Despite continuous efforts to retrieve aerosol optical depth (AOD) using a conventional 5-channel meteorological imager in geostationary orbit, the accuracy in urban areas has been poorer than other areas primarily due to complex urban surface properties and mixed aerosol types from different emission sources. The two largest error sources in aerosol retrieval have been aerosol type selection and surface reflectance. In selecting the aerosol type from a single visible channel, the season-dependent aerosol optical properties were adopted from long-term measurements of Aerosol Robotic Network (AERONET) sun-photometers. With the aerosol optical properties obtained from the AERONET inversion data, look-up tables were calculated by using a radiative transfer code: the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Surface reflectance was estimated using the clear sky composite method, a widely used technique for geostationary retrievals. Over East Asia, the AOD retrieved from the Meteorological Imager showed good agreement, although the values were affected by cloud contamination errors. However, the conventional retrieval of the AOD over Hong Kong was largely underestimated due to the lack of information on the aerosol type and surface properties. To detect spatial and temporal variation of aerosol type over the area, the critical reflectance method, a technique to retrieve single scattering albedo (SSA), was applied. Additionally, the background aerosol effect was corrected to improve the accuracy of the surface reflectance over Hong Kong. The AOD retrieved from a modified algorithm was compared to the collocated data measured by AERONET in Hong Kong. The comparison showed that the new aerosol type selection using the critical reflectance and the corrected surface reflectance significantly improved the accuracy of AODs in Hong Kong areas, with a correlation coefficient increase from 0.65 to 0.76 and a regression line change from τMI [basic algorithm]=0.41τAERONET+0.16 to τMI [new algorithm]=0.70τAERONET+0.01.
Bibliographical noteFunding Information:
We acknowledge the Korea Meteorological Administration (KMA) for the COMS dataset used in this work. This research was supported by the GEMS program of the Ministry of Environment, Korea and the Eco Innovation Program of KEITI ( 2012000160002 ). This research was partially supported by the Brain Korea 21 Plus for J. Kim, M. Kim.
All Science Journal Classification (ASJC) codes
- Soil Science
- Computers in Earth Sciences