103, D103, 3781-3788, doi: 10.1029/97JD03455.
[9] Jacobson, M. (2001). Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols, Nature. 409, 696-697, doi: 10.10381/35055518.
[10] IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 1535 pp., ISBN 978-1-107-05799-1.
[11] Kirchstetter, T. and T. Novakov, T. (2004). Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon, J. Geophys. Res., 109, D21208, doi: 10.1029/2004JD004999.
[12] Kalashnikova, O., Garay, M., Bates, Chen, Y. and Bond, T. C. (2010). Light absorption by organic carbon from wood combustion, Atmos. Chem. Phys., 10, 1773–1787, doi: 10.5194/acp-10-1773-2010.
[13] Kalashnikova, O., Garay, M., Bates, K., Kenseth, C., Kong. W., Cappa, C., Lyapustin, A., Jonsson, H., Seidel, F., Xu, F., Diner, D. and Seinfeld, J. (2018). Photopolarimetric sensitivity to black carbon content of wildfire smoke: results from the 2016 ImPACT-PM field campaign, J. Geophys. Res., 123, 5376-5396, doi: 10.1029/2017JD028032.
[14] Fernandez, R. (2002). Do humans create deserts?, Trends Ecol. Evol., 17, 6-7, doi: 10.1016/S0169-5347(01)02366-7.
[15] Wagner, R., Ajtai, T., Kandler, K., Lieke, K., Linke, C., Müller, T., Schnaiter, M. and Vragel, M. (2012). Complex refractive indices of Saharan dust samples at visible and near UV wavelengths: a laboratory study, Atmos. Chem. Phys., 12, 2491–2512, doi: 10.5194/acp-12-2491-2012.
[16] Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., Journet, E., Nowak, S., Caquineau, S., Andreae, M., Kandler, K., Saeed, T., Piketh, S., Seibert, D., Williams, E. and Doussin, J. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing, Atmos. Chem. Phys., 17, 1901–1929, doi: 10.5194/acp-17-1901-2017.
[17] Littmann, T. (1991). Dust storm frequency in Asia: Climatic control and variability, International Journal of Climatology, 11, 393–412.
[18] Kinne, S., Lohmann, U., Feichter, J., Schulz, M., Timmreck, C., Ghan, S., Easter, R., Chin, M., Ginoux, P., Takemura, T., Tegen, I., Koch, D., Herzog, M., Penner, J., Pitari, G., Holben, B., Eck, T., Smirnov, A., Dubovik, O., Slutsker, I., Tanre, D., Torres, O., Mishchenko, M., Geogdzhayev, G., Chu, D. and Kaufman, Y. (2003). Monthly averages of aerosol properties: A global comparison among models, satellite data and AERONET ground data, J. Geophys. Res., 108, D20, 4634, doi: 10.1029/ 2001JD 001253.
[19] Westervelt, D., W. Horowitz, L., Naik, V., Golaz, J. and Mauzerall, L. (2015). Radiative forcing and climate response to projected 21st century aerosol decreases, Atmos. Chem. Phys., 15, 12681–12703, doi: 10.5194/acp-15-12681-2015.
[20] Jung, J., Souri, A., Wong, D., Lee, S., Jeon, W., Kim, J. and Choi, Y. (2019). The impact of the direct effect of aerosols on meteorology and air quality using aerosol optical depth assimilation during the KORUS-AQ campaign, J. Geophys. Res., 124, (14), doi: 10.1029/2019JD030641.
[21] King, M., Menzel, P. Kaufman, Y., Tanré, D., Gao, B., Platnick, S., Ackerman, S., Remer, L. Oincus, R. and Hubanks, P. (2003). Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE Trans. Geosci. Remote Sens., 41 (2), 442-458, doi: 10.1109/TGRS.2002.808226.
