Abstract
Abstract. An aerosol model optimized for northeast Asia is updated with the inversion data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-northeast (NE) Asia campaign which was conducted during spring from March to May 2012. This updated aerosol model was then applied to a single visible channel algorithm to retrieve aerosol optical depth (AOD) from a Meteorological Imager (MI) on-board the geostationary meteorological satellite, Communication, Ocean, and Meteorological Satellite (COMS). This model plays an important role in retrieving accurate AOD from a single visible channel measurement. For the single-channel retrieval, sensitivity tests showed that perturbations by 4 % (0.926 ± 0.04) in the assumed single scattering albedo (SSA) can result in the retrieval error in AOD by over 20 %. Since the measured reflectance at the top of the atmosphere depends on both AOD and SSA, the overestimation of assumed SSA in the aerosol model leads to an underestimation of AOD. Based on the AErosol RObotic NETwork (AERONET) inversion data sets obtained over East Asia before 2011, seasonally analyzed aerosol optical properties (AOPs) were categorized by SSAs at 675 nm of 0.92 ± 0.035 for spring (March, April, and May). After the DRAGON-NE Asia campaign in 2012, the SSA during spring showed a slight increase to 0.93 ± 0.035. In terms of the volume size distribution, the mode radius of coarse particles was increased from 2.08 ± 0.40 to 2.14 ± 0.40. While the original aerosol model consists of volume size distribution and refractive indices obtained before 2011, the new model is constructed by using a total data set after the DRAGON-NE Asia campaign. The large volume of data in high spatial resolution from this intensive campaign can be used to improve the representative aerosol model for East Asia. Accordingly, the new AOD data sets retrieved from a single-channel algorithm, which uses a precalculated look-up table (LUT) with the new aerosol model, show an improved correlation with the measured AOD during the DRAGON-NE Asia campaign. The correlation between the new AOD and AERONET value shows a regression slope of 1.00, while the comparison of the original AOD data retrieved using the original aerosol model shows a slope of 1.08. The change of y-offset is not significant, and the correlation coefficients for the comparisons of the original and new AOD are 0.87 and 0.85, respectively. The tendency of the original aerosol model to overestimate the retrieved AOD is significantly improved by using the SSA values in addition to size distribution and refractive index obtained using the new model.
Highlights
An understanding of global aerosol distribution and its optical characteristics is important, for predictions related to climate change, and for monitoring the effects of changing air quality on human health
The campaign was performed during MAM 2012 to improve our understanding of the aerosol optical properties (AOPs) on a high spatial scale over well-known aerosol source regions where aerosol loading is affected by both desert emissions and industrial pollutants
0.107 0.112 0.122 0.185 0.164 0.053 0.116 0.136 0.167 0.155 0.155 0.143 0.190 0.176 0.186 0.147 0.139 0.157 0.183 0.138 0.111 0.123 0.164 0.104 0.150 0.125 0.194 0.136 0.206 0.197 0.201 0.179 0.128 0.146 0.082 0.197 0.194 0.113 0.217 a minimum reflectance method was applied, and the background AOD (BAOD) was used to correct for the persistent background aerosol levels over East Asia
Summary
An understanding of global aerosol distribution and its optical characteristics is important, for predictions related to climate change, and for monitoring the effects of changing air quality on human health. Since the global aerosol distribution shows high spatial and temporal variability, many studies have developed aerosol retrieval algorithms utilizing both low Earth orbit satellite measurements (Hsu et al, 2004; Kim et al, 2007; Torres et al, 2007; Kahn et al, 2010; Lyapustin et al, 2011b; von HoyningenHuene et al, 2011; Wong et al, 2010; Bevan et al, 2012; Sayer et al, 2012; Levy et al, 2013) and geostationary orbit (GEO) satellite measurements (Knapp et al, 2002, 2005; Wang et al, 2003; Urm and Sohn, 2005; Yoon et al, 2007; Kim et al, 2008; Lee et al, 2010; Zhang et al, 2011; Kim et al, 2014) These studies have typically adopted an inversion approach, using a precalculated look-up table (LUT) based on assumed aerosol optical properties (AOPs) to retrieve aerosol information from the measured visible reflectance at the top of the atmosphere.
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