A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States

Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retr...

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Main Authors: H. Zhang, A. Lyapustin, Y. Wang, S. Kondragunta, I. Laszlo, P. Ciren, R. M. Hoff
Format: Article
Language:English
Published: Copernicus Publications 2011-12-01
Series:Atmospheric Chemistry and Physics
Online Access:http://www.atmos-chem-phys.net/11/11977/2011/acp-11-11977-2011.pdf
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spelling doaj-e9c9123e17834e08bd49947640284ff22020-11-24T23:35:26ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242011-12-011123119771199110.5194/acp-11-11977-2011A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United StatesH. ZhangA. LyapustinY. WangS. KondraguntaI. LaszloP. CirenR. M. HoffAerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 μm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.http://www.atmos-chem-phys.net/11/11977/2011/acp-11-11977-2011.pdf
collection DOAJ
language English
format Article
sources DOAJ
author H. Zhang
A. Lyapustin
Y. Wang
S. Kondragunta
I. Laszlo
P. Ciren
R. M. Hoff
spellingShingle H. Zhang
A. Lyapustin
Y. Wang
S. Kondragunta
I. Laszlo
P. Ciren
R. M. Hoff
A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States
Atmospheric Chemistry and Physics
author_facet H. Zhang
A. Lyapustin
Y. Wang
S. Kondragunta
I. Laszlo
P. Ciren
R. M. Hoff
author_sort H. Zhang
title A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States
title_short A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States
title_full A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States
title_fullStr A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States
title_full_unstemmed A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States
title_sort multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the united states
publisher Copernicus Publications
series Atmospheric Chemistry and Physics
issn 1680-7316
1680-7324
publishDate 2011-12-01
description Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 μm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.
url http://www.atmos-chem-phys.net/11/11977/2011/acp-11-11977-2011.pdf
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