Integration of GOCI, AHI and GEMS Yonsei aerosol products

Abstract:

With the importance of aerosols in Earth’s climate, cloud microphysics, and air quality, aerosol optical properties have been observed extensively from many satellites. Diverse algorithms have been implemented with the use of different sophisticated techniques. The Yonsei AErosol Retrieval (YAER) algorithm can retrieve aerosol properties only over dark surfaces, so it is important to mask pixels with bright surfaces. In contrast to GOCI, AHI is equipped with 3 shortwave and 9 IR channels, which is advantageous for bright pixel masking. In addition, multiple visible and near-IR channels provide a great advantage in aerosol property retrieval from GOCI and AHI. Also, by retrieving the aerosol optical properties through the YAER algorithm at 10-minute of AHI or 1-hour interval of GOCI in 6 km x 6 km resolution, we can observe diurnal variations and transport of aerosols, which has not been possible from LEO satellites. On the other hand, GEMS is under in-orbit test after launch, so data fusion was performed using TROPOMI as proxy data.

This study attempts to estimate the optimal AOD for East Asia by considering the satellite retrieval uncertainty. The first step of the fusion is to analyze the AERONET data and the error characteristics of each retrieved result, and perform bias correction according to the normalized vegetation indexes. The bias correction is based on the assumption that the different AOD products have normal distributions, and bias was corrected through the Gaussian fitting. After the bias was corrected, the fused product was produced using an ensemble average and maximum likelihood estimation method. These fused results were a combination of retrieved AODs, all of which have a higher % within expected error than the respective aerosol product from each satellite instrument.