Cloud Modeling and Probability Density Functions

University Of Wisconsin-Milwaukee, Milwaukee WI

Investigators

Abstract

Large-scale numerical models of the atmosphere include parameterizations of microphysical and radiative processes. Such parameterizations require accurate input in the form of grid-resolved fields such as liquid water content, ice water content, and droplet number concentration. These fields are produced by host model equations. Some mesoscale models prognose liquid water using the assumption that there is no thermodynamic variability on scales smaller than the grid box size. This assumption is incompatible with cloud schemes that predict subgrid-scale cloud fraction or any other thermodynamic subgrid variability. In this research, the Principal Investigator seeks to improve the formulation and accuracy of host model equations for liquid, ice, and droplet number that drive microphysics and radiative parameterizations. To improve them, he will investigate the effects of subgrid variability. A principal object of study is the probability density function (PDF) of relevant quantities, such as total water content. Specifically, the PI will (1) use mathematical properties of the relevant PDF to rigorously derive large-scale equations for liquid water content, ice water content, and droplet number concentration; (2) analyze aircraft data to ascertain the shapes of joint PDFs of liquid and ice, and then use the PDFs to close the host model equations; and (3) implement the host model equations in an idealized one-dimensional cloud parameterization. The intellectual merit is twofold: (1) to improve understanding of mixed-phase clouds; and (2) to improve representation of clouds in atmospheric numerical models. If successful, the research may ultimately have implications for cloud parameterizations ranging from simple bulk microphysics schemes to bin microphysics schemes, and for models ranging from general circulation models to cloud resolving models. The research will have two broader impacts: it will help train a postdoctoral research associate, and its results will be made widely available to the scientific community through journal publications and the internet.