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...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Chunlin Jin, Yong Xue, Xingxing Jiang, Yuxin Sun, Shuhui Wu
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
PSO
Q
Online Access:https://doi.org/10.3390/rs13224689
https://doaj.org/article/3715545841a346d38d9872a84b3b2949
id ftdoajarticles:oai:doaj.org/article:3715545841a346d38d9872a84b3b2949
record_format openpolar
spelling 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
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
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
container_volume 13
container_issue 22
container_start_page 4689
_version_ 1766007337604087808