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 10ĝ€¯min AHI or 1ĝ€¯h GOCI data at 6km×6km 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), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error (RMSE), and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean products can still be useful as MLE.
| Original language | English |
|---|---|
| Pages (from-to) | 4575-4592 |
| Number of pages | 18 |
| Journal | Atmospheric Measurement Techniques |
| Volume | 14 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2021 Jun 21 |
Bibliographical note
Funding Information:Acknowledgements. We thank all principal investigators and their staff for establishing and maintaining the AERONET sites used in this investigation. This subject is supported by the Korea Ministry of Environment (MOE) through the “Public Technology Program based on Environmental Policy (2017000160001)”. This work was also supported by a grant from the National Institute of Environment Research (NIER), funded by the MOE of the Republic of Korea (NIER-2021-01-02-071). This work was also supported by a grant from the NIER, funded by the MOE of the Republic of Korea (NIER-2021-04-02-056). This research was also supported by the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (grant no.: 2020M3G1A1114615). We thank all members of the KORUS-AQ science team for their contributions to the field study and the data processing (https://doi.org/10.5067/Suborbital/KORUSAQ/DATA01).
Funding Information:
Financial support. This research was supported by the Korea Ministry of Environment (MOE) through the “Public Technology Program based on Environmental Policy” (grant no. 2017000160001). This work was also supported by a grant from the National Institute of Environment Research (NIER), funded by the MOE of the Republic of Korea (grant no. NIER-2021-01-02-071). This work was also supported by a grant from the NIER, funded by the MOE of the Republic of Korea (grant no. NIER-2021-04-02-056). This research was also supported by the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (grant no. 2020M3G1A1114615).
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All Science Journal Classification (ASJC) codes
- Atmospheric Science