Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data
Abstract
:1. Introduction
2. Data and Methods
2.1. Data
2.1.1. Himawari-8 Advanced Himawari Imager (AHI)
2.1.2. Moderate Resolution Imaging Spectroradiometer (MODIS)
2.1.3. Aerosol Robotic Network (AERONET)
2.2. Methodology
2.2.1. Cloud and Water Screen Method
2.2.2. Aerosol Optical Properties over East Asia
2.2.3. Red/Blue and SWIR/Red Surface Reflectance Assumptions
2.2.4. Algorithm for AOD Retrieval from Himawari-8/AHI Data
3. Results
3.1. Retrieved AOD from the Proposed Algorithm
3.2. Validation of Retrieved AOD with MODIS
3.3. Validation of Retrieved AOD with AERONET
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Hansen, J.; Sato, M.; Ruedy, R. Radiative forcing and climate response. J. Geophys. Res. 1997, 102, 6831–6864. [Google Scholar] [CrossRef]
- Kaufman, Y.J.; Tanré, D.; Boucher, O. A satellite view of aerosols in the climate system. Nature 2002, 419, 215–223. [Google Scholar] [CrossRef] [PubMed]
- Andreae, M.O.; Jones, C.D.; Cox, P.M. Strong present-day aerosol cooling implies a hot future. Nature 2005, 435, 1187–1190. [Google Scholar] [CrossRef] [PubMed]
- Yu, H.; Kaufman, Y.J.; Chin, M.; Feingold, G.; Remer, L.A.; Anderson, T.L.; Balkanski, Y.; Bellouin, N.; Boucher, O.; Christopher, S.; et al. A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos. Chem. Phys. 2006, 6, 613–666. [Google Scholar] [CrossRef]
- Adler, C.E.; Hirsch Hadorn, G. The IPCC and treatment of uncertainties: Topics and sources of dissensus. Wiley Interdiscip. Rev. Clim. Chang. 2014, 5, 663–676. [Google Scholar] [CrossRef]
- Anderson, T.L. Atmospheric Science: Climate Forcing by Aerosol—A Hazy Picture. Science 2003, 300, 1103–1104. [Google Scholar] [CrossRef] [PubMed]
- Mishchenko, M.I.; Geogdzhayev, I.V.; Cairns, B.; Carlson, B.E.; Chowdhary, J.; Lacis, A.A.; Liu, L.; Rossow, W.B.; Travis, L.D. Past, present, and future of global aerosol climatologies derived from satellite observations: A perspective. J. Quant. Spectrosc. Radiat. Transf. 2007, 106, 325–347. [Google Scholar] [CrossRef]
- Levy, R.C.; Mattoo, S.; Munchak, L.A.; Remer, L.A.; Sayer, A.M.; Patadia, F.; Hsu, N.C. The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech. 2013, 6, 2989–3034. [Google Scholar] [CrossRef]
- Remer, L.A.; Mattoo, S.; Levy, R.C.; Munchak, L.A. MODIS 3 km aerosol product: Algorithm and global perspective. Atmos. Meas. Tech. 2013, 6, 1829–1844. [Google Scholar] [CrossRef]
- Hsu, N.C.; Jeong, M.J.; Bettenhausen, C.; Sayer, A.M.; Hansell, R.; Seftor, C.S.; Huang, J.; Tsay, S.C. Enhanced Deep Blue aerosol retrieval algorithm: The second generation. J. Geophys. Res. Atmos. 2013, 118, 9296–9315. [Google Scholar] [CrossRef]
- Riffler, M.; Popp, C.; Hauser, A.; Fontana, F.; Wunderle, S. Validation of a modified AVHRR aerosol optical depth retrieval algorithm over Central Europe. Atmos. Meas. Tech. 2010, 3, 1255–1270. [Google Scholar] [CrossRef] [Green Version]
