A PDF-Based Formulation of Microphysical Variability in Cumulus Congestus Clouds*

Abstract

Abstract Calculating unbiased microphysical process rates over mesoscale model grid volumes necessitates knowledge of the subgrid-scale (SGS) distribution of variables, typically represented as probability distribution functions (PDFs) of the prognostic variables. In the 2014 Journal of the Atmospheric Sciences paper by Kogan and Mechem, they employed large-eddy simulation of Rain in Cumulus over the Ocean (RICO) trade cumulus to develop PDFs and joint PDFs of cloud water, rainwater, and droplet concentration. In this paper, the approach of Kogan and Mechem is extended to deeper, precipitating cumulus congestus clouds as represented by a simulation based on conditions from the TOGA COARE field campaign. The fidelity of various PDF approximations was assessed by evaluating errors in estimating autoconversion and accretion rates. The dependence of the PDF shape on grid-mean variables is much stronger in congestus clouds than in shallow cumulus. The PDFs obtained from the TOGA COARE simulations for the calculation of accretion rates may be applied to both shallow and congestus cumulus clouds. However, applying the TOGA COARE PDFs to calculate autoconversion rates introduces unacceptably large errors in shallow cumulus clouds, thus precluding the use of a “universal” PDF formulation for both cloud types.