GOES Aerosol/Smoke Product (GASP) over North America: Comparisons to AERONET and MODIS observations

Abstract

[1] The GOES Aerosol/Smoke Product (GASP) is a retrieval of the aerosol optical depth (AOD) using visible imagery. The product currently runs operationally at NOAA/NESDIS in near-real time at 30 min intervals. This high temporal resolution is not possible with polar orbiting instruments which produce one daily image. This work evaluates the GASP AOD from the GOES-12 Imager over North America at various temporal and spatial scales based on comparisons with AOD from the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We find a mean GASP/AERONET correlation of 0.79, rms difference of 0.13 and slope of 0.8, based on a statistical analysis at 10 northeastern U.S. and Canadian sites. The GASP AOD has a slight negative bias when the AOD is greater than 0.35 and a positive bias elsewhere. The absolute bias is less than 30% when the AOD is greater than 0.1. When the AOD is less than 0.15 we find poor correlation and biases greater than 30%. The GASP/ AERONET statistics also indicate that GASP can be used to examine the seasonal and diurnal variability in the AOD over the eastern United States between 1215 and 2115 UTC. GASP/AERONET AOD correlations were generally less than 0.5 elsewhere in the continental United States. Comparisons between the MODIS and GASP AOD over the eastern United States in the summer of 2004 showed agreement within 20% and correlations greater than 0.7 under elevated AOD conditions. Simultaneous comparisons between GASP, MODIS, and AERONET AODs showed good agreement over the northeastern United States and Canada, with higher correlation and lower rms differences in the MODIS/AERONET comparisons than in the GASP/AERONET comparisons.

An Improved Method for Estimating Surface Fine Particle Concentrations Using Seasonally Adjusted Satellite Aerosol Optical Depth

“…GASP AOD correlations with the hourly PM 2.5 concentrations were 0.54 for the summer months and 0.50 for 2005, compared with correlations of greater than 0.7 for MODIS Collection 5 and MISR. This result was consistent with Prados et al, 23 in which the authors found that MODIS had a higher correlation than GASP with AERONET AOD observations in a statistical analysis of 10 eastern U.S. sites. Prados et al 23 also found that high GASP AOD measurements (Ͼ0.7) did not appear to correspond with high surface PM 2.5 concentrations; similar results were found in this analysis.…”

“…21 In contrast to the polar-orbiting sensors, which only have one daily daytime overpass for any location on the Earth, the GOES Aerosol and Smoke Product (GASP) is an AOD product provided by the National Oceanic and Atmospheric Administration (NOAA) from the geostationary GOES-12 (East) imager at 15 and 45 min after the hour during daylight hours. 22,23 The AOD is computed from the imager's visible channel (520 -720 nm) at approximately 1-km horizontal resolution. The cloud-masking algorithm is based on the Clouds from AVHRR (CLAVR) algorithm.…”

“…High temporal resolution of aerosol data can be used to assess rapidly changing air quality because of dust events, wildfires, or rapidly moving aerosol plumes. 23 This study focuses on analysis of satellite AOD and ground-based PM 2.5 monitor measurements from the summer (June, July, and August) of 2004 and the entire year of 2005. Increased PM 2.5 levels in the summer months can result in NAAQS violations (40 CFR part 50) and contribute to increased incidences of adverse public health effects, such as lung and respiratory disorders.…”

Retrieving aerosol in a cloudy environment: aerosol product availability as a function of spatial resolution

“…Scans of just North America or the continental United States are much faster. GOES produces an aerosol product (Prados et al, 2007), but these will not be analyzed in this study. Only the cloud mask developed for GOES-R, which has not yet been launched, will be applied to GOES data and used in this study.…”

“…For climate, a global perspective is needed Remer et al, 2009). For air quality, even if interest is more regional, there is need for a more complete coverage and higher density of spatial sampling than a network of ground-based in situ monitoring stations can provide (Chu et al, 2003;Prados et al, 2007;Gupta and Christopher, 2009). Both these applications are increasingly relying on satellite retrievals of aerosol information to provide the observational constraints on models, offer new insights on aerosol distributions, and provide day-to-day coverage and accumulated statistics of aerosol properties (Stier et al, 2005;Yu et al, 2006;van Donkelaar et al, 2006van Donkelaar et al, , 2011.…”