Thursday, 27 October 2005: 5:00 PM
Alvarado ABC (Hotel Albuquerque at Old Town)
Uncertainties in parameterizing clouds have been identified as important sources of error in climate model outputs. These problems are linked to insufficient data and the complex behaviors of clouds. Progress in reducing these uncertainties is being made as more data are collected and analyzed. In particular, polarimetric radars are capable of estimating microphysical cloud properties in addition to macrophysical features. In this paper, a physical-based electromagnetic backscattering model, whose components are based on previous experimental work, is presented for diagnosing and linking cloud particle behavior to the scattered signals received. The scatterers in the model consist of prolate and oblate spheroids and the scattering regime is assumed to be Rayleigh. Inputs to the scattering model include mean drop diameter, equivalent spherical drop size distribution form, and liquid-water content. These parameters are converted to equivalent spheroids using mass conservation and diameter dependent axial ratios. A terminal fall velocity based on fall geometry is calculated and a background wind field is imposed. Finally, additional scaling and dyadic scattering of plane waves are applied to simulate the effective scattered signal. Using this simulation algorithm, innovative signal processing/filtering techniques will be developed for the observations of cold clouds. Comparative results will also be presented from the S-band polarimetric radar (KOUN) operated by the National Severe Storms Laboratory in Norman, Oklahoma.
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