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

Abstract

Abstract. 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 MultiAngle 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 Correspondence to: H. Zhang (hazhang@umbc.edu) 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.

Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS)

“…Aerosols are also linked to respiratory illnesses (e.g., Pope and Dockery, 2006) and meningitis epidemics (e.g., Deroubaix et al, 2013). Since the global aerosol distribution shows high spatial and temporal variability, many studies have developed aerosol retrieval algorithms utilizing both low Earth orbit satellite measurements (Hsu et al, 2004;Kim et al, 2007;Torres et al, 2007;Kahn et al, 2010;Lyapustin et al, 2011b;von HoyningenHuene et al, 2011;Wong et al, 2010;Bevan et al, 2012;Sayer et al, 2012;Levy et al, 2013) and geostationary orbit (GEO) satellite measurements (Knapp et al, 2002(Knapp et al, , 2005Wang et al, 2003;Urm and Sohn, 2005;Yoon et al, 2007;Kim et al, 2008;Lee et al, 2010;Zhang et al, 2011;. These studies have typically adopted an inversion approach, using a precalculated look-up table (LUT) based on assumed aerosol optical properties (AOPs) to retrieve aerosol information from the measured visible reflectance at the top of the atmosphere.…”

Utilization of O<sub>4</sub> slant column density to derive aerosol layer height from a space-borne UV–visible hyperspectral sensor: sensitivity and case study

“…For these reasons, observations from satellite remote sensing have been carried out to investigate aerosol properties at regional and global scale, including aerosol optical depth (AOD) (e.g., Curier et al, 2008;Levy et al, 2007;Torres et al, 2007;Ahn et al, 2014;Veefkind et al, 1999;Zhang et al, 2011), fine-mode fraction (FMF) or Ångström exponent (AE) (e.g., Jones and Christopher, 2007;Lee et al, 2010;Nakajima and Higurashi, 1998;Remer et al, 2008), single scattering albedo (SSA) (e.g., Dubovik et al, 2002;Levy et al, 2007;Jeong and Hsu, 2008;Torres et al, 1998Torres et al, , 2005Torres et al, , 2007Jethva et al, 2014), and aerosol types (e.g., Higurashi and Nakajima, 2002;Kim et al, 2007;Lee et al, 2010). This information was further utilized to estimate radiative forcing of aerosol (e.g., Christopher et al, 2006;Chung et al, …”

Aerosol optical depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the western United States

“…However, surface reflectance can change rapidly due to the change of the solar position, the change of surface vegetation, the change of the surface wetness and snow coverage during the 28-day period. For example, a change of surface reflectance from 0.11 to 0.14 in a 28-day period was observed at 16:45 UTC at GSFC (Goddard Space Flight Center) site in the fall (Zhang et al, 2011). Moreover, GASP can be affected by cloud shadows and underestimates the surface reflectance in the solar-satellite geometries where cloud shadows are often observed, e.g.…”

“…To address this problem, Zhang et al (2011) developed a new AOD retrieval algorithm for the GOES imager by adapting the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm originally developed for MODIS (Lyapustin and Wang, 2008;Lyapustin et al, 2011a,b). While this paper will not reproduce the entire MAIAC logic, a brief description is necessary to understand the new hybrid algorithm.…”