Improving satellite-retrieved aerosol microphysical properties using GOCART data

S Li,Y Liu,M J Garay,R Kahn,M Chin

doi:10.5194/amt-8-1157-2015

Abstract

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) aboard the NASA Earth Observing System's Terra satellite can provide more reliable aerosol optical depth (AOD) and better constraints on particle size (Ångström exponent, or ANG), sphericity, and single-scattering albedo (SSA) than many other satellite instruments. However, many aerosol mixtures pass the algorithm acceptance criteria, yielding a poor constraint, when the particle-type information in the MISR radiances is low, typically at low AOD. We investigate adding value to the MISR aerosol product under these conditions by filtering the list of MISR-retrieved mixtures based on agreement between the mixture ANG and absorbing AOD (AAOD) values, and simulated aerosol properties from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. MISR–GOCART ANG difference and AAOD ratio thresholds for applying GOCART constraints were determined based on coincident AOD, ANG, and AAOD measurements from the AErosol RObotic NETwork (AERONET). The results were validated by comparing the adjusted MISR aerosol optical properties over the contiguous USA between 2006 and 2009 with additional AERONET data. The correlation coefficient (r) between the adjusted MISR ANG derived from this study and AERONET improves to 0.45, compared to 0.29 for the MISR Version 22 standard product. The ratio of the adjusted MISR AAOD to AERONET increases to 0.74, compared to 0.5 for the MISR operational retrieval. These improvements occur primarily when AOD < 0.2 for ANG and AOD < 0.5 for AAOD. Spatial and temporal differences among the aerosol optical properties of MISR V22, GOCART, and the adjusted MISR are traced to (1) GOCART underestimation of AOD and ANG in polluted regions; (2) aerosol mixtures lacking in the MISR Version 22 algorithm climatology; (3) low MISR sensitivity to particle type under some conditions; and (4) parameters and thresholds used in our method.

Highlights

Atmospheric aerosols affect global climate directly by absorbing and reflecting solar radiation (Myhre, 2009) and indirectly by altering cloud microphysics and biogeochemical cycles (Mahowald, 2011)
The analysis presented in this paper focuses on the Multi-angle Imaging SpectroRadiometer (MISR), which was launched into a sun-synchronous polar orbit in December 1999 aboard the NASA Earth Observing System (EOS) Terra satellite
Our analysis indicates that setting a relatively stringent Goddard Chemistry Aerosol Radiation and Transport (GOCART) ANG threshold can significantly improve the correlation between MISR ANG and AErosol RObotic NETwork (AERONET)

Summary

Introduction

Atmospheric aerosols affect global climate directly by absorbing and reflecting solar radiation (Myhre, 2009) and indirectly by altering cloud microphysics and biogeochemical cycles (Mahowald, 2011). Despite several decades of research, the quantitative relationships among aerosols, clouds, and precipitation within the global climate system are still not well understood due to the inadequacy of existing tools and methodologies (Stevens and Feingold, 2009) and available measurements. Ground-based observations, such as those provided by the AErosol RObotic NETwork (AERONET), are often used to constrain columneffective aerosol optical properties, but these point measurements have very limited spatial coverage (Holben et al, 1998), and the derivation of particle properties other than the Published by Copernicus Publications on behalf of the European Geosciences Union. S. Li et al.: Improving satellite-retrieved aerosol microphysical properties spectral optical depth or Ångström exponent (ANG) requires many assumptions. Researchers have explored the potential of using satellite-retrieved aerosol properties to fill gaps not covered by ground observations. Satellite products have advanced our understanding of aerosol impacts on global climate change (Lohmann and Lesins, 2002), particle type (Kahn and Limbacher, 2012; Liu et al, 2007a), and air quality (Liu et al, 2009b; van Donkelaar et al, 2010)

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Results

Conclusion