Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part I: Description and Sensitivity Analysis

Abstract

This study evaluates the sensitivity of winter precipitation to numerous aspects of a bulk, mixed-phase microphysical parameterization found in three widely used mesoscale models [the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5), the Rapid Update Cycle (RUC), and the Weather Research and Forecast (WRF) model]. Sensitivities of the microphysics to primary ice initiation, autoconversion, cloud condensation nuclei (CCN) spectra, treatment of graupel, and parameters controlling the snow and rain size distributions are tested. The sensitivity tests are performed by simulating various cloud depths (with different cloud-top temperatures) using flow over an idealized two-dimensional mountain. The height and width of the two-dimensional barrier are designed to reproduce an updraft pattern with extent and magnitude consistent with documented freezing-drizzle cases. By increasing the moisture profile to saturation at low temperatures, a deep, precipitating snow cloud is also simulated. Upon testing the primary sensitivities of the microphysics scheme in two dimensions as reported in the present study, the MM5 with the modified scheme will be tested in multiple case studies and the results will be compared to observations in a forthcoming companion paper, Part II. The key results of this study are 1) the choice of ice initiation schemes is relatively unimportant for deep precipitating snow clouds but more important for shallow warm clouds having cloud-top temperature greater than Ϫ13ЊC, 2) the assumed snow size distribution and associated snow diffusional growth along with the assumed graupel size distribution and method of transforming rimed snow into graupel have major impacts on the mass of cloud water and formation of freezing drizzle, and 3) a proper simulation of drizzle using a singlemoment scheme and exponential size distribution requires an increase in the rain intercept parameter, thereby reducing rain terminal velocities to values more characteristic of drizzle.

Evaluation of a WRF dynamical downscaling simulation over California

“…2.2) commonly used for numerical weather prediction. For this simulation we use the following parameterization options available in the standard model distribution: Thompson microphysics ( Thompson et al 2004), Kain and Fritsch (1990) convection, Rapid Radiative Transfer Model (RRTM) longwave radiation (Mlawer et al 1997), Dudhia (1989) shortwave radiation, Yonsei University (YSU) boundary layer scheme (Hong et al 2006), and Rapid Update Cycle (RUC) surface parameterization (Smirnova et al 1997(Smirnova et al , 2000. Motivation for these choices is provided in Chin (2008).…”

Modeling Rapid Intensification of Typhoon Saomai (2006) with the Weather Research and Forecasting Model and Sensitivity to Cloud Microphysical Parameterizations

“…This ensures that all of these processes are treated equally. Thompson et al (2004) scheme is a new bulk microphysical parameterization (BMP) that has been developed for using with WRF or other mesoscale models. Unlike any other BMP, the assumed snow size distribution depends on both ice water content and temperature, and is represented as a sum of exponential and gamma distributions.…”

Quantitative precipitation forecasