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Intercomparing the Aerosol Optical Depth Using the Geostationary Satellite Sensors (AHI, GOCI and MI) from Yonsei AErosol Retrieval (YAER) Algorithm

연세에어로졸 알고리즘을 이용하여 정지궤도위성 센서(AHI, GOCI, MI)로부터 산출된 에어로졸 광학두께 비교 연구

  • Lim, Hyunkwang (Department of Atmospheric Sciences, Yonsei University) ;
  • Choi, Myungje (Department of Atmospheric Sciences, Yonsei University) ;
  • Kim, Mijin (Department of Atmospheric Sciences, Yonsei University) ;
  • Kim, Jhoon (Department of Atmospheric Sciences, Yonsei University) ;
  • Go, Sujung (Department of Atmospheric Sciences, Yonsei University) ;
  • Lee, Seoyoung (Department of Atmospheric Sciences, Yonsei University)
  • Received : 2018.02.21
  • Accepted : 2018.04.25
  • Published : 2018.04.30

Abstract

Aerosol Optical Properties (AOPs) are retrieved using the geostationary satellite instruments such as Geostationary Ocean Color Imager (GOCI), Meteorological Imager (MI), and Advanced Himawari Imager (AHI) through Yonsei AErosol Retrieval algorithm (YAER). In this study, the retrieved aerosol optical depths (AOD)s from each instrument were intercompared and validated with the ground-based sunphotometer AErosol Robotic NETwork (AERONET) data. As a result, the four AOD products derived from different instruments showed consistent results over land and ocean. However, AODs from MI and GOCI tend to be overestimated due to cloud contamination. According to the comparison results with AERONET, the percentage within expected errors (EE) are 36.3, 48.4, 56.6, and 68.2% for MI, GOCI, AHI-minimum reflectivity method (MRM), and AHI-estimated surface reflectance from shortwave Infrared (ESR) product, respectively. Since MI AOD is retrieved from a single visible channel, and adopts only one aerosol type by season, EE is relatively lower than other products. On the other hand, the AHI ESR is more accurate than the minimum reflectance method as used by GOCI, MI, and AHI MRM method in May and June when the vegetation is relatively abundant. These results are explained by the RMSE and the EE for each AERONET site. The ESR method result show to be better than the other satellite product in terms of EE for 15 out of 22 sites used for validation, and they are better than the other product for 13 sites in terms of RMSE. In addition, the error in observation time in each product is found by using characteristics of geostationary satellites. The absolute median biases at 00 to 06 Universal Time Coordinated (UTC) are 0.05, 0.09, 0.18, 0.18, 0.14, 0.09, and 0.10. The absolute median bias by observation time has appeared in MI and the only 00 UTC appeared in GOCI.

동아시아 지역의 에어로졸 광학정보에 대하여 천리안 위성에 탑재된 GOCI, MI, 그리고 Himawari 8 위성에 탑재된 AHI 센서들의 측정자료를 연세 에어로졸 알고리즘(YAER)을 이용하여 산출하였다. 본 연구에서는 각 센서에서 산출되는 에어로졸 광학두께(Aerosol optical depth, AOD)를 상호비교하고, 지상장비인 AERONET과의 검증결과도 보였다. 사용한 AOD 자료는 세 종류의 센서에서 최소반사도 방법(Minimum reflectance method, MRM)을 이용하여 산출된 AOD, 그리고 AHI에서는 단파적외선이용 지표면정보산출방법(Estimated surface reflectance from SWIR, ESR)을 이용한 방법의 AOD까지 총 네가지이다. 세 위성간의 산출결과에서 육지와 해양에서 일관된 결과를 보이고 있으나, MI와 GOCI에서는 구름제거에 한계가 존재하며 AOD의 과대 추정 문제가 보인다. 한편 지상장비인 AERONET과의 비교검증결과는 MI, GOCI, 그리고 AHI 의 MRM 방법, ESR 방법 에서 기대오차 내에 들어오는 비율(% within Expected error, EE)이 36.3, 48.4, 56.6, 68.2%로 각각 나타났다. MI의 경우는 단일 채널을 이용하여 에어로졸광학정보를 산출하고 있고, 계절에 따른 에어로졸 유형을 고정하고 있어, 다양한 오차가 포함되어 낮은 EE를 보이고 있다. 5, 6월에는 ESR 방법의 결과물은 높은 EE 를 나타내고 있는데 이는 GOCI, MI, MRM 방법 에서 사용하고 있는 최소반사도 방법보다 정확한 지면반사도를 산출하기 때문으로 추정된다. 이 결과는 AERONET 사이트 별로 RMSE 와 EE 로 설명하고 있으며, 검증한 총 22개 사이트 중 15개 사이트에서 ESR 방법이 가장 높은 EE 를 보이고 있고, RMSE는 13개 사이트에서 가장 낮게 나타났다. 또한 정지궤도 위성의 특징을 이용하여 시간대별 오차를 각 산출물 별로 보였다. 00~06 Universal Time Coordinated (UTC)에서 한 시간별로 최대로 나타나는 absolute median bias error 는 0.05, 0.09, 0.18, 0.18, 0.14, 0.09, 0.10 로 나타나며 00UTC에서는 GOCI 에서, 나머지 시간대에서는 MI에서 최대오차를 보였다.

