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Ozone Monitoring Instrument (OMI) multi satellite observations

The core information for this publication's citation.: 
Chatfield, R., A. Strawa, et al. (2013), Ozone Monitoring Instrument (OMI) multi satellite observations, Iss., 63, 1434-1446.
Abstract: 

A combination of multiplatform satellite observations and statistical data analysis are used to improve the correlation between estimates of PM2.5 (particulate mass with aerodynamic diameter less that 2.5 µm) retrieved from satellite observations and ground-level measured PM2.5. Accurate measurements of PM2.5 can be used to assess the impact of air pollution levels on human health and the environment and to validate air pollution models. The area under study is California's San Joaquin Valley (SJV) that has a history of poor particulate air quality. Attempts to use simple linear regressions to estimate PM2.5 from satellite-derived aerosol optical depth (AOD) have not yielded good results. The period of study for this project was from October 2004 to July 2008 for six sites in the SJV. A simple linear regression between surface-measured PM2.5 and satellite-observed AOD (from MODIS [Moderate Resolution Imaging Spectroradiometer]) yields a correlation coefficient of about 0.17 in this region. The correlation coefficient between the measured PM2.5 and that retrieved combining satellite observations in a generalized additive model (GAM) resulted in an improved correlation coefficient of 0.77. The model used combinations of MODIS AOD, OMI (Ozone Monitoring Instrument) AOD, NO2 concentration, and a seasonal variable as parameters. Particularly noteworthy is the fact that the PM2.5 retrieved using the GAM captures many of the PM2.5 exceedances that were not seen in the simple linear regression model.

Implications:
Particulate Mass (PM) in the air is a concern because of its effect on climate and human health. PM concentrations retrieved from satellite observations of aerosol optical depth can provide broad regional coverage that is not attained by surface sites. The techniques developed in this paper have resulted in greatly improved correlations between PM retrieved from satellite observations and PM from surface measurements in areas where the correlation is typically low. These improved retrievals can be used to fill in the gaps between surface sites and validate air quality models that are used for air quality forecasts and epidemiological studies.

Research Program: 
Applied Sciences Program (ASP)
Atmospheric Composition Modeling and Analysis Program (ACMAP)