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)

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 0.87Published by Copernicus Publications on behalf of the European Geosciences Union. M. Kim et al.: AOD retrieval using GEO measurement during DRAGON-NE Asia 2012and 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.

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

“…Aerosol properties have been observed extensively by a number of LEO satellite instruments, including Moderate Resolution Imaging Spectroradiometer (MODIS; Levy et al 2013) and Visible Infrared Imaging Radiometer Suite (VIIRS; Jackson et al 2013). High-temporal-and high-spatial-resolution observations of aerosol properties have been available from geostationary Earth orbit (GEO) instruments: the Meteorological Imager (MI) and the Geostationary Ocean Color Imager (GOCI) on board the Geostationary Korea Multi-Purpose Satellite (GK)-1, also known as Communication, Oceanography and Meteorology Satellite (COMS), and, more recently, from the AHI over Asia (Kim et al 2008;Kim et al 2016;Choi et al 2016;Choi and Ho 2015;Lim et al 2018). Long-term validation of the GOCI aerosol optical depth (AOD) indicates good agreement with ground-based Aerosol Robotic Network (AERONET) measurements, with correlation coefficients of ~0.9 (M. Choi et al 2018).…”

An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

“…They used the MD DISCOVER-AQ airborne high spectral-resolution lidar and MD DRAGON data sets to assess the fidelity of the 3 km AOD product, finding improvement over the coarseresolution product but some additional variability due to the complexity of urban cover types. Kim et al (2016) used the DRAGON-NE Asia networks to refine the single scattering input to a single channel AOD retrieval model used with the GEO COMS Meteorological Imager (MI). They note that the surface-based inputs from DRAGON significantly improved the model to predict AOD, thereby reducing previous overestimates.…”

AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products

“…Land surface reflectance was estimated from SWIR in addition to existing Minimum Reflectance Method (MRM), and ocean reflectance was from Fresnel equations with the consideration of chlorophyll-a concentration, as discussed in the next section. Here, AOPs were retrieved for February, May, August, and November to represent each season in 2016 using the YAER algorithm [22][23][24], and AOP results were compared with those calculated from AERONET sun-photometer data. The YAER algorithm should be preceded by cloud masking and surface reflectance estimation.…”