[22] Remer, L., Levy, R., Mattoo, S., Tanré, D., Gupta, P., Shi, Y., Sawyer, V., Leigh A. Munchak, L., Zhou, Y., Kim, M., Ichoku, C., Patadia, F., Li, R., Gassó, S., Kleidman, R. and Holben, B. (2020). The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future, Remote Sens., 12 (18), 2900; doi.org/10.3390/rs12182900.
[23] Torres, O., Bhartia, P., Herman, J., Ahmad, J. and Gleason, J. (1998). Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: Theoretical basis, J. Geophys. Res., 103, 17099–17110, doi: 10.1029/98JD00900.
[24] Torres, O., (2007). Aerosols and surface UV products from OMI observations: An overview, J. Geophys. Res., 112 (D24S47), doi: 10.1029/2007/JD008809.
[25] Deuzé, J., BréOn, F., Devaux, C., Goloub, P., Herman, M., Lafrance, B., Maignan, F., Marchand, A., Nadal, F., Perry, G., and Tanré, D. (2001). Remote sensing of aerosols over land surfaces from POLDER/ADEOS-1 polarized measurements, J. Geophys. Res., 106, 4913–4926, doi: 10.1029/2000JD900364.
[26] Waquet, F., Cornet, C., Deuzé, J., Dubovik, O., Ducos, F., Goloub, P., Herman, M., Lapyonok, T., Labonnote, L., Riedi, J., Tanré, D., Thieuleux, F. and Vanbauce, C. (2013). Retrieval of aerosol microphysical and optical properties above liquid clouds from POLDER/PARASOL polarization measurements,” Atmos. Meas. Tech., 6, 991–1016, doi: 10.5194/amt-6-991-2013.
[27] Waquet, F., Peers, F., Ducos, F., Goloub, P., Platnick, S., Riedi, J., Tanré, D., and Thieuleux, F. (2013). Global analysis of aerosol properties above clouds, Geophys. Res. Lett., 40, 5809–5814, https://doi.org/10.1002/2013GL057482.
[28] Peers, F., Waquet, F., Cornet, C., Dubuisson, P., Ducos, F., Goloub, P., Szczap, F., Tanré, D. and Thieuleux, F. (2015). Absorption of aerosols above clouds from POLDER/PARASOL measurements and estimation of their direct radiative effect, Atmos. Chem. Phys., 15, 4170–4196, doi: 10.5194/acp-15-4179-2015.
[29] Eck, T., Holben, B., Reid, J., Dubovik, O., Smirnov, A., O'Neill, N., Slutsker, I. and Kinne, S.(1999). Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols, J. Geophys. Res., 104 (D24), 31333-31349, doi: 10.1029/1999JD900923.
[30] O'Neill, N., Dubovik. O. and Eck, T., “Modified Angstrom exponent for the characterization of submicrometer aerosols,” Applied Optics, 40, 2368–2375, doi: /10.1364/AO.40.002368 (2001).
[31] Dubovik, O., Holben, B., Eck, T., Smirnov, A., Kaufman, Y., King, M., Tanré, D. and Slutsker, I. (2002). Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590−608, doi: 10.1175/1520-0469.
[32] Omar, A., Won, J., Winker, D., Yoon, S., Dubovik, O., and McCormick, P. (2005). Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements, J. Geophys. Res. 110 (D10S14), 1–14, doi: 10.1029/2004JD004874.
[33] Omar, A., Winker, D., Vaughan, M., Hu, Y., Trepte, C., Ferrare, R., Lee, K., Hostetler, C., Kittaka, C., Rogres, R., Kuehn, R. and Liu, Z. (2009). The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm, J. Atmos. Ocean. Tech., 26, 1994-2014, doi: 10.1175/2009JTECHA1231.1.
[34] Burton, S., Ferrare, R., Hostetler, C., Hair, J., Rogers, R., Obland, M., Butler, C., Cook, A., Harper, D. and Froyd, K. (2012). Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples, Atmos. Meas. Tech., 5, 73–98, doi: 10.5194/amt-5-73-2012.
[35] Hamill, P., Giordano, M., Ward, C., Giles, D. and Holben, B. (2016). An AERONET-based aerosol classification using the Mahalanobis distance, Atmos. Environ., 140, 213-233, doi: 10.1016/j.atmosenv.2016.06.002.
