Warning message

Member access has been temporarily disabled. Please try again later.
The ESD Publications website is undergoing a major upgrade that began Friday, October 11th at 5:00 PM PDT. The new upgraded site will be available no later than Monday, October 21st. Until that time, the current site will be visible but logins are disabled.

A PDF-Based Microphysics Parameterization for Simulation of Drizzling Boundary...

Cheng, A., and K. Xu (2009), A PDF-Based Microphysics Parameterization for Simulation of Drizzling Boundary Layer Clouds, J. Atmos. Sci., 66, 2317-2334, doi:10.1175/2009JAS2944.1.
Abstract: 

Formulating the contribution of subgrid-scale (SGS) variability to microphysical processes in boundary layer and deep convective cloud parameterizations is a challenging task because of the complexity of microphysical processes and the lack of subgrid-scale information. In this study, a warm-rain microphysics parameterization that is based on a joint double-Gaussian distribution of vertical velocity, liquid water potential temperature, total water mixing ratio, and perturbation of rainwater mixing ratio is developed to simulate drizzling boundary layer clouds with a single column model (SCM). The probability distribution function (PDF) is assumed, but its parameters evolve according to equations that invoke higher-order turbulence closure. These parameters are determined from the first-, second-, and third-order moments and are then used to derive analytical expressions for autoconversion, collection, and evaporation rates. The analytical expressions show that correlation between rainwater and liquid water mixing ratios of the Gaussians enhances the collection rate whereas that between saturation deficit and rainwater mixing ratios of the Gaussians enhances the evaporation rate. Cases of drizzling shallow cumulus and stratocumulus are simulated with large-eddy simulation (LES) and SCM runs (SCM-CNTL and SCM-M): LES explicitly resolves SGS variability, SCM-CNTL parameterizes SGS variability with the PDF-based scheme, but SCM-M uses the grid-mean profiles to calculate the conversion rates of microphysical processes. SCM-CNTL can well reproduce the autoconversion, collection, and evaporation rates from LES. Comparisons between the two SCM experiments showed improvements in mean profiles of potential temperature, total water mixing ratio, liquid water, and cloud amount in the simulations considering SGS variability. A 3-week integration using the PDF-based microphysics scheme indicates that the scheme is stable for long-term simulations.

PDF of Publication: 
Download from publisher's website.
Research Program: 
Modeling Analysis and Prediction Program (MAP)