Climate Effects/Controls on Mesoscale Processes
Other Agency – Parameterization of Drop Spectra in Drizzling Stratocumulus Clouds
Z. Kogan (primary – CIMMS at OU), Y. Kogan, Mechem
Funding Agencies: U.S. DOE, Office of Naval Research
Objectives
Parameterize drop spectra by analytical functions for use in remote sensing
retrievals and cloud parameterization.
Accomplishments
The development of cloud microphysical retrievals and cloud microphysics
parameterizations rely heavily on the knowledge of the shape of drop size
distributions (DSDs). Many investigations assume that DSDs in the whole,
or parts of the drop size range, may be approximated by known analytical
functions. The most frequently employed approximations are gamma, lognormal,
Khrgian-Mazin, and Marshall-Palmer type functions. At present, little is
known about the accuracy of each of these approximations, especially their
ability to successfully simulate the higher moments of the DSD. We present
results from an evaluation of the applicability and accuracy of DSD approximations
using a combination of lognormal and gamma-type functions for stratocumulus
and shallow convective clouds.
The DSDs are generated using the new version of the CIMMS LES explicit microphysics model (SAMEX) in simulations of cases observed during the ASTEX and DYCOMS-II field projects. Special emphasis in the analysis is placed on the fidelity of representing the higher moments of the drop spectra, such as precipitation flux and radar reflectivity. Our results indicate that approximating drop spectra in drizzling stratocumulus by Gamma-type distributions proves to be much more accurate than approximation by the lognormal distribution. In drizzling stratocumulus the two mode approximations provide better accuracy than the one-mode approximations. In numerical models which use two-moment microphysical parameterization schemes, the six parameters defining the two-mode Gamma distribution can be expressed through the four predictive microphysical variables describing concentrations and mixing ratios of cloud and rain drops. The latter approach requires parameterization of the drizzle mode dispersion.
This project is ongoing.
Publications
Kogan, Y. L., Z.N. Kogan, and D. B. Mechem, 2007: Approximation
of cloud drop distributions by analytical functions. Proc., Seventeenth
Atmospheric Radiation Measurement (ARM) Science Team Meeting, Monterey,
CA, U.S. Dept. of Energy.
Comparisons of rain rates (left panels) and radar reflectivity (right panels) approximated by two-mode Gamma-type distributions. Top row: Gamma-distribution is defined by three parameters. Middle row: Gamma-distribution is defined by two parameters with dispersion parameterized as a function of drizzle drop concentration. Bottom row: The dispersion is parameterized as a function of drizzle drop concentration and drizzle mixing ratio