Articles | Volume 14, issue 5
https://doi.org/10.5194/amt-14-3449-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-3449-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
12 May 2021
Research article | Highlight paper |
| 12 May 2021
| 12 May 2021
A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading
NASA Goddard Space Flight Center, Greenbelt, MD, USA
UMBC/JCET, Baltimore, MD, USA
Robert C. Levy
NASA Goddard Space Flight Center, Greenbelt, MD, USA
School of Surveying and Land Information Engineering, Henan
Polytechnic University, Jiaozuo 454003, China
Lorraine A. Remer
UMBC/JCET, Baltimore, MD, USA
Shana Mattoo
NASA Goddard Space Flight Center, Greenbelt, MD, USA
SSAI, Lanham, MD, USA
Oleg Dubovik
French National Centre for Scientific Research, Unviersity of Lille, Lille, France
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Cited articles
AERONET team: AERONET direct sun measurements and inversion product, available at: https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_aod.html, last access: 8 May 2021.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P.,
Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A. L., and Dufresne, J. L.: Bounding global aerosol radiative forcing of climate
change, Rev. Geophys., 58, 2019RG000660, https://doi.org/10.1029/2019RG000660, 2020.
Bi, J., Belle, J. H., Wang, Y., Lyapustin, A. I., Wildani, A., and Liu, Y.:
Impacts of snow and cloud covers on satellite-derived PM2.5 levels. Remote Sens. Environ., 221, 665–674,
https://doi.org/10.1016/j.rse.2018.12.002, 2019.
Bilal, M. and Nichol, J. E.: Evaluation of MODIS aerosol retrieval algorithms
over the Beijing-Tianjin-Hebei region during low to very high pollution
events, J. Geophys. Res.-Atmos., 120, 7941–7957,
https://doi.org/10.1002/2015JD023082, 2015.
Bilal, M., Nichol, J. E., and Chan, P. W.: Validation and accuracy assessment
of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low
and high aerosol loadings and dust storms, Remote Sens. Environ.,
153, 50–60, https://doi.org/10.1016/j.rse.2014.07.015, 2014.
Chen, H. and Wang, H.: Haze days in North China and the associated
atmospheric circulations based on daily visibility data from 1960 to
2012, J. Geophys. Res.-Atmos., 120, 5895–5909,
https://doi.org/10.1002/2015JD023225, 2015.
Chen, Y., Jin, G. Z., Kumar, N., and Shi, G.: The promise of Beijing:
Evaluating the impact of the 2008 Olympic Games on air quality, J.
Environ. Econ. Manag., 66, 424–443, https://doi.org/10.1016/j.jeem.2013.06.005, 2013.
Chen, W., Fan, A., and Yan, L.: Performance of MODIS C6 aerosol product during frequent haze-fog events: A case study of Beijing, Remote Sensing,
9, 496, https://doi.org/10.3390/rs9050496, 2017.
China Meteorological Administration: Observation and forecasting levels of
haze. The State Standard of the People's Republic of China QX/T 113-2010, China Meteorological Press, Beijing, China, 2010 (in Chinese).
Christopher, S. A. and Zhang, J.: Shortwave aerosol radiative forcing from MODIS and CERES observations over the oceans, Geophys. Res. Lett., 29, 6-1–6-4
https://doi.org/10.1029/2002GL014803, 2002.
Costantino, L. and Bréon, F.-M.: Aerosol indirect effect on warm clouds over South-East Atlantic, from co-located MODIS and CALIPSO observations, Atmos. Chem. Phys., 13, 69–88, https://doi.org/10.5194/acp-13-69-2013, 2013.
Dave, J. V. and Mateer, C.L.: A preliminary study on the possibility of
estimating total atmospheric ozone from satellite measurements, J. Atmos.
Sci., 24, 414–427, https://doi.org/10.1175/1520-0469(1967)024<0414:APSOTP>2.0.CO;2, 1967.
Ding, Y. and Liu, Y.: Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity, Sci. China
Earth Sci., 57, 36–46, https://doi.org/10.1007/s11430-013-4792-1, 2014.
