Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization
Journal article
Jin, Chunlin, Jiang, Xingxing, Sun, Yuxin, Wu, Shuhui and Xue, Yong 2021. Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization. Remote Sensing. 13 (22), p. 4689. https://doi.org/10.3390/rs13224689
| Authors | Jin, Chunlin, Jiang, Xingxing, Sun, Yuxin, Wu, Shuhui and Xue, Yong |
|---|---|
| Abstract | The Advanced Himawari Imager (AHI) aboard the Himawari-8, a new generation of geostationary meteorological satellite, has high-frequency observation, which allows it to effectively capture atmospheric variations. In this paper, we have proposed an Improved Bi-angle Aerosol optical depth (AOD) retrieval Algorithm (IBAA) from AHI data. The algorithm ignores the aerosol effect at 2.3 μm and assumes that the aerosol optical depth does not change within one hour. According to the property that the reflectivity ratio K of two observations at 2.3 μm does not change with wavelength, we constructed the equation for two observations of AHI 0.47 μm band. Then Particle Swarm Optimization (PSO) was used to solve the nonlinear equation. The algorithm was applied to the AHI observations over the Chinese mainland (80°–135°E, 15°–60°N) between April and June 2019 and hourly AOD at 0.47 μm was retrieved. We validated IBAA AOD against the Aerosol Robotic Network (AERONET) sites observation, including surrounding regions as well as the Chinese mainland, and compared it with the AHI L3 V030 hourly AOD product. Validation with AERONET of 2079 matching points shows a correlation coefficient R = 0.82, root-mean-square error RMSE = 0.27, and more than 62% AOD retrieval results within the expected error of ±(0.05 + 0.2 × AODAERONET). Although IBAA does not perform very well in the case of coarse-particle aerosols, the comparison and validation demonstrate it can estimate AHI AOD with good accuracy and wide coverage over land on the whole |
| Keywords | Bid Data; PSO; geostationary meteorological satellite |
| Year | 2021 |
| Journal | Remote Sensing |
| Journal citation | 13 (22), p. 4689 |
| Publisher | MDPI AG |
| ISSN | 2072-4292 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/rs13224689 |
| Web address (URL) | http://hdl.handle.net/10545/626346 |
| http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| hdl:10545/626346 | |
| Publication dates | 20 Nov 2021 |
| Publication process dates | |
| Deposited | 07 Mar 2022, 12:36 |
| Accepted | 18 Nov 2021 |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
| Contributors | China University of Mining and Technology, Xuzhou 221116, China and University of Derby |
| File | File Access Level Open |
| File | File Access Level Open |
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