Evaluations of cirrus contamination and screening in ground aerosol observations using collocated lidar systems

Huang, J., C. Hsu, S. Tsay, B.N. Holben, J. Welton, A. Smirnov, M. Jeong, R.A. Hansell, T.A. Berkoff, . Liu, G. Liu, J.R. Campbell, S.C. Liew, and J.E. Barnes (2012), Evaluations of cirrus contamination and screening in ground aerosol observations using collocated lidar systems, J. Geophys. Res., 117, D15204, doi:10.1029/2012JD017757.
Abstract

Cirrus clouds, particularly subvisual high thin cirrus with low optical thickness, are difficult to screen in operational aerosol retrieval algorithms. Collocated aerosol and cirrus observations from ground measurements, such as the Aerosol Robotic Network (AERONET) and the Micro-Pulse Lidar Network (MPLNET), provide us with an unprecedented opportunity to systematically examine the susceptibility of operational aerosol products to cirrus contamination. Quality assured aerosol optical thickness (AOT) measurements were also tested against the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask (VFM) and the Moderate Resolution Imaging Spectroradiometer (MODIS) thin cirrus screening parameters for the purpose of evaluating cirrus contamination. Key results of this study include: (1) quantitative evaluations of data uncertainties in AERONET AOT retrievals are conducted; although AERONET cirrus screening schemes are successful in removing most cirrus contamination, strong residuals displaying strong spatial and seasonal variability still exist, particularly over thin cirrus prevalent regions during cirrus peak seasons; (2) challenges in matching up different data for analysis are highlighted and corresponding solutions proposed; and (3) estimates of the relative contributions from cirrus contamination to aerosol retrievals are discussed. The results are valuable for better understanding and further improving ground aerosol measurements that are critical for aerosol-related climate research.

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Research Program
Radiation Science Program (RSP)