- Shi, Y.; Zhang, J.; Reid, J.S.; Liu, B.; Hyer, E.J. Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products. Atmos. Meas. Tech. 2014, 7, 1791–1801. [Google Scholar] [CrossRef] [Green Version]
- Torres, O.; Ahn, C.; Chen, Z. Improvements to the OMI near-UV aerosol algorithm using A-train CALIOP and AIRS observations. Atmos. Meas. Tech. 2013, 6, 3257–3270. [Google Scholar] [CrossRef]
- Zhang, W.; Gu, X.; Xu, H.; Yu, T.; Zheng, F. Assessment of OMI near-UV aerosol optical depth over Central and East Asia. J. Geophys. Res. Atmos. 2016, 121, 382–398. [Google Scholar] [CrossRef]
- Collins, W.D.; Rasch, P.J.; Eaton, B.E.; Khattatov, B.V.; Lamarque, J.-F.; Zender, C.S. Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX. J. Geophys. Res. Atmos. 2001, 106, 7313–7336. [Google Scholar] [CrossRef]
- Yang, J.; Gong, P.; Fu, R.; Zhang, M.; Chen, J.; Liang, S.; Xu, B.; Shi, J.; Dickinson, R. The role of satellite remote sensing in climate change studies. Nat. Clim. Chang. 2013, 3, 1001. [Google Scholar] [CrossRef]
- Wang, J.; Christopher, S.A.; Brechtel, F.; Kim, J.; Schmid, B.; Redemann, J.; Russell, P.B.; Quinn, P.; Holben, B.N. Geostationary satellite retrievals of aerosol optical thickness during ACE-Asia. J. Geophys. Res. 2003, 108, 8657. [Google Scholar] [CrossRef]
- Knapp, K.R. Quantification of aerosol signal in GOES 8 visible imagery over the United States. J. Geophys. Res. 2002, 107, 4426. [Google Scholar] [CrossRef]
- Knapp, K.R.; Frouin, R.; Kondragunta, S.; Prados, A. Toward aerosol optical depth retrievals over land from GOES visible radiances: Determining surface reflectance. Int. J. Remote Sens. 2005, 26, 4097–4116. [Google Scholar] [CrossRef]
- Vermote, E.F.; Tanré, D.; Deuzé, J.L.; Herman, M.; Morcrette, J.J. Second simulation of the satellite signal in the solar spectrum, 6s: An overview. IEEE Trans. Geosci. Remote Sens. 1997, 35, 675–686. [Google Scholar] [CrossRef]
- Mei, L.; Xue, Y.; Wang, Y.; Hou, T.; Guang, J.; Li, Y.; Xu, H.; Wu, C.; He, X.; Dong, J.; et al. Prior information supported aerosol optical depth retrieval using FY2D data. In Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 24–29 July 2011; Volume 3, pp. 2677–2680. [Google Scholar]
- Kim, J.; Yoon, J.-M.; Ahn, M.H.; Sohn, B.J.; Lim, H.S. Retrieving aerosol optical depth using visible and mid-IR channels from geostationary satellite MTSAT-1R. Int. J. Remote Sens. 2008, 29, 6181–6192. [Google Scholar] [CrossRef]
- Zhang, H.; Lyapustin, A.; Wang, Y.; Kondragunta, S.; Laszlo, I.; Ciren, P.; Hoff, R.M. A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States. Atmos. Chem. Phys. 2011, 11, 11977–11991. [Google Scholar] [CrossRef]
- Zhang, Y.; Li, Z.; Zhang, Y.; Hou, W.; Xu, H.; Chen, C.; Ma, Y. High temporal resolution aerosol retrieval using Geostationary Ocean Color Imager: Application and initial validation. J. Appl. Remote Sens. 2014, 8, 83612. [Google Scholar] [CrossRef]
