Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model

Abstract

S u MMARYAn explicit microphysical parametrization including ice physics was developed for use in the NCARPenn State Mesoscale Model Version 5 (MM5). This scheme includes three options of increasing complexity to represent the hydrometeor species. The scheme is evaluated by comparing model simulations with two well observed winter storms that occurred during the Winter Icing and Storms Project. The evaluation focused on the prediction of supercooled liquid water (SLW), which is of particular importance to aircraft icing. The intercomparisons showed that:1. The double-moment microphysical scheme, in which both ice mixing ratios and number concentrations were predicted, performed best, with close agreement to the observed fields.2. The single-moment schemes, in which the mixing ratio of ice species are predicted and number concentration specified, performed reasonably well if a diagnostic equation for No,,, the Y-intercept of the assumed exponential snow distribution, is allowed to vary with snow mixing ratio.3. Accurate microphysical simulations of SLW in shallow upslope clouds and cyclonic storms required accurate simulations of the kinematic and thermodynamic structure and evolution of the storms.Though the two storms were dynamically different, the SLW formed through a balance of the condensational growth of cloud water and the depletion of cloud water by deposition and riming of snow andlor graupel for both storms. The results of this study suggest that accurate prediction of SLW over limited areas of the country may be possible using the current microphysical parametrization and high-resolution grids (6x < 10 km).

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

“…Compared with data from the Surface Heat Budget of the Arctic (SHEBA) project, they noted an underestimation of the CLW by 42%, which was found to be sensitive to the ice crystal concentration. Other recent modeling studies in the Arctic compared with SHEBA data note similar deficiencies in modeling supercooled liquid water using the Reisner et al (1998) scheme that is currently used in AMPS (Morrison andPinto 2005, 2006;Morrison et al 2005). Consequently, Morrison and Pinto (2006) find that a scheme that not only predicts mixing ratios of the hydrometeor species of cloud ice, cloud liquid water, rain, and snow within cloud layers [as in the Reisner et al (1998) scheme], but also the number concentration of these species, is required to generate sufficient supercooled liquid water that is in better agreement with the observations.…”

“…Other recent modeling studies in the Arctic compared with SHEBA data note similar deficiencies in modeling supercooled liquid water using the Reisner et al (1998) scheme that is currently used in AMPS (Morrison andPinto 2005, 2006;Morrison et al 2005). Consequently, Morrison and Pinto (2006) find that a scheme that not only predicts mixing ratios of the hydrometeor species of cloud ice, cloud liquid water, rain, and snow within cloud layers [as in the Reisner et al (1998) scheme], but also the number concentration of these species, is required to generate sufficient supercooled liquid water that is in better agreement with the observations. These studies help to explain the deficiency in low-cloud prediction by the pseudosatellite product as well as what changes are needed to predict the CLW variability and, therefore, likely improve the CF correlation.…”

Water vapour variability induced by urban/rural surface heterogeneities during convective conditions

“…Cloud microphysics are treated with a sophisticated scheme having prognostic equations for cloud water, cloud ice, cloud ice particle number concentration, rain, snow and graupel (Reisner et al, 1998). The Grell cumulus parametrization (Grell, 1993) is used in model domain 1, and is switched off in model domain 2.…”

North Atlantic cloud cover response to the North Atlantic oscillation and relationship to surface temperature changes

“…MM5 is a nonhydrostatic model and is configured using a staggered horizontal grid with a vertical s-coordinate system. In the simulations discussed here, large-scale (grid-scale) cloud and precipitation processes are represented using the explicit microphysics parameterization of Reisner et al [1998]. This parameterization predicts cloud water and ice, rain, and snow water mixing ratios, and allows for the occurrence of mixed phase clouds.…”

“…A modified form of the NCAR Community Climate Model, version 2 (CCM2), radiation parameterization [Hack et al, 1993] is used to calculate radiative transfer through the model atmosphere. The radiative properties of model-generated clouds are prescribed based on the predicted cloud water and ice mixing ratios from the Reisner et al [1998] scheme. Boundary layer turbulent fluxes are determined from the 1.5-order turbulence closure method used in the National Centers for Environmental Prediction Eta Model [Janjic, 1994].…”