Dubovik, O. and King, M. D.: A flexible inversion algorithm for retrieval of
aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res.-Atmos., 105, 20673–20696, https://doi.org/10.1029/2000JD900282, 2000.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D.,
Tanré, D., and Slutsker, I.: Variability of absorption and optical
properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002.
Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B. N., Mishchenko, M., Yang,
P., Eck, T. F., Volten, H., Munoz, O., Veihelmann, B., and Van der Zande,
W. J.: Application of spheroid models to account for aerosol particle
nonsphericity in remote sensing of desert dust, J. Geophys. Res.-Atmos., 111, D11208, https://doi.org/10.1029/2005JD006619, 2006.
Eck, T.: Interactive comment on “A Dark Target researchaerosol algorithm for MODIS observations overeastern China: Increasing coverage whilemaintaining accuracy at high aerosol loading” by Yingxi R. Shi et al., Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-450-SC3, 2021.
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'neill,
N. T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical depth
of biomass burning, urban, and desert dust aerosols, J. Geophys. Res.-Atmos., 104, 31333–31349, https://doi.org/10.1029/1999JD900923,
1999.
Eck, T. F., Holben, B. N., Reid, J. S., Xian, P., Giles, D. M., Sinyuk, A.,
Smirnov, A., Schafer, J. S., Slutsker, I., Kim, J., and Koo, J. H.:
Observations of the interaction and transport of fine mode aerosols with
cloud and/or fog in Northeast Asia from Aerosol Robotic Network and
satellite remote sensing, J. Geophys. Res.-Atmos.,
123, 5560–5587, https://doi.org/10.1029/2018JD028313, 2018.
Eck, T. F., Holben, B. N., Giles, D. M., Slutsker, I., Sinyuk, A., Schafer,
J. S., Smirnov, A., Sorokin, M., Reid, J. S., Sayer, A. M., and Hsu, N. C.:
AERONET remotely sensed measurements and retrievals of biomass burning
aerosol optical properties during the 2015 Indonesian burning season, J. Geophys. Res.-Atmos., 124, 4722–4740, https://doi.org/10.1029/2018JD030182, 2019.
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Holben, B. N., Eck, T. F., Slutsker, I. A., Tanre, D., Buis, J. P., Setzer, A.,
Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., and Lavenu, F.:
AERONET – A federated instrument network and data archive for aerosol
characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
Holben, B. N., Eck, T. F., Slutsker, I., Smirnov, A., Sinyuk, A., Schafer, J.,
Giles, D., and Dubovik, O.: December. AERONET's version 2.0 quality assurance
criteria, in: Remote Sensing of the Atmosphere and Clouds, 6408,
64080Q, https://doi.org/10.1117/12.706524, 2006.
Hsu, N. C., Tsay, S. C., King, M. D., and Herman, J. R.: Deep blue retrievals of
Asian aerosol properties during ACE-Asia, IEEE T. Geosci. Remote, 44, 3180–3195, https://doi.org/10.1109/TGRS.2006.879540, 2006.
Hsu, N. C., Lee, J., Sayer, A. M., Kim, W., Bettenhausen, C., and Tsay, S. C.:
VIIRS Deep blue aerosol products over land: Extending the EOS long-term
aerosol data records, J. Geophys. Res.-Atmos., 124,
4026–4053, https://doi.org/10.1029/2018JD029688, 2019.
Huang, R. J., Zhang, Y., Bozzetti, C., Ho, K. F., Cao, J. J., Han, Y.,
Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., and Zotter, P.:
High secondary aerosol contribution to particulate pollution during haze
events in China, Nature, 514, 218–222, https://doi.org/10.1038/nature13774, 2014.
Ichoku, C., Remer, L. A., Kaufman, Y. J., Levy, R., Chu, D. A., Tanré, D., and Holben, B. N.: MODIS observation of aerosols and estimation of aerosol
radiative forcing over southern Africa during SAFARI 2000, J. Geophys. Res.-Atmos., 108, 8499, https://doi.org/10.1029/2002JD002366, 2003.
