Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization
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) retrie...
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ftdoajarticles:oai:doaj.org/article:3715545841a346d38d9872a84b3b2949 2023-05-15T13:06:28+02:00 Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization Chunlin Jin Yong Xue Xingxing Jiang Yuxin Sun Shuhui Wu 2021-11-01T00:00:00Z https://doi.org/10.3390/rs13224689 https://doaj.org/article/3715545841a346d38d9872a84b3b2949 EN eng MDPI AG https://www.mdpi.com/2072-4292/13/22/4689 https://doaj.org/toc/2072-4292 doi:10.3390/rs13224689 2072-4292 https://doaj.org/article/3715545841a346d38d9872a84b3b2949 Remote Sensing, Vol 13, Iss 4689, p 4689 (2021) AHI AOD IBAA PSO Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13224689 2022-12-30T20:32:47Z 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 × AOD AERONET ). 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. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 13 22 4689 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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language |
English |
topic |
AHI AOD IBAA PSO Science Q |
spellingShingle |
AHI AOD IBAA PSO Science Q Chunlin Jin Yong Xue Xingxing Jiang Yuxin Sun Shuhui Wu Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization |
topic_facet |
AHI AOD IBAA PSO Science Q |
description |
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 × AOD AERONET ). 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. |
format |
Article in Journal/Newspaper |
author |
Chunlin Jin Yong Xue Xingxing Jiang Yuxin Sun Shuhui Wu |
author_facet |
Chunlin Jin Yong Xue Xingxing Jiang Yuxin Sun Shuhui Wu |
author_sort |
Chunlin Jin |
title |
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization |
title_short |
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization |
title_full |
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization |
title_fullStr |
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization |
title_full_unstemmed |
Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization |
title_sort |
improved bi-angle aerosol optical depth retrieval algorithm from ahi data based on particle swarm optimization |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13224689 https://doaj.org/article/3715545841a346d38d9872a84b3b2949 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing, Vol 13, Iss 4689, p 4689 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/22/4689 https://doaj.org/toc/2072-4292 doi:10.3390/rs13224689 2072-4292 https://doaj.org/article/3715545841a346d38d9872a84b3b2949 |
op_doi |
https://doi.org/10.3390/rs13224689 |
container_title |
Remote Sensing |
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13 |
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22 |
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4689 |
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1766007337604087808 |