Journal of Korean Society for Atmospheric Environment (한국대기환경학회지)

Korean Society for Atmospheric Environment (한국대기환경학회)
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Detection and Classification of Major Aerosol Type Using the Himawari-8/AHI Observation Data

Himawari-8/AHI 관측자료를 이용한 주요 대기 에어로솔 탐지 및 분류 방법

  • Lee, Kwon-Ho (Radiation-Satellite Research Institute (RSRI), Department of Atmospheric & Environmental Sciences (DAES), Gangneung-Wonju National University (GWNU)) ;
  • Lee, Kyu-Tae (Radiation-Satellite Research Institute (RSRI), Department of Atmospheric & Environmental Sciences (DAES), Gangneung-Wonju National University (GWNU))
  • 이권호 (강릉원주대학교 복사위성연구소, 대기환경과학과) ;
  • 이규태 (강릉원주대학교 복사위성연구소, 대기환경과학과)
  • Received : 2018.06.10
  • Accepted : 2018.06.12
  • Published : 2018.06.30
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Abstract

Due to high spatio-temporal variability of amount and optical/microphysical properties of atmospheric aerosols, satellite-based observations have been demanded for spatiotemporal monitoring the major aerosols. Observations of the heavy aerosol episodes and determination on the dominant aerosol types from a geostationary satellite can provide a chance to prepare in advance for harmful aerosol episodes as it can repeatedly monitor the temporal evolution. A new geostationary observation sensor, namely the Advanced Himawari Imager (AHI), onboard the Himawari-8 platform, has been observing high spatial and temporal images at sixteen wavelengths from 2016. Using observed spectral visible reflectance and infrared brightness temperature (BT), the algorithm to find major aerosol type such as volcanic ash (VA), desert dust (DD), polluted aerosol (PA), and clean aerosol (CA), was developed. RGB color composite image shows dusty, hazy, and cloudy area then it can be applied for comparing aerosol detection product (ADP). The CALIPSO level 2 vertical feature mask (VFM) data and MODIS level 2 aerosol product are used to be compared with the Himawari-8/AHI ADP. The VFM products can deliver nearly coincident dataset, but not many match-ups can be returned due to presence of clouds and very narrow swath. From the case study, the percent correct (PC) values acquired from this comparisons are 0.76 for DD, 0.99 for PA, 0.87 for CA, respectively. The MODIS L2 Aerosol products can deliver nearly coincident dataset with many collocated locations over ocean and land. Increased accuracy values were acquired in Asian region as POD=0.96 over land and 0.69 over ocean, which were comparable to full disc region as POD=0.93 over land and 0.48 over ocean. The Himawari-8/AHI ADP algorithm is going to be improved continuously as well as the validation efforts will be processed by comparing the larger number of collocation data with another satellite or ground based observation data.