Lee, J., Hsu, N. C., Sayer, A. M., Bettenhausen, C., and Yang, P.:
AERONET-Based Nonspherical Dust Optical Models and Effects on the VIIRS Deep
Blue/SOAR Over Water Aerosol Product, J. Geophys. Res.-Atmos., 122, 10384–10401, https://doi.org/10.1002/2017JD027258,
2017.
Levy, R. C. and Hsu, C.: MODIS Atmosphere L2 Aerosol Product, NASA MODIS
Adaptive Processing System, Goddard Space Flight Center, USA [data set], https://doi.org/10.5067/MODIS/MOD04_L2.006, 2015.
Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.:
Second-generation operational algorithm: Retrieval of aerosol properties
over land from inversion of Moderate Resolution Imaging Spectroradiometer
spectral reflectance, J. Geophys. Res.-Atmos., 112, D13211,
https://doi.org/10.1029/2006JD007811, 2007a.
Levy, R. C., Remer, L. A., and Dubovik, O.: Global aerosol optical properties
and application to Moderate Resolution Imaging Spectroradiometer aerosol
retrieval over land, J. Geophys. Res.-Atmos., 112, D13210, https://doi.org/10.1029/2006JD007815, 2007b.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Li, F., Vogelmann, A. M., and Ramanathan, V.: Saharan dust aerosol radiative
forcing measured from space, J. Climate, 17, 2558–2571,
https://doi.org/10.1175/1520-0442(2004)017<2558:SDARFM>2.0.CO;2, 2004.
Li, M., Tang, G. Q., Huang, J., Liu, Z. R., An, J. L., and Wang, Y. S.:
Characteristics of winter atmospheric mixing layer height in
Beijing-Tianjin-Hebei region and their relationship with the atmospheric
pollution, Huan jing ke xue = Huanjing kexue, 36, 1935–1943, https://doi.org/10.13227/j.hjkx.2015.06.004, 2015.
Li, R. R., Remer, L., Kaufman, Y. J., Mattoo, S., Gao, B. C., and Vermote, E.:
Snow and ice mask for the MODIS aerosol products, IEEE Geosci. Remote
S., 2, 306–310, https://doi.org/10.1109/LGRS.2005.847755, 2005.
Li, S., Chen, L., Xiong, X., Tao, J., Su, L., Han, D., and Liu, Y.: Retrieval
of the haze optical thickness in North China Plain using MODIS data, IEEE
T. Geosci. Remote, 51, 2528–2540, https://doi.org/10.1109/TGRS.2012.2214038, 2012.
Liu, J., Huang, J., Chen, B., Zhou, T., Yan, H., Jin, H., Huang, Z., and
Zhang, B.: Comparisons of PBL heights derived from CALIPSO and ECMWF
reanalysis data over China, J. Quant. Spectrosc.
Rad., 153, 102–112, https://doi.org/10.1016/j.jqsrt.2014.10.011, 2015.
Luan, T., Guo, X., Guo, L., and Zhang, T.: Quantifying the relationship between PM2.5 concentration, visibility and planetary boundary layer height for long-lasting haze and fog–haze mixed events in Beijing, Atmos. Chem. Phys., 18, 203–225, https://doi.org/10.5194/acp-18-203-2018, 2018.
Luo, Y., Zheng, X., Zhao, T., and Chen, J.: A climatology of aerosol optical
depth over China from recent 10 years of MODIS remote sensing data,
Int. J. Climatol., 34, 863–870, https://doi.org/10.1002/joc.3728,
2014.
Lyapustin, A., Wang, Y., Xiong, X., Meister, G., Platnick, S., Levy, R., Franz, B., Korkin, S., Hilker, T., Tucker, J., Hall, F., Sellers, P., Wu, A., and Angal, A.: Scientific impact of MODIS C5 calibration degradation and C6+ improvements, Atmos. Meas. Tech., 7, 4353–4365, https://doi.org/10.5194/amt-7-4353-2014, 2014.
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018.
Mhawish, A., Banerjee, T., Sorek-Hamer, M., Lyapustin, A., Broday, D. M., and
Chatfield, R.: Comparison and evaluation of MODIS Multi-angle Implementation
of Atmospheric Correction (MAIAC) aerosol product over South Asia, Remote
Sens. Environ., 224, 12–28, https://doi.org/10.1016/j.rse.2019.01.033, 2019.