- Bessho, K.; Date, K.; Hayashi, M.; Ikeda, A.; Imai, T.; Inoue, H.; Kumagai, Y.; Miyakawa, T.; Murata, H.; Ohno, T.; et al. An Introduction to Himawari-8/9 Japan’s New-Generation Geostationary Meteorological Satellites. J. Meteorol. Soc. Jpn. Ser. II 2016, 94, 151–183. [Google Scholar] [CrossRef]
- Yumimoto, K.; Nagao, T.M.; Kikuchi, M.; Sekiyama, T.T.; Murakami, H.; Tanaka, T.Y.; Ogi, A.; Irie, H.; Khatri, P.; Okumura, H.; et al. Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite. Geophys. Res. Lett. 2016, 43, 5886–5894. [Google Scholar] [CrossRef]
- Luan, Y.; Jaeglé, L. Composite study of aerosol export events from East Asia and North America. Atmos. Chem. Phys. 2013, 13, 1221–1242. [Google Scholar] [CrossRef] [Green Version]
- Uesawa, D. Aerosol Optical Depth Product Derived from Himawari-8 Data for Asian Dust Monitoring; Meteorological Satellite Center Technical Note; 2016; pp. 59–63. Available online: http://www.data.jma.go.jp/mscweb/technotes/msctechrep61-6.pdf (accessed on 17 January 2018).
- Lee, S.; Han, H.; Im, J.; Jang, E.; Lee, M.-I. Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data. Atmos. Meas. Tech. 2017, 10, 1859–1874. [Google Scholar] [CrossRef]
- Barnes, W.L.; Xiong, X.; Salomonson, V.V. Status of Terra MODIS and Aqua MODIS. Adv. Space Res. 2003, 32, 2099–2106. [Google Scholar] [CrossRef]
- Xiong, X.; Chiang, K.; Esposito, J.; Guenther, B.; Barnes, W. MODIS on-orbit calibration and characterization. Metrologia 2003, 40, S89–S92. [Google Scholar] [CrossRef]
- Holben, B.N.; Tanré, D.; Smirnov, A.; Eck, T.F.; Slutsker, I.; Abuhassan, N.; Newcomb, W.W.; Schafer, J.S.; Chatenet, B.; Lavenu, F.; et al. An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET. J. Geophys. Res. Atmos. 2001, 106, 12067–12097. [Google Scholar] [CrossRef]
- Holben, B.N.; Eck, T.F.; Slutsker, I.; Smirnov, A.; Sinyuk, A.; Schafer, J.; Giles, D.; Dubovik, O. Aeronet’s Version 2.0 quality assurance criteria. Proc. SPIE 2006, 6408, 64080Q. [Google Scholar] [CrossRef]
- Dubovik, O.; Smirnov, A.; Holben, B.N.; King, M.D.; Kaufman, Y.J.; Eck, T.F.; Slutsker, I. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements. J. Geophys. Res. Atmos. 2000, 105, 9791–9806. [Google Scholar] [CrossRef]
- Dubovik, O.; King, M.D. A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements. J. Geophys. Res. Atmos. 2000, 105, 20673–20696. [Google Scholar] [CrossRef]
- Eck, T.F.; Holben, B.N.; Reid, J.S.; Dubovik, O.; Smirnov, A.; O’Neill, N.T.; Slutsker, I.; Kinne, S. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. Atmos. 1999, 104, 31333–31349. [Google Scholar] [CrossRef]
- Imai, T.; Yoshida, R. Algorithm Theoretical Basis for Himawari-8 Cloud Mask Product; Meteorological Satellite Center Technical Note; 2016; pp. 1–17. Available online: http://www.data.jma.go.jp/mscweb/technotes/msctechrep61-1.pdf (accessed on 17 January 2018).
- McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432. [Google Scholar] [CrossRef]
- Omar, A.H.; Won, J.G.; Winker, D.M.; Yoon, S.C.; Dubovik, O.; McCormick, M.P. Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements. J. Geophys. Res. D Atmos. 2005, 110, 1–14. [Google Scholar] [CrossRef]
- Levy, R.C.; Remer, L.A.; Dubovik, O. Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. J. Geophys. Res. Atmos. 2007, 112, D13210. [Google Scholar] [CrossRef]
- Lee, K.H.; Kim, Y.J. Satellite remote sensing of Asian aerosols: A case study of clean, polluted, and Asian dust storm days. Atmos. Meas. Tech. 2010, 3, 1771–1784. [Google Scholar] [CrossRef]
- Zhang, W. Research on the Retrieving of High Temporal Resolution Aerosol Optical Properties from Remote Sensing Data over East Asian. Ph.D Thesis, The University of Chinese Academy of Sciences, Beijing, China, 30 May 2016. [Google Scholar]
- Zhang, W.; Xu, H.; Zheng, F. Classifying Aerosols Based on Fuzzy Clustering and Their Optical and Microphysical Properties Study in Beijing, China. Adv. Meteorol. 2017, 2017, 4197652. [Google Scholar] [CrossRef]
- Kaufman, Y.J.; Wald, A.E.; Remer, L.A.; Gao, B.-C.; Li, R.-R.; Flynn, L. The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Trans. Geosci. Remote Sens. 1997, 35, 1286–1298. [Google Scholar] [CrossRef]
- Kaufman, Y.J.; Gobron, N.; Pinty, B.; Widlowski, J.-L.; Verstraete, M.M. Relationship between surface reflectance in the visible and mid-IR used in MODIS aerosol algorithm-theory. Geophys. Res. Lett. 2002, 29. [Google Scholar] [CrossRef]
- Fröhlich, C.; Shaw, G.E. New determination of Rayleigh scattering in the terrestrial atmosphere. Appl. Opt. 1980, 19, 1773–1775. [Google Scholar] [CrossRef] [PubMed]
- Tao, M.; Chen, L.; Wang, Z.; Tao, J.; Che, H.; Wang, X.; Wang, Y. Comparison and evaluation of the MODIS Collection 6 aerosol data in China. J. Geophys. Res. Atmos. 2015, 120, 6992–7005. [Google Scholar] [CrossRef]
Band | CW 1 (μm) | BW 2 (nm) | Resolution at SSP 3 | Prime Measurement Objectives and Use of Sample Data |
---|---|---|---|---|
1 | 0.455 | 50 | 1.0 km | Daytime aerosol over land, coastal water mapping |
2 | 0.510 | 20 | 1.0 km | Green band—to produce color composite imagery |
3 | 0.645 | 30 | 0.5 km | Daytime vegetation/burn scar and aerosols over water, winds |
4 | 0.860 | 20 | 1.0 km | Daytime cirrus cloud |
5 | 1.61 | 20 | 2.0 km | Daytime cloud-top phase and particle size, snow |
6 | 2.26 | 20 | 2.0 km | Daytime land/cloud properties, particle size, vegetation, snow |
7 | 3.85 | 220 | 2.0 km | Surface and cloud, fog at night, fire, winds |
8 | 6.25 | 370 | 2.0 km | High-level atmospheric water vapor, winds, rainfall |
9 | 6.95 | 120 | 2.0 km | Mid-level atmospheric water vapor, winds, rainfall |
10 | 7.35 | 170 | 2.0 km | Lower-level water vapor, winds and SO2 |