Keywords

References

  1. Ackerman, S.A., Strabala, K., Menzel, P., Frey, R., Moeller, C., Gumley, L., Baum, B., Seeman, S.W., Zhang, H. (1998) Discriminating clear-sky from cloud with MODIS. Algorithm theoretical basis document (MOD35), Journal of Geophysical Research, 103(D24), 32141-32157. https://doi.org/10.1029/1998JD200032
  2. Al-Saadi, J., Szykman, J., Pierce, B., Kittaka, C., Neil, D., Chu, D.A., Remer, L., Gumley, L., Prins, E., Weinstock, L., McDonald, C., Wayland, R., Dimmick, F. (2005) Improving national air quality forecasts with satellite aerosol observations, Bulletin of the American Meteorological Society, 86(9), 1249-1261. https://doi.org/10.1175/BAMS-86-9-1249
  3. Bae, M.-S., Park, D.-J., Lee, K.-H., Cho, S.-S., Lee, K.-Y., Park, K. (2017) Determination of analytical approach for ambient $PM_{2.5}$ free amino acids using LC-MSMS, Journal of Korean Society for Atmospheric Environment, 33(1), 55-64, https://doi.org/10.5572/KOSAE.2017.33.1.055. (in Korean with English abstract).
  4. Charlson, R.J., Lovelock, J.E., Andreae, M.O., Warren, S.G. (1987) Oceanic phytoplankton, atmospheric sulphur, cloud albedo, and climate, Nature, 326, 655-661. https://doi.org/10.1038/326655a0
  5. Charlson, R.J., Schwartz, S.E., Hales, J.H., Cess, R.D., Coakley Jr. J.A., Hansen, J.E., Hofmann, D.J. (1992) Climate forcing by anthropogenic aerosols, Science, 255, 423-430, doi:10.1126/science.255.5043.423.
  6. Darmenov, A., Sokolik, I.N. (2005) Identifying the regional thermal-IR radiative signature of mineral dust with MODIS, Geophysical Research Letters, 32, L16803, doi:10.1029/2005GL023092.
  7. Ellrod, G.P., Connell, B.H., Hillger, D.W. (2003) Improved detection of airborne volcanic ash using multispectral infrared satellite data, Journal of Geophysical Research, 108(D12), 4356, doi:10.1029/2002JD002802.
  8. Frey, R.A., Ackerman, S.A., Liu, Y., Strabala, K.I., Zhang, H., Key, J.R., Wang, X. (2008) Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for Collection 5, Journal of Atmospheric and Oceanic Technology, 25, 1057-1072. https://doi.org/10.1175/2008JTECHA1052.1
  9. Griggs, M. (1975) Measurements of atmospheric aerosol optical thickness over water using ERTS-1 data, Journal of the Air Pollution Control Association, 25, 622-626. https://doi.org/10.1080/00022470.1975.10470118
  10. Hale, R.E., Querry, M.R. (1973) Optical constants of water in the 200-nm to 200-${\mu}m$ wavelength region, Applied Optics, 12(3), 555-563. https://doi.org/10.1364/AO.12.000555
  11. Hansell, R.A., Ou, S.C., Liou, K.N., Roskovensky, J.K., Tsay, S.C., Hsu, C., Ji, Q. (2007) Simultaneous detection/separation of mineral dust and cirrus clouds using MODIS thermal infrared window data, Geophysical Research Letters, 34, L11808, doi:10.1029/2007GL029388.
  12. Hess, M., Koepke, P., Schult, I. (1998) Optical Properties of Aerosols and Clouds: The Software Package OPAC, Bulletin of the American Meteorological Society, 79, 831-844. https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2
  13. Hoff, R., Zhang, H., Jordan, N., Prados, A., Engel-Cox, J., Huff, A., Weber, S., Zell, E., Kondragunta, S., Szykman, J., Johns, B., Dimmick, F., Wimmers, A., Al-Saadi, J., Kittaka, C. (2009) Application of the Three-Dimensional Air Quality System (3D-AQS) to Western U.S. Air Quality: IDEA, Smog Blog, Smog Stories, Air Quest, and the Remote Sensing Information Gateway, Journal of the Air & Waste Management Association, 59, 980-989. https://doi.org/10.3155/1047-3289.59.8.980
  14. Hsu, N.C., Herman, J.R., Bhartia, P.K., Seftor, C.J., Torres, O., Thompson, A.M., Gleason, J.F., Eck, T.F., Holben, B.N. (1996) Detection of Biomass Burning Smoke from TOMS Measurements, Geophysical Research Letters, 23(7), 745-748. https://doi.org/10.1029/96GL00455
  15. Hsu, N.C., Tsay, S.C., King, M.D., Herman, J.R. (2006) Deep blue retrievals of Asian aerosol properties during ACE-Asia, IEEE Transactions on Geoscience and Remote Sensing, 44, 3180-3195. https://doi.org/10.1109/TGRS.2006.879540
  16. Hutchison, K.D., Hardy, K.R. (1995) Threshold functions for automated cloud analyses of global meteorological satellite imagery, International Journal of Remote Sensing, 16, 3665-3680. https://doi.org/10.1080/01431169508954653
  17. Inoue, T. (1987) A cloud type classification with NOAA 7 splitwindow measurements, Journal of Geophysical Research, 92(D4), 3991-4000, doi:10.1029/JD092iD04p03991.
  18. International Panel on Climate Change (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, Cambridge, United Kingdom and New York, NY, USA.
  19. Kaufman, Y.J., Koren, I., Remer, L.A., Rosenfeld, D., Rudich, Y. (2005) The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean, Proceedings of the National Academy of Sciences of the United States of America, 102(32), 11207-11212, doi:10.1073/pnas.0505191102.
  20. Kaufman, Y.J., Tanre, D., Boucher, O. (2002) A satellite view of aerosols in the climate system, Nature, 419, 215-223. https://doi.org/10.1038/nature01091