Miao, Y., Guo, J., Liu, S., Liu, H., Li, Z., Zhang, W., and Zhai, P.: Classification of summertime synoptic patterns in Beijing and their associations with boundary layer structure affecting aerosol pollution, Atmos. Chem. Phys., 17, 3097–3110, https://doi.org/10.5194/acp-17-3097-2017, 2017.
MODIS Characterization Support Team (MCST): MODIS 1km Calibrated Radiances Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA [data set], https://doi.org/10.5067/MODIS/MYD021KM.061, 2017.
Petäjä, T., Järvi, L., Kerminen, V. M., Ding, A. J., Sun, J. N.,
Nie, W., Kujansuu, J., Virkkula, A., Yang, X., Fu, C. B., and Zilitinkevich,
S.: Enhanced air pollution via aerosol-boundary layer feedback in China,
Sci. Rep., 6, 18998, https://doi.org/10.1038/srep18998, 2016.
Quaas, J., Boucher, O., Bellouin, N., and Kinne, S.: Satellite-based estimate
of the direct and indirect aerosol climate forcing, J. Geophys. Res.-Atmos., 113, D05204, https://doi.org/10.1029/2007JD008962, 2008.
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins,
J. V., Li, R. R., Ichoku, C., Levy, R. C., Kleidman, R. G., and Eck, T. F.: The
MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62, 947–973, https://doi.org/10.1175/JAS3385.1, 2005.
Remer, L. A., Levy, R. C., Mattoo, S., Tanré, D., Gupta, P., Shi, Y.,
Sawyer, V., Munchak, L. A., Zhou, Y., Kim, M., Ichoku, C., Patadia, F., Li,
R.-R., Gassó, S., Kleidman, R. G., and Holben, B. N.: The Dark Target Algorithm
for Observing the Global Aerosol System: Past, Present, and Future, Remote
Sens., 12, 2900, https://doi.org/10.3390/rs12182900, 2020.
Renhe, Z., Li, Q., and Zhang, R.: Meteorological conditions for the
persistent severe fog and haze event over eastern China in January
2013, Science China Earth Sciences, 57, 26–35, https://doi.org/10.1007/s11430-013-4774-3, 2014.
Sayer, A. M., Hsu, N. C., Bettenhausen, C., and Jeong, M. J.: Validation and
uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data, J. Geophys. Res.-Atmos., 118, 7864–7872, https://doi.org/10.1002/jgrd.50600, 2013.
Shi, Y., Zhang, J., Reid, J. S., Holben, B., Hyer, E. J., and Curtis, C.: An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation, Atmos. Chem. Phys., 11, 557–565, https://doi.org/10.5194/acp-11-557-2011, 2011.
Shi, Y., Zhang, J., Reid, J. S., Hyer, E. J., and Hsu, N. C.: Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa, Atmos. Meas. Tech., 6, 949–969, https://doi.org/10.5194/amt-6-949-2013, 2013.
Shi, Y. R., Levy, R. C., Eck, T. F., Fisher, B., Mattoo, S., Remer, L. A., Slutsker, I., and Zhang, J.: Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals, Atmos. Chem. Phys., 19, 259–274, https://doi.org/10.5194/acp-19-259-2019, 2019.
Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., Schafer, J. S., Smirnov, A., Sorokin, M., and Lyapustin, A.: The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2, Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, 2020.
Stackpole, J. D.: The WMO format for the storage of weather product
information and the exchange of weather product messages in gridded binary
form, US Department of Commerce, National Oceanic and Atmospheric
Administration, National Weather Service, National Meteorological Center, Washington, D.C., USA, 1994.
Tang, G., Zhu, X., Hu, B., Xin, J., Wang, L., Münkel, C., Mao, G., and Wang, Y.: Impact of emission controls on air quality in Beijing during APEC 2014: lidar ceilometer observations, Atmos. Chem. Phys., 15, 12667–12680, https://doi.org/10.5194/acp-15-12667-2015, 2015.