[36] Hsu, N., Herman, J., Bhartia, P., Seftor, C., Torres, O., Thompson, A., Gleason, J., Eck, T. and Holben, B. (1996). Detection of biomass burning smoke from TOMS measurements, Geophys. Res. Lett., 23, 745–748, doi: 10.1029/96GL00455.
[37] Chiapello, I., Prospero, J., Herman, J. and Hsu, N. (1999). Detection of mineral dust over the North Atlantic Ocean and Africa with the Nimbus 7 TOMS, J. Geophys. Res., 104, 9277–9291, doi: 10.1029/1998JD200083.
[38] Sano, I., Mukai, S., Okada, Y. and Mukai, M. (2009). Retrieval algorithm based on combined use of POLDER and GLI data for biomass aerosols, J. Rem. Sens. Soc. Jpn., 29 (1), 54–59, doi: 10.11440/rssj.2954.
[39] de Graaf, M., Stammes, P., Torres, O. and Koelemeijer, R. (2005). Absorbing Aerosol Index: Sensitivity Analysis, application to GOME and comparison with TOMS, J. Geophys. Res., 102 (D14), 16,911-16,921, doi: 10.1029/2004JD005178.
[40] Ciren, P. and S. Kondragunta, S. (2014). Dust aerosol index (DAI) algorithm for MODIS, J. Geophys. Res., 119, 4770-4792, doi: 10.1002/2013JD020855.
[41] Lensky, I. and Rosenfeld, D. (2008). Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT),” Atmos. Chem. Phys., 8, 6739–6753, doi: 10.5194/acp-8-6739-2008 (2008).
[42] Mukai, S., Sano, I. and Nakata, M. (2019). Inheritance of aerosol retrieval by GCOM-C/SGLI from ADEOS-2/GLI, Proc. SPIE 11152, Remote Sensing of Clouds and the Atmosphere XXIV, 1115215; doi: 10.1117/12.2532504.
[43] Jethva, H, Torres, O., Remer, L., Redemann, J., Livingston, J., Dunagan, S., Shinozuka, Y., Kacenelenbogen, M., Rosenheimer, M., and Spurr, R. (2016). Validating MODIS above-cloud aerosol optical depth retrieval from “color ratio” algorithm using direct measurements made by NASA’s airborne AATS and 4STAR sensors, Atmos. Meas. Tech., 9, 5053–5062, doi: 10.5194/amt-9-5053-2016.
[44] Mukai, S., Yokomae, T., Sano, I. and Nakata, M. (2012). Multiple scattering in a dense aerosol atmosphere, Atmos. Mes. Tech. Discuss, 5, 881–907, doi: 10.5194/amtd-5-881-2012.
[45] Lucia D., Waquet, F., Josset, D., Ferlay, N., Peers, F., Thieuleux, F., Ducos, F., Pascal, N., Tanré, D., Pelon, J., and Goloub, P. (2017). Consistency of aerosols above clouds characterization from A-Train active and passive measurements, Atmos. Meas. Tech., doi: 10.5194/amt-10-3499-2017.
[46] Peers, F., Francis, P., Fox, C., Abel, S., Szpek, K., Cotterell, M, Davies, N., Justin M. Langridge, J., Meyer, K. Platnick, E. and Haywood, J. (2019). Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 1: Method description and sensitivity, Atmos. Chem. Phys., 19, 9595–9611, doi: 10.5194/acp-19-9595-2019.
[47] Mukai, S., Sano, I. and Nakata, M. (2019). Efficient algorithms for aerosol retrieval from GCOM-C/SGLI”, Proc. IGARSS 2019, Atmosphere Applications: Aerosols and Atmospheric Chemistry, 7614-7617.
[48] Sano, I. and Mukai, S. (2019) Detection of dense biomass burning area and the particle properties from GCOM-C / SGLI measurements”, 2nd APOLO meeting, 2019/11/4-7 in Lille, France.
[49] Mukai, S., Sano, I. and Nakata, M. (2020) Effective characterization of aerosols in severe events using multi-channel measurements including polarization with GCOM-C/SGLI, Proc. SPIE 2020 (in press).
Cite This Article
  • APA Style