  21. Kim, K., Lee, D., Lee, K., Lee, K., Noh, Y. (2016) Estimation of surface-level $PM_{2.5}$ concentration based on MODIS aerosol optical depth over Jeju, Korea, Korean Journal of Remote Sensing, 32(5), 413-421, http://dx.doi.org/10.7780/kjrs.2016.32.5.2. (in Korean with English abstract)
  22. King, M.D., Kaufman, Y.J., Tanre, D., Nakajima, T. (1999) Remote sensing of tropospheric aerosols from space: Past, present, and future, Bulletin of the American Meteorological Society, 80, 2229-2259. https://doi.org/10.1175/1520-0477(1999)080<2229:RSOTAF>2.0.CO;2
  23. Koepke, P., Hess, M., Schult, I., Shettle, E.P. (1997) Global Aerosol Data Set, Report No. 243, MPI Hamburg, Germany, 44 pp.
  24. Lee, K.H. (2012) Impact of Northeast Asian biomass burning activities on regional atmospheric environment, Journal of the Korean Association of Geographic Information Studies, 15(1), 184-196. (in Korean with English abstract) https://doi.org/10.11108/kagis.2012.15.1.184
  25. Lee, K.H., Kim, Y.J. (2010) Satellite remote sensing of Asian storm days, Atmospheric Measurement Techniques, 3, 1771-1784, doi:10.5194/amt-3-1771-2010.
  26. Lee, K.H., Kim, Y.J., von Hoyningen-Huene, W., Burrow, J.P. (2007) Spatio-temporal variability of satellite-derived aerosol optical thickness over northeast Asia in 2004, Atmospheric Environment, 41(19), 3959-3973, doi:10.1016/j.atmosenv.2007.01.048.
  27. Lee, K.H., Lee, D.H., Kim, Y.J. (2006) Application of MODIS satellite observation data for air quality forecast, Journal of Korean Society for Atmospheric Environment, 22(6), 851-862. (in Korean with English abstract)
  28. Lee, K.H., Lee, K.-T., Chung, S.R. (2016) Time-resolved observation of volcanic ash using COMS/MI: a case study from the 2011 Shinmoedake eruption, Remote Sensing of Environment, 173, 122-132. doi:10.1016/j.rse.2015.11.014.
  29. Lee, K.H., Li, Z., Kim, Y.J., Kokhanovsky, A. (2009) Atmospheric aerosol monitoring from satellite observations: A history of three decades. In Atmospheric and Biological Environmental Monitoring (Kim, Y.J., Platt, U., Gu, M.B. and Iwahashi, H. Eds), Springer, Berlin, Heidelberg, pp. 13-38.
  30. Lee, K.H., Wong, M.S., Chung, S.-R., Sohn, E. (2014) Improved volcanic ash detection based on a hybrid reverse absorption technique, Atmospheric Research, 143, 31-41. https://doi.org/10.1016/j.atmosres.2014.01.019
  31. Legrand, M., Cautenet, G., Burie, J.C. (1992) Thermal Impact of Saharan Dust over Land. Part 11: Application to Satellite IR Remote Sensing, Journal of Applied Meteorology, 31, 181-193. https://doi.org/10.1175/1520-0450(1992)031<0181:TIOSDO>2.0.CO;2
  32. Levy, R.C., Remer, L.A., Mattoo, S., Vermote, E.F., Kaufman, Y.J. (2007) Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, 112(D13211).
  33. Li, Z., Khananian, A., Fraser, R., Cihlar, J. (2001) Automatic detection of fire smoke using artificial neural networks and threshold approaches applied to AVHRR imagery, IEEE Transactions on Geoscience and Remote Sensing, 39, 1859-1870. https://doi.org/10.1109/36.951076
  34. Martins, J.V., Tane, D., Remer, L., Kaufman, Y., Mattoo, S., Levy, R. (2002) MODIS cloud screening for remote sensing of aerosols over oceans using spatial variability, Geophysical Research Letters, 29(12), 8009, doi:10.1029/2001GL013252.
  35. Okada, Y., Mukai, S., Sano, I. (2001) Neural network approach for aerosol retrieval, the International Geoscience and Remote Sensing Symposium (IGARSS), 4, 1716-1718.
  36. Prata, A.J. (1989) Observations of volcanic ash clouds in the 10-12-micron window using AVHRR/2 Data, International Journal of Remote Sensing, 10, 751-761. https://doi.org/10.1080/01431168908903916
  37. Ricchiazzi, P., Yang, S., Gautier, C., Sowle, D. (1998) SBDART: A research and teaching software tool for planeparallel radiative transfer in the earth's atmosphere, Bulletin of the American Meteorological Society, 79(10), 2101-2114. https://doi.org/10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2
  38. Saunders, R.W., Kriebel, K.T. (1988) An improved method for detecting clear sky and cloudy radiances from AVHRR data, International Journal of Remote Sensing, 9(1), 123-150, doi:10.1080/01431168808954841.
  39. Vaughan, M., Powell, K., Kuehn, R., Young, S., Winker, D., Hostetler, C., Hunt, W., Liu, Z., McGill, M., Getzewich, B. (2009) Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements, Journal of Atmospheric and Oceanic Technology, 26, 2034-2050. https://doi.org/10.1175/2009JTECHA1228.1
  40. Warren, S.G., Brandt, R.E. (2008) Optical constants of ice from the ultraviolet to the microwave: A revised compilation, Journal of Geophysical Research, 113, D14220. https://doi.org/10.1029/2007JD009744
  41. World Climate Research Programme (WCRP) (1986) A preliminary cloudless standard atmosphere for radiation computation, WCP-112, WMO/TD-24, 53. Geneva.