q 2003 American Meteorological Society Ice Cloud Optical Thickness and Extinction Estimates from Radar Measurements

• Main Authors: Sergey Y. Matrosov, Matthew D. Shupe, Andrew J. Heymsfield
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 2002
• Subjects: Arctic; Arctic Ocean; Surface Heat Budget of the Arctic Ocean
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.537.3313; http://www.rsmas.miami.edu/users/pzuidema/matrosov_pz_2002_jam.pdf

A remote sensing method is proposed to derive vertical profiles of the visible extinction coefficients in ice clouds from measurements of the radar reflectivity and Doppler velocity taken by a vertically pointing 35-GHz cloud radar. The extinction coefficient and its vertical integral, optical thickness t, are among the fundamental cloud optical parameters that, to a large extent, determine the radiative impact of clouds. The results obtained with this method could be used as input for different climate and radiation models and for comparisons with parameterizations that relate cloud microphysical parameters and optical properties. An important advantage of the proposed method is its potential applicability to multicloud situations and mixed-phase conditions. In the latter case, it might be able to provide the information on the ice component of mixed-phase clouds if the radar moments are dominated by this component. The uncertainties of radar-based retrievals of cloud visible optical thickness are estimated by comparing retrieval results with optical thicknesses obtained independently from radiometric measurements during the yearlong Surface Heat Budget of the Arctic Ocean (SHEBA) field exper-iment. The radiometric measurements provide a robust way to estimate t but are applicable only to optically thin ice clouds without intervening liquid layers. The comparisons of cloud optical thicknesses retrieved from radar and from radiometer measurements indicate an uncertainty of about 77 % and a bias of about 214 % in