Evaluation of Subgrid-Scale Hydrometeor Transport Schemes Using a High-Resolution Cloud-Resolving Model

Abstract

Potential ways of parameterizing vertical turbulent fluxes of hydrometeors are examined using a high-resolution simulation of continental deep convection. The cloud-resolving model uses a double-moment microphysics scheme that contains prognostic variables for four hydrometeor types: rain, graupel, cloud ice, and snow. The benchmark simulation with a horizontal grid spacing of 250 m is analyzed to evaluate three different ways of parameterizing the turbulent vertical fluxes of hydrometeors: an eddy-diffusion approximation, a quadrant-based decomposition, and a scaling method that accounts for within-quadrant (subplume) correlations. Results show that the downgradient nature of the eddy-diffusion approximation enforces transport of mass away from concentrated regions, whereas the benchmark simulation indicates that the vertical transport often moves mass from below the level of maximum concentration to aloft. Unlike the eddy-diffusion approach, the quadrimodal decomposition is able to capture the signs of the flux gradient but underestimates the magnitudes. The scaling approach, which accounts empirically for within-quadrant correlations, improves the representation of the vertical fluxes for all hydrometeors except snow. A sensitivity study is performed to illustrate how vertical transport effects on the vertical distribution of hydrometeors are compounded by accompanying changes in microphysical process rates. Results from the sensitivity tests show that suppressing rain or graupel transport drastically alters vertical profiles of cloud ice and snow through changes in the distribution of cloud water, which in turn governs the production of cloud ice and snow aloft. Last, a viable subgrid-scale hydrometeor transport scheme in an assumed probability density function parameterization is discussed.

A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection

“…Important features of P(w, q x ) (i.e., much of the impact of correlation on the fluxes) can be captured by decomposing the joint PDF into a small number of sections, such as quadrants as was done in Wong et al (2015). Instead of defining P(w, q x ), it is possible to compute the covariance directly from the variances and the correlation coefficient (r w,qx ) between w and q x .…”

“…Wong et al (2015) defined each quadrant using the conditions Wong et al (2015) defined each quadrant using the conditions…”

“…Implementing the quadrantbased decomposition as described in Wong et al (2015) is difficult because the method assumes that the joint distribution P(w, q x ) is known a priori. 0.…”

“…In search for an improved parameterization of subgrid-scale transport of hydrometeors, Wong et al (2015) demonstrated that the shape of the vertical flux profile can be well represented by decomposing the net flux into contributions from four quadrants of the joint hydrometeor mixing ratio-vertical velocity distribution. This step is referred to as conditional sampling.…”

“…First, we aim to modify the hydrometeor transport scheme of Wong et al (2015), so that the conditional sampling is applied to marginal (i.e., one dimensional) PDFs instead of joint (i.e., two dimensional) distributions. First, we aim to modify the hydrometeor transport scheme of Wong et al (2015), so that the conditional sampling is applied to marginal (i.e., one dimensional) PDFs instead of joint (i.e., two dimensional) distributions.…”