Keywords

References

  1. Bilal, M., Nichol, J.E., and Wang, L., 2017, New customized methods for improvement of the MODIS C6 Dark Target and Deep Blue merged aerosol product. Remote Sensing of Environment, 197, 115-124. https://doi.org/10.1016/j.rse.2017.05.028
  2. 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 DRAGON-NE Asia 2012 campaign. Atmospheric Measurement Techniques 9(3): 1377-1398. https://doi.org/10.5194/amt-9-1377-2016
  3. Choi, M., J. Kim, J. Lee, M. Kim, Y.-J. Park, B. Holben, T. F. Eck, Z. Li and C. H. Song., 2018, GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia. Atmospheric Measurement Techniques 11(1): 385-408. https://doi.org/10.5194/amt-11-385-2018
  4. Cox, C. and Munk, 1954, Statistics of the sea surface derived from sun glitter, J. Marine Res., 13, 198-227.
  5. Daisaku, U., 2016, Aerosol Optical Depth product derived from Himawari-8 data for Asian dust monitoring. Meteorological Satellite Center Technical Note (61).
  6. Garay, M. J., O. V. Kalashnikova and M. A. Bull., 2017, Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data. Atmospheric Chemistry and Physics 17(8): 5095-5106. https://doi.org/10.5194/acp-17-5095-2017
  7. 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. https://doi.org/10.1109/TGRS.2006.879540
  8. Hsu, N.C., M. J. Jeong, C. Bettenhausen, A. M. Sayer, R. Hansell, C. S. Seftor, J. Huang and S. C. Tsay., 2013, Enhanced Deep Blue aerosol retrieval algorithm: The second generation. Journal of Geophysical Research: Atmospheres 118(16): 9296-9315. https://doi.org/10.1002/jgrd.50712
  9. Jackson, J.M., H. Liu, I. Laszlo, S. Kondragunta, L. A.Remer, J. Huang and H.-C.Huang., 2013, Suomi-NPP VIIRS aerosol algorithms and data products. Journal of Geophysical Research: Atmospheres 118(22): 12,673-612,689. https://doi.org/10.1002/2013JD020449
  10. Kalashnikova, O.V., M. J.Garay, J. V. Martonchik and D. J. Diner., 2013, MISR Dark Water aerosol retrievals: operational algorithm sensitivity to particle nonsphericity. Atmospheric Measurement Techniques 6(8): 2131-2154. https://doi.org/10.5194/amt-6-2131-2013
  11. Kim, J., Yoon, J. M., Ahn, M., Sohn, B., and Lim, H., 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. https://doi.org/10.1080/01431160802175553
  12. 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. https://doi.org/10.1016/j.rse.2013.12.003
  13. Kim, M., J. Kim, U. Jeong, W. Kim, H. Hong, B. Holben, T. F.Eck, J. H. Lim, C. K. Song, S. Lee and C. Y. Chung., 2016, Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS). Atmospheric Chemistry and Physics 16(3): 1789-1808. https://doi.org/10.5194/acp-16-1789-2016
  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. https://doi.org/10.1080/01431160500099329
  15. Laszlo, I. and H. Liu., 2016, EPS Aerosol Optical Depth (AOD) Algorithm Theoretical Basis Document.
  16. 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)
  17. Levy, RC., 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. https://doi.org/10.5194/amtd-6-159-2013
  18. Lim, H., M. Choi, M. Kim, J. Kim and P. W. Chan., 2016, Retrieval and Validation of Aerosol Optical Properties Using Japanese Next Generation Meteorological Satellite, Himawari-8. Korean Journal of Remote Sensing 32(6): 681-691. https://doi.org/10.7780/kjrs.2016.32.6.12
  19. 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.
  20. Tao, M., Chen, L., Wang, Z., Tao, J., Che, H., Wang, X., Wang, Y., 2015, Comparison and evaluation of the MODIS Collection 6 aerosol data in China. Journal of Geophysical Research: Atmospheres, 120, 6992-7005. https://doi.org/10.1002/2015JD023360
  21. Wang, J., Christopher, S.A., Brechtel, F., Kim, J., Schmid, B., Redemann, J., B. Russell, P., Quinn, P., and Holben, B. N., 2003, Geostationary satellite retrievals of aerosol optical thickness during ACE-Asia. Journal of Geophysical Research: Atmospheres, 108(D23).
  22. Yoon, J. M., J. Kim, J. H. Lee, H. K. Cho, B.-J. Sohn and M.-H. Ahn., 2007, Retrieval of aerosol optical depth over east Asia from a geostationary satellite, MTSAT-1R. Asia-Pacific Journal of Atmospheric Sciences 43(2): 49-58.
  23. Zhang, H., S. Kondragunta, I. Laszlo, H. Liu, L. A. Remer, J. Huang, S. Superczynski and P. Ciren., 2016, An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database. Journal of Geophysical Research: Atmospheres 121 (18).