Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign

Abstract. Recently launched multi-channel geostationary-Earth-orbit (GEO) satellite sensors such as the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI) provide aerosol products over East Asia with high accuracy, which enables the monitoring of rapid diurnal variations and the transboundary transport of aerosols. Most aerosol studies to date have used low-Earth-orbit (LEO) satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR) with a maximum of one or two overpass daylight times per day at mid- to low latitudes. Thus, the demand for new GEO observations with high temporal resolution and improved accuracy has been significant. In this study the aerosol optical depth (AOD) products from three LEO sensors – MODIS, MISR, and the Visible/Infrared Imager Radiometer Suite (VIIRS) – along with two GEO sensors – GOCI and AHI – are validated, compared and integrated for the period during the Korea–United Sates Air Quality Study (KORUS-AQ) field campaign from 1 May to 12 June 2016 over East Asia. The AOD products analyzed here generally have high accuracy, but their error characteristics differ according to the use of several different surface-reflectance estimation methods plus differences in cloud screening. High-accuracy near-real-time GOCI and AHI measurements facilitate the detection of rapid AOD changes, such as smoke aerosol transport from Russia to Japan on 18–21 May 2016, heavy pollution transport from China to Korea on 25 May 2016, and local emission transport from the Seoul Metropolitan Area to the Yellow Sea in Korea on 5 June 2016. These high-temporal-resolution GEO measurements result in more-representative daily AOD values and make a greater contribution to a combined daily AOD product assembled by median-value selection with a 0.5° × 0.5° grid resolution. The combined AOD is more spatially continuous and of higher accuracy than the individual products. This study characterizes aerosol measurements from LEO and GEO satellites currently in operation over East Asia, and results presented here can be used to evaluate satellite measurement bias and air-quality models.

[1]  Dan Chen,et al.  Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China , 2018 .

[2]  N. C. Hsu,et al.  Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations , 2015 .

[3]  Young Sung Ghim,et al.  GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia , 2015 .

[4]  Zhengqiang Li,et al.  Comprehensive Study of Optical, Physical, Chemical, and Radiative Properties of Total Columnar Atmospheric Aerosols over China: An Overview of Sun-Sky Radiometer Observation Network (SONET) Measurements , 2017 .

[5]  Yujie Wang,et al.  Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables , 2011 .

[6]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[7]  Jay R. Herman,et al.  Earth surface reflectivity climatology at 340–380 nm from TOMS data , 1997 .

[8]  Toshihiko Takemura,et al.  Consistency of the aerosol type classification from satellite remote sensing during the Atmospheric Brown Cloud–East Asia Regional Experiment campaign , 2007 .

[9]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .

[10]  Lorraine A. Remer,et al.  A surface reflectance scheme for retrieving aerosol optical depth over urbansurfaces in MODIS Dark Target retrieval algorithm , 2016 .

[11]  Bing Xu,et al.  Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison , 2019, Remote. Sens..

[12]  Zhengqiang Li,et al.  GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia , 2018 .

[13]  C. Cox Statistics of the sea surface derived from sun glitter , 1954 .

[14]  Hiroshi Murakami,et al.  Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[15]  B. Holben,et al.  MODIS 3 km aerosol product: applications over land in an urban/suburban region , 2013 .

[16]  Alexander Smirnov,et al.  SeaWiFS Ocean Aerosol Retrieval (SOAR): Algorithm, validation, and comparison with other data sets , 2012 .

[17]  Michael J. Garay,et al.  New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water , 2017 .

[18]  N. C. Hsu,et al.  Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements , 2017, Journal of geophysical research. Atmospheres : JGR.

[19]  Michael Eisinger,et al.  Refinement of a Database of Spectral Surface Reflectivity in the Range 335-772 nm Derived from 5.5 Years of GOME Observations , 2003 .

[20]  Dong L. Wu,et al.  Improvement of aerosol optical depth retrieval over Hong Kong from a geostationary meteorological satellite using critical reflectance with background optical depth correction , 2014 .

[21]  Jae Hwa Lee,et al.  Retrieval of Aerosol Optical Depth over East Asia from a Geostationary Satellite, MTSAT-1R , 2007 .

[22]  Jhoon Kim,et al.  AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products , 2018, Remote. Sens..

[23]  Andrew M. Sayer,et al.  Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data , 2013 .

[24]  Yujie Wang,et al.  Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm , 2011 .

[25]  Ukkyo Jeong,et al.  An optimal-estimation-based aerosol retrieval algorithm using OMI near-UV observations , 2016 .

[26]  T. Eck,et al.  Characteristics of Classified Aerosol Types in South Korea during the MAPS-Seoul Campaign , 2018 .

[27]  N. Christina Hsu,et al.  Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land , 2019, Journal of Geophysical Research: Atmospheres.

[28]  Yoram J. Kaufman,et al.  Aerosol optical depth retrieval from GOES-8: Uncertainty study and retrieval validation over South America , 2002 .

[29]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[30]  Teruyuki Nakajima,et al.  Overview of the Atmospheric Brown Cloud East Asian Regional Experiment 2005 and a study of the aerosol direct radiative forcing in east Asia , 2007 .

[31]  E. Vermote,et al.  The MODIS Aerosol Algorithm, Products, and Validation , 2005 .

[32]  Qingyang Xiao,et al.  Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia , 2015 .

[33]  Paul Ingmann,et al.  Requirements for the GMES Atmosphere Service and ESA's implementation concept: Sentinels-4/-5 and -5p , 2012 .

[34]  Fengjie Zheng,et al.  Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data , 2018, Remote. Sens..

