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http://dx.doi.org/10.7780/kjrs.2016.32.6.12

Retrieval and Validation of Aerosol Optical Properties Using Japanese Next Generation Meteorological Satellite, Himawari-8  

Lim, Hyunkwang (Global Environment Laboratory, Dept. of Atmospheric Sciences, Yonsei University)
Choi, Myungje (Global Environment Laboratory, Dept. of Atmospheric Sciences, Yonsei University)
Kim, Mijin (Global Environment Laboratory, Dept. of Atmospheric Sciences, Yonsei University)
Kim, Jhoon (Global Environment Laboratory, Dept. of Atmospheric Sciences, Yonsei University)
Chan, P.W. (Hong Kong Observatory)
Publication Information
Korean Journal of Remote Sensing / v.32, no.6, 2016 , pp. 681-691 More about this Journal
Abstract
Using various satellite measurements in UV, visible and IR, diverse algorithms to retrieve aerosol information have been developed and operated to date. Advanced Himawari Imager (AHI) onboard the Himawari 8 weather satellite was launched in 2014 and has 16 channels from visible to Thermal InfRared (TIR) in high temporal and spatial resolution. Using AHI, it is very valuable to retrieve aerosol optical properties over dark surface to demonstrate its capability. To retrieve aerosol optical properties using visible and Near InfRared (NIR) region, surface signal is very important to be removed which can be estimated using minimum reflectivity method. The estimated surface reflectance is then used to retrieve the aerosol optical properties through the inversion process. In this study, we retrieve the aerosol optical properties over dark surface, but not over bright surface such as clouds, desert and so on. Therefore, the bright surface was detected and masked using various infrared channels of AHI and spatial heterogeneity, Brightness Temperature Difference (BTD), etc. The retrieval result shows the correlation coefficient of 0.7 against AERONET, and the within the Expected Error (EE) of 49%. It is accurately retrieved even for low Aerosol Optical Depth (AOD). However, AOD tends to be underestimated over the Beijing Hefei area, where the surface reflectance using the minimum reflectance method is overestimated than the actual surface reflectance.
Keywords
Himawari-8; AHI; remote sensing; aerosol optical properties; AERONET;
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1 Spurr, R.J.D., 2006. VLIDORT: A linearized pseudospherical vector discrete ordinate radiative transfer code for forward model and retrieval studies in multilayer multiple scattering media. Journal of Quantitative Spectroscopy and Radiative Transfer, 102(2): 316-342.   DOI
2 Stocker, T., D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex and B. Midgley, 2013. 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.
3 Torres, O., H. Jethva, and P. Bhartia, 2012. Retrieval of aerosol optical depth above clouds from OMI observations: Sensitivity analysis and case studies. Journal of the Atmospheric Sciences, 69(3): 1037-1053.   DOI
4 Wong, M.S., K.-H. Lee, J.E. Nichol, and Z. Li, 2010. Retrieval of Aerosol Optical Thickness Using MODIS, a Study in Hong Kong and the Pearl River Delta Region. Geoscience and Remote Sensing, IEEE Transactions on, 48(8): 3318-3327.   DOI
5 Zhao, H., H. Che, Y. Ma, X. Xia, Y. Wang, P. Wang and X. Wu, 2015. Temporal variability of the visibility, particulate matter mass concentration and aerosol optical properties over an urban site in Northeast China. Atmospheric Research, 166: 204-212.   DOI
6 Holben, B., T. Eck, I. Slutsker, D. Tanre, J. Buis, A. Setzer, E. Vermote, J. Reagan, Y. Kaufman and T. Nakajima, 1998. AERONET-A federated instrument network and data archive for aerosol characterization. Remote sensing of environment, 66(1): 1-16.   DOI
7 Choi, M., J. Kim, J. Lee, M. Kim, Y.-J. Park, U. Jeong, W. Kim, H. Hong, B. Holben, T.F. Eck, C.H. Song, J.-H. Lim, and C.-K. Song, 2016. GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGONNE Asia 2012 campaign. Atmospheric Measurement Techniques, 9(3): 1377-1398.   DOI
8 Ciren, P. and S. Kondragunta, 2014. Dust aerosol index (DAI) algorithm for MODIS. Journal of Geophysical Research: Atmospheres, 119(8): 4770-4792.   DOI
9 Dubovik, O. and M.D. King, 2000. A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements. Journal of Geophysical Research, D16, 105: 20673-20696.   DOI
10 Dubovik, O., A. Smirnov, B. Holben, M. King, Y. Kaufman, T. Eck and I. Slutsker, 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements, Journal of Geophysical Research, D8, 105, 9791-9806.   DOI
