An Efficient Formulation of Microphysical Subgrid Variability in Convective Clouds

Unbiased calculations of microphysical process rates such as autoconversion and accretion in mesoscale numerical weather prediction models require that subgrid-scale (SGS) variability over the model grid volume be taken into account. This variability can be expressed as probability distribution functions (PDFs) of microphysical variables.

Using dynamically balanced Large Eddy Simulation (LES) model results from cases of shallow and congestus cumulus convective clouds, we developed PDFs of the cloud water, droplet concentration, and rain water variables, as well as joint PDFs (JPDFs) of combination of these variables employed in calculations of microphysical conversion rates, such as autoconversion and accretion The fidelity of various PDF approximations was assessed by evaluating errors in estimating these rates.

Biases in assuming different approximations of the JPDFs which vary in their complexity were evaluated. I will introduce a new efficient approximation of the JPDFs which is easy to implement in NWP models. Its derivation will be presented and assessment of its accuracy will be discussed.