Integration of GOCI and AHI Yonsei Aerosol Optical Depth Products During the 2016 KORUS-AQ and 2018 EMeRGe Campaigns

Abstract

Abstract. The Yonsei AErosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 minutes AHI or 1 hour GOCI data at 6 km × 6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), and bias correction based on normalized difference vegetation indexes; and (2) estimation of the fused product using ensemble-mean and maximum-likelihood estimation methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error, median bias error than the retrieved result for each product.

Interpretation of geostationary satellite aerosol optical depth (AOD) over East Asia in relation to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and seasonality

“…and is biased high on the Yellow Sea islands, resulting in an overall -10% bias, consistent with the validation by Lim et al (2020). Sampling the AODs at or near the seven PM2.5 supersites operating during KORUS-AQ shows no significant bias (inset values in Figure 4a).…”

“…Fusion of this four-member ensemble is done by the maximum likelihood estimate (MLE) method, with weighting and averaging based on errors determined by comparison to AERONET AOD. The fused satellite AOD product is shown by Lim et al (2020) to have higher accuracy than its member products in comparison with AERONET data during the KORUS-AQ campaign. We will refer to it as the 'GEO satellite AOD' product in what follows.…”

“…Geostationary satellite AOD data are from the GOCI and AHI instruments, covering eastern China and South Korea at 6 km × 6 km spatial resolution and hourly temporal resolution for 8 hours per day (09:30 to 16:30 local time). We use the fused AOD product generated from the GOCI and AHI AOD retrievals, each using two different surface reflectance methods (Lim et al, 2020). Fusion of this four-member ensemble is done by the maximum likelihood estimate (MLE) method, with weighting and averaging based on errors determined by comparison to AERONET AOD.…”