Tao, M., Chen, L., Wang, Z., Tao, J., Che, H., Wang, X., and Wang, Y.:
Comparison and evaluation of the MODIS Collection 6 aerosol data in China, J. Geophys. Res.-Atmos., 120, 6992–7005, https://doi.org/10.1002/2015JD023360, 2015.
Torres, O., Jethva, H., and Bhartia, P. K.: Retrieval of aerosol optical depth above clouds from OMI observations: Sensitivity analysis and case studies, J. Atmos. Sci., 69, 1037–1053, https://doi.org/10.1175/JAS-D-11-0130.1, 2012.
Tucker, C. J.: Red and Photographic Infrared Linear Combinations for
Monitoring Vegetation, Rem. Sens. Environ, 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
Wei, J., Li, Z., Peng, Y., and Sun, L.: MODIS Collection 6.1 aerosol optical
depth products over land and ocean: validation and
comparison, Atmos. Environ., 201, 428–440, https://doi.org/10.1016/j.atmosenv.2018.12.004, 2019.
World Health Organization: Air quality guidelines: global update 2005:
particulate matter, ozone, nitrogen dioxide, and sulfur dioxide, World
Health Organization, available at: https://apps.who.int/iris/handle/10665/69477 (last access: 8 May 2021), 2006.
Yan, X., Shi, W., Luo, N., and Zhao, W.: A new method of satellite-based haze
aerosol monitoring over the North China Plain and a comparison with MODIS
Collection 6 aerosol products, Atmos. Res., 171, 31–40, https://doi.org/10.1016/j.atmosres.2015.12.003, 2016.
Yang, L., Hu, X., Wang, Ha., He, X., Liu, P., Xu, Na., Yang, Z., and Zhang,
P.: Preliminary test of quantitative capability in aerosol retrieval over
land from MERSI-II onboard Fengyun-3D, National Remote Sensing Bulletin,
Published Online, https://doi.org/10.11834/jrs.20200286,
2020.
Yu, H., Dickinson, R. E., Chin, M., Kaufman, Y. J., Zhou, M., Zhou, L., Tian,
Y., Dubovik, O., and Holben, B. N.: Direct radiative effect of aerosols as
determined from a combination of MODIS retrievals and GOCART simulations, J. Geophys. Res.-Atmos., 109, D03206, https://doi.org/10.1029/2003JD003914, 2004.
Zhang, J. and Reid, J. S.: MODIS aerosol product analysis for data
assimilation: Assessment of over-ocean level 2 aerosol optical thickness
retrievals, J. Geophys. Res.-Atmos., 111, D22207,
https://doi.org/10.1029/2005JD006898, 2006.
Zhang, K., O'Donnell, D., Kazil, J., Stier, P., Kinne, S., Lohmann, U., Ferrachat, S., Croft, B., Quaas, J., Wan, H., Rast, S., and Feichter, J.: The global aerosol-climate model ECHAM-HAM, version 2: sensitivity to improvements in process representations, Atmos. Chem. Phys., 12, 8911–8949, https://doi.org/10.5194/acp-12-8911-2012, 2012.
Zhang, Q., He, K., and Huo, H.: Cleaning China's air, Nature, 484, 161–162, https://doi.org/10.1038/484161a, 2012.
Zhang, X., Wang, H., Che, H. Z., Tan, S. C., Shi, G. Y., Yao, X. P., and Zhao, H. J.: Improvement of
snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in
East China, Atmos. Res., 30, 105063, https://doi.org/10.1016/j.atmosres.2020.105063, 2020.
Zhao, X. J., Zhao, P. S., Xu, J., Meng,, W., Pu, W. W., Dong, F., He, D., and Shi, Q. F.: Analysis of a winter regional haze event and its formation mechanism in the North China Plain, Atmos. Chem. Phys., 13, 5685–5696, https://doi.org/10.5194/acp-13-5685-2013, 2013.
Short summary
Due to fast industrialization and development, China has been experiencing haze pollution episodes with both high frequencies and severity over the last 3 decades. This study improves the accuracy and data coverage of measured aerosol from satellites, which help quantify, characterize, and understand the impact of the haze phenomena over the entire East Asia region.
Due to fast industrialization and development, China has been experiencing haze pollution...