11 | 8.60 | 320 | 2.0 km | Total water for stability, cloud phase, dust, SO2, rainfall |
12 | 9.63 | 180 | 2.0 km | Total ozone, turbulence, winds |
13 | 10.45 | 300 | 2.0 km | Surface and cloud |
14 | 11.20 | 200 | 2.0 km | Imagery, SST, clouds, rainfall |
15 | 12.35 | 300 | 2.0 km | Total water, ash, SST |
16 | 13.30 | 200 | 2.0 km | Air temperature, cloud heights and amounts |
Number | AERONET Sites | Longitude (degree) | Latitude (degree) | Elevation (meter) |
---|---|---|---|---|
1 | Beijing | 116.381 | 39.977 | 92 |
2 | Beijing-CAMS | 116.317 | 39.933 | 106 |
3 | XiangHe | 116.962 | 39.754 | 36 |
4 | Hong_Kong_Sheung | 114.117 | 22.483 | 40 |
5 | Taipei_CWB | 121.5 | 25.03 | 26 |
6 | Chen-Kung_Univ | 120.217 | 23 | 50 |
7 | Hankuk_UFS | 127.266 | 37.339 | 167 |
8 | Yonsei_University | 126.935 | 37.564 | 88 |
9 | Anmyon | 126.33 | 36.539 | 47 |
10 | Shirahama | 135.357 | 33.693 | 10 |
11 | Osaka | 135.591 | 34.651 | 50 |
12 | Noto | 137.137 | 37.334 | 200 |
Aerosol Properties | M1 1 | M2 | M3 | M4 | M5 | M6 | |
---|---|---|---|---|---|---|---|
REFR 2 | 440 nm | 1.42 | 1.46 | 1.40 | 1.47 | 1.50 | 1.48 |
676 nm | 1.42 | 1.46 | 1.42 | 1.50 | 1.52 | 1.51 | |
869 nm | 1.43 | 1.47 | 1.43 | 1.51 | 1.53 | 1.51 | |
1020 nm | 1.42 | 1.46 | 1.44 | 1.51 | 1.53 | 1.50 | |
REFI 3 | 440 nm | 0.0052 | 0.0118 | 0.0079 | 0.0197 | 0.0120 | 0.0064 |
676 nm | 0.0044 | 0.0093 | 0.0061 | 0.0129 | 0.0072 | 0.0034 | |
869 nm | 0.0043 | 0.0094 | 0.0059 | 0.0131 | 0.0069 | 0.0031 | |
1020 nm | 0.0044 | 0.0097 | 0.0058 | 0.0136 | 0.0070 | 0.0031 | |
VolConF 4 (μm3/μm2) | 0.13 | 0.12 | 0.10 | 0.09 | 0.08 | 0.06 | |
EffRadF 5 (μm) | 0.25 | 0.22 | 0.18 | 0.17 | 0.17 | 0.15 | |
StdDevF 6 | 0.55 | 0.55 | 0.46 | 0.50 | 0.48 | 0.51 | |
VolConC (μm3/μm2) | 0.07 | 0.09 | 0.08 | 0.11 | 0.15 | 0.26 | |
EffRadC (μm) | 2.86 | 2.78 | 2.57 | 2.73 | 2.61 | 2.25 | |
StdDevC | 0.59 | 0.59 | 0.64 | 0.64 | 0.65 | 0.61 |
Parameters | Numbers | Values |
---|---|---|
Wavelength | 3 | 0.455, 0.645 and 2.26 μm |
Solar zenith angle | 13 | 0, 6, 12, …, 72 (step 6 deg 1) |
View zenith angle | 13 | 0, 6, 12, …, 72 (step 6 deg) |
Relative azimuth angle | 16 | 0, 12, 24, …, 180.0 (step 12 deg) |
Aerosol model | 6 | M1, M2, M3, M4, M5 and M6 |
AOD 550 nm | 8 | 0, 0.25, 0.5, 0.75, 1.0, 2.0, 3.0 and 5.0 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, W.; Xu, H.; Zheng, F. Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data. Remote Sens. 2018, 10, 137. https://doi.org/10.3390/rs10010137
Zhang W, Xu H, Zheng F. Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data. Remote Sensing. 2018; 10(1):137. https://doi.org/10.3390/rs10010137
Chicago/Turabian StyleZhang, Wenhao, Hui Xu, and Fengjie Zheng. 2018. "Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data" Remote Sensing 10, no. 1: 137. https://doi.org/10.3390/rs10010137