    Sonoyo Mukai, Makiko Nakata, Toshiyuki Fujito, Itaru Sano. (2020). Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data. International Journal of Environmental Monitoring and Analysis, 8(5), 170-180. https://doi.org/10.11648/j.ijema.20200805.16

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    ACS Style

    Sonoyo Mukai; Makiko Nakata; Toshiyuki Fujito; Itaru Sano. Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data. Int. J. Environ. Monit. Anal. 2020, 8(5), 170-180. doi: 10.11648/j.ijema.20200805.16

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    AMA Style

    Sonoyo Mukai, Makiko Nakata, Toshiyuki Fujito, Itaru Sano. Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data. Int J Environ Monit Anal. 2020;8(5):170-180. doi: 10.11648/j.ijema.20200805.16

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  • @article{10.11648/j.ijema.20200805.16,
      author = {Sonoyo Mukai and Makiko Nakata and Toshiyuki Fujito and Itaru Sano},
      title = {Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data},
      journal = {International Journal of Environmental Monitoring and Analysis},
      volume = {8},
      number = {5},
      pages = {170-180},
      doi = {10.11648/j.ijema.20200805.16},
      url = {https://doi.org/10.11648/j.ijema.20200805.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijema.20200805.16},
      abstract = {This work aimed at detection of aerosols above clouds (AAC). It has been known that AAC has significant potential to change the global radiation budget, namely plays an important role in elucidating climate change. First we examined the advantages of multichannel data from near-UV to thermal infrared (IR) including polarization channels at red and near-IR collected using the GCOM-C/SGLI. The near-UV data at 0.38μm and 0.41μm not only detected absorbing aerosols such as biomass burning aerosols (BBA) or mineral dust (DUST), but were also used to distinguish between BBA and DUST with short wavelength IR measurements at 1.63μm. Because understanding aerosol types facilitates subsequent aerosol characterization, classification algorithms for aerosol types have been dealt with since the previous work. Discriminant verification was performed using ground measurements from NASA/AERONET and practically examined in a case of large forest fire. Then the detection of optically thick clouds was challenged in a similar way to aerosol classification in order to lead such a final goal of this work as detection of aerosols above clouds. Subsequently some scenes concerned with DUST type aerosols or BBA ones above water clouds were detected using GCOM-C/SGLI radiance or polarization measurements, respectively, and validated with Terra/MODIS products.},
     year = {2020}
    }
    

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    T1  - Efficient Detection of Aerosols Above Clouds Utilizing GCOM-C/SGLI Data
    AU  - Sonoyo Mukai
    AU  - Makiko Nakata
    AU  - Toshiyuki Fujito
    AU  - Itaru Sano
    Y1  - 2020/10/26
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    DO  - 10.11648/j.ijema.20200805.16
    T2  - International Journal of Environmental Monitoring and Analysis
    JF  - International Journal of Environmental Monitoring and Analysis
    JO  - International Journal of Environmental Monitoring and Analysis
    SP  - 170
    EP  - 180
    PB  - Science Publishing Group
    SN  - 2328-7667
    UR  - https://doi.org/10.11648/j.ijema.20200805.16
    AB  - This work aimed at detection of aerosols above clouds (AAC). It has been known that AAC has significant potential to change the global radiation budget, namely plays an important role in elucidating climate change. First we examined the advantages of multichannel data from near-UV to thermal infrared (IR) including polarization channels at red and near-IR collected using the GCOM-C/SGLI. The near-UV data at 0.38μm and 0.41μm not only detected absorbing aerosols such as biomass burning aerosols (BBA) or mineral dust (DUST), but were also used to distinguish between BBA and DUST with short wavelength IR measurements at 1.63μm. Because understanding aerosol types facilitates subsequent aerosol characterization, classification algorithms for aerosol types have been dealt with since the previous work. Discriminant verification was performed using ground measurements from NASA/AERONET and practically examined in a case of large forest fire. Then the detection of optically thick clouds was challenged in a similar way to aerosol classification in order to lead such a final goal of this work as detection of aerosols above clouds. Subsequently some scenes concerned with DUST type aerosols or BBA ones above water clouds were detected using GCOM-C/SGLI radiance or polarization measurements, respectively, and validated with Terra/MODIS products.
    VL  - 8
    IS  - 5
    ER  - 

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Author Information
  • School of Applied Information Technology, The Kyoto College of Graduate Studies for Informatics, Kyoto, Japan

  • Faculty of Applied Sociology, Kindai University, Higashi-Osaka, Japan

  • School of Applied Information Technology, The Kyoto College of Graduate Studies for Informatics, Kyoto, Japan

  • Faculty of Science and Engineering, Kindai University, Higashi-Osaka, Japan

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