The Sensitivity of Simulated Convective Storms to Variations in Prescribed Single-Moment Microphysics Parameters that Describe Particle Distributions, Sizes, and Numbers

Abstract

The sensitivity of cloud-scale simulations of deep convection to variations in prescribed microphysics parameters is studied, using the single-moment scheme in the Regional Atmospheric Modeling System (RAMS) model. Realistic changes were made to the shape parameters in the gamma distributions of the diameters of precipitating hydrometeors and of cloud droplets, in the number concentration of cloud droplets, and in the mean size of the hail and graupel. Simulations were performed with two initial soundings that are identical except for their temperature. The precipitation rate at the ground is not very sensitive to changes in the value of the shape parameter used for all precipitating hydrometeors (rain, hail, graupel, snow, and aggregates) or to the mean size of the hail and graupel, owing to counteracting effects. For example, with a larger shape parameter value, there is a greater production of precipitation by collection of cloud water, but also a larger rate of evaporation of the liquid precipitation. However, with a larger shape parameter value, the greater production of precipitation by collection and the increased evaporation result in more low-level cooling by the downdraft. Specifying larger hail and graupel results in less low-level cooling by the downdraft. The simulation with the cold initial sounding showed a change in storm propagation velocity when the specified sizes of hail and graupel were increased, but this did not occur when the warm initial sounding was used. With a larger shape parameter for cloud water or with a larger number concentration of cloud droplets, there is less autoconversion and less collection of cloud water and, consequently, much less precipitation at the ground and denser cirrus anvils. While the number concentration of cloud droplets can be forecast in some models with parameterized microphysics, at present the shape parameter for cloud water cannot and must, therefore, be carefully selected.

Cloud-resolving simulations of mercury scavenging and deposition in thunderstorms

“…PW impacts the amount of condensate present within the updraft and thus the vertical velocity. The three parameter space used in this study is a subset of higher dimensional parameter spaces utilized by prior numerical modeling studies of thunderstorm morphology and evolution (Cohen, 2000;McCaul Jr. and Weissman, 2001;McCaul Jr. et al, 2005;Cohen and McCaul Jr., 2006;Kirkpatrick et al, 2007;Kirkpatrick et al, 2011). Note that these studies do show that CAPE, SHEAR and PW modulate cloud mass flux, cloud entrainment and hydrometeor mass distribution in thunderstorms and all of these processes are important to wet deposition removal of atmospheric mercury.…”

The Importance of the Shape of Cloud Droplet Size Distributions in Shallow Cumulus Clouds. Part I: Bin Microphysics Simulations

“…When only considering condensation, theoretical arguments and parcel modeling have shown that the distribution narrows less (shape parameter increases less) for distributions with initially higher cloud droplet or aerosol concentrations (Yum and Hudson 2005;Liu et al 2006;Peng et al 2007;Pinsky et al 2014). In turn, the shape of the droplet distribution can influence the rate at which these processes act (e.g., Milbrandt and Yau 2005a;Cohen and McCaul 2006). For example, a higher shape parameter (narrower distribution) is expected to result in slower collection of cloud water by rain (Cohen and McCaul 2006) and reduced size sorting for precipitating hydrometeors (Milbrandt and Yau 2005a).…”

Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms

“…For the purpose of this study, single-moment bulk microphysics parameterization scheme is used as in other sensitivity studies of the similar type (Van den Heever and Cotton, 2004;Cohen and McCaul, 2006). The model microphysics represents cloud water, rain and three classes of ice (cloud ice, snow and hail).…”

“…The improvement of model microphysics is essential because convective precipitation is very sensitive to the uncertainties in cloud microphysics (Gilmore et al, 2004a,b;Spiridonov andĆurić, 2005). The choice of a hydrometeor size distribution is critical for the cloud model outputs (Ćurić et al, 1998;Cohen and McCaul, 2006). There are studies that use the meteorological models to simulate the convective precipitation over a large area as well as to compare the modeled and observed datasets (e.g.…”