11 Hsu, N.C., S.-C. Tsay, M.D. King and J.R. Herman, 2004. Aerosol properties over bright-reflecting source regions. Geoscience and Remote Sensing, IEEE Transactions on, 42(3): 557-569.   DOI
12 Hsu, N.C., S.-C. Tsay, M.D. King and J.R. Herman, 2006. Deep blue retrievals of Asian aerosol properties during ACE-Asia. Geoscience and Remote Sensing, IEEE Transactions on, 44(11): 3180-3195.   DOI
13 Jeong, U., J. Kim, C. Ahn, O. Torres, X. Liu, P.K. Bhartia, R.J. Spurr, D. Haffner, K. Chance and B. N. Holben, 2016. An optimal-estimationbased aerosol retrieval algorithm using OMI near-UV observations. Atmospheric Chemistry and Physics, 16(1): 177-193.   DOI
14 Knapp, K., R. Frouin, S. Kondragunta and A. Prados, 2005. Toward aerosol optical depth retrievals over land from GOES visible radiances: determining surface reflectance. International Journal of Remote Sensing, 26(18): 4097-4116.   DOI
15 Jethva, H., O. Torres, L.A. Remer and P.K. Bhartia, 2013. A color ratio method for simultaneous retrieval of aerosol and cloud optical thickness of above-cloud absorbing aerosols from passive sensors: Application to MODIS measurements. Geoscience and Remote Sensing, IEEE Transactions on, 51(7): 3862-3870.   DOI
16 Jethva, H., O. Torres, F. Waquet, D. Chand and Y. Hu, 2014. How do A-train sensors intercompare in the retrieval of above cloud aerosol optical depth? A case study-based assessment. Geophysical Research Letters, 41(1): 186-192.   DOI
17 Kim, J., J. Lee, H.C. Lee, A. Higurashi, T. Takemura and C. H. Song, 2007. Consistency of the aerosol type classification from satellite remote sensing during the Atmospheric Brown Cloud-East Asia Regional Experiment campaign. Journal of Geophysical Research: Atmospheres, 112(D22), D22S33.
18 Kim, J., J.M. Yoon, M. Ahn, B. Sohn and H. Lim, 2008. Retrieving aerosol optical depth using visible and mid-IR channels from geostationary satellite MTSAT-1R. International Journal of Remote Sensing, 29(21): 6181-6192.   DOI
19 Kim, M., J. Kim, M.S. Wong, J. Yoon, J. Lee, D. Wu, P. Chan, J.E. Nichol, C.-Y. Chung and M.-L. Ou, 2014. Improvement of aerosol optical depth retrieval over Hong Kong from a geostationary meteorological satellite using critical reflectance with background optical depth correction. Remote Sensing of Environment, 142: 176-187.   DOI
20 Lau, K.M. and K.M. Kim, 2006. Observational relationships between aerosol and Asian monsoon rainfall, and circulation. Geophysical Research Letters, 33(21), L21810, doi:10.1029/2006GL027546.   DOI
21 Lee, J., J. Kim, C.H. Song, S.B. Kim, Y. Chun, B.J. Sohn and B. N. Holben, 2010a. Characteristics of aerosol types from AERONET sunphotometer measurements. Atmospheric Environment, 44(26): 3110-3117.   DOI
22 Lee, J., J. Kim, C.H. Song, J.-H. Ryu, Y.-H. Ahn and C.K. Song, 2010b. Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager. Remote Sensing of Environment, 114(5): 1077-1088.   DOI
23 Levy, R.C., L.A. Remer, S. Mattoo, E.F. Vermote and Y.J. Kaufman, 2007. Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. Journal of Geophysical Research: Atmospheres, 112(D13): D13211, doi:10.1029/2006JD007811.   DOI
24 Lee, J., J. Kim, P. Yang and N.C. Hsu, 2012. Improvement of aerosol optical depth retrieval from MODIS spectral reflectance over the global ocean using new aerosol models archived from AERONET inversion data and tri-axial ellipsoidal dust database. Atmospheric Chemistry and Physics, 12(15): 7087-7102.   DOI
25 Levy, R., S. Mattoo, L. Munchak, L. Remer, A. Sayer and N. Hsu, 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech. Discuss, 6: 159-259.   DOI
26 Levy, R. C., L.A. Remer, R.G. Kleidman, S. Mattoo, C. Ichoku, R. Kahn, and T.F. Eck, 2010. Global evaluation of the Collection 5 MODIS darktarget aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399-10420.   DOI
27 Sayer, A., N. Hsu, C. Bettenhausen, and M.J. Jeong, 2013. Validation and uncertainty estimates for MODIS Collection 6 "Deep Blue" aerosol data. Journal of Geophysical Research: Atmospheres, 118(14): 7864-7872.   DOI
28 Remer, L. A., Y. Kaufman, D. Tanre, S. Mattoo, D. Chu, J.V. Martins, R.-R. Li, C. Ichoku, R. Levy, and R. Kleidman, 2005. The MODIS aerosol algorithm, products, and validation. Journal of the atmospheric sciences, 62(4): 947-973.   DOI
29 Remer, L.A., R.G. Kleidman, R.C. Levy, Y.J. Kaufman, D. Tanre, S. Mattoo, J. V. Martins, C. Ichoku, I. Koren, H. Yu and B. N. Holben, 2008. Global aerosol climatology from the MODIS satellite sensors. Journal of Geophysical Research, 113(D14).