[35]  Lorraine A. Remer,et al.  Suomi‐NPP VIIRS aerosol algorithms and data products , 2013 .

[36]  Teruyuki Nakajima,et al.  Development of a Two-Channel Aerosol Retrieval Algorithm on a Global Scale Using NOAA AVHRR , 1999 .

[37]  M. Garay,et al.  Development and Assessment of a High Spatial Resolution (4.4 km) MISR Aerosol Product Using AERONET-DRAGON Data , 2016 .

[38]  Yujie Wang,et al.  Exploring systematic offsets between aerosol products from the two MODIS sensors. , 2018, Atmospheric measurement techniques.

[39]  G. Carmichael,et al.  Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area. , 2015, The Science of the total environment.

[40]  Michael J. Garay,et al.  Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data , 2017 .

[41]  S. Solomon The Physical Science Basis : Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[42]  Alexei Lyapustin,et al.  MODIS Collection 6 MAIAC algorithm , 2018, Atmospheric Measurement Techniques.

[43]  Michael J. Garay,et al.  Decadal-scale trends in regional aerosol particle properties and their linkage to emission changes , 2017 .

[44]  Bernard Pinty,et al.  Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview , 1998, IEEE Trans. Geosci. Remote. Sens..

[45]  J. Burrows,et al.  Changes in atmospheric aerosol loading retrieved from space-based measurements during the past decade , 2013 .

[46]  Yafang Cheng,et al.  Assimilation of next generation geostationary aerosol optical depth retrievals to improve air quality simulations , 2014 .

[47]  Jin Xing,et al.  Using the Gaofen-4 geostationary satellite to retrieve aerosols with high spatiotemporal resolution , 2018, Journal of Applied Remote Sensing.

[48]  R. Park,et al.  Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea , 2018, Atmospheric Chemistry and Physics.

[49]  T. Eck,et al.  Analysis of long-range transboundary transport (LRTT) effect on Korean aerosol pollution during the KORUS-AQ campaign , 2019, Atmospheric Environment.

[50]  E. Vermote,et al.  Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer , 1997 .

[51]  Thomas F. Eck,et al.  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) , 2016 .

[52]  John C. Gille,et al.  Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission: Design, execution, and first results , 2003 .

[53]  Brent N. Holben,et al.  Validation and expected error estimation of Suomi‐NPP VIIRS aerosol optical thickness and Ångström exponent with AERONET , 2016 .

[54]  Philip B. Russell,et al.  Geostationary satellite retrievals of aerosol optical thickness during ACE‐Asia , 2003 .

[55]  H. S. Lim,et al.  Retrieving aerosol optical depth using visible and mid‐IR channels from geostationary satellite MTSAT‐1R , 2008 .

[56]  Robert C. Levy,et al.  MODIS Collection 6 aerosol products: Comparison between Aqua's e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations , 2014 .

[57]  J. Reid,et al.  An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals , 2010 .

[58]  T. Eck,et al.  Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols , 1999 .

[59]  P. Bhartia,et al.  Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation , 1998 .

[60]  Jasper R. Lewis,et al.  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 , 2019, Atmospheric Measurement Techniques.

[61]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[62]  Thomas F. Eck,et al.  GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign , 2015 .

[63]  Teruyuki Nakajima,et al.  Aerosol model evaluation using two geostationary satellites over East Asia in May 2016 , 2019, Atmospheric Research.

[64]  J. Ryu,et al.  Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager , 2010 .

[65]  P. Yang,et al.  Radiative and Microphysical Properties of Cirrus Cloud Inferred from Infrared Measurements Made by the Moderate Resolution Imaging Spectroradiometer (MODIS). Part I: Retrieval Method , 2014 .

[66]  Thomas F. Eck,et al.  New approach to monitor transboundary particulate pollution over Northeast Asia , 2013 .

[67]  Hongqing Liu,et al.  An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database , 2016 .

[68]  Jassim A. Al-Saadi,et al.  Tropospheric Emissions: Monitoring of Pollution (TEMPO) , 2014 .

[69]  Alan D. Lopez,et al.  A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.

[70]  P Wang,et al.  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 , 2018, Journal of geophysical research. Atmospheres : JGR.

[71]  Soon-Chang Yoon,et al.  Seasonal and monthly variations of columnar aerosol optical properties over East Asia determined from multi-year MODIS, LIDAR, and AERONET Sun/sky radiometer measurements , 2007 .

[72]  Meng Gao,et al.  Diurnal variation of aerosol optical depth and PM2.5 in South Korea: a synthesis from AERONET, satellite (GOCI), KORUS-AQ observation, and the WRF-Chem model , 2018, Atmospheric Chemistry and Physics.

[73]  Manu Mehta,et al.  Recent global aerosol optical depth variations and trends — A comparative study using MODIS and MISR level 3 datasets , 2016 .

[74]  Estimation of Aerosol Optical Thickness over East Asia Using GMS-5 Visible Channel Measurements , 2005 .

[75]  Alexei Lyapustin,et al.  MODIS Collection 6 MAIAC algorithm , 2018, Atmospheric Measurement Techniques.

[76]  L. Emmons,et al.  Preface to a special issue "Megacity Air Pollution Studies (MAPS)" , 2018 .

[77]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[78]  P. Percell,et al.  Computationally efficient air quality forecasting tool: implementation ofSTOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust , 2016 .

[79]  Jeffrey S. Reid,et al.  MODIS aerosol product analysis for data assimilation: Assessment of over‐ocean level 2 aerosol optical thickness retrievals , 2006 .

[80]  Woogyung V. Kim,et al.  An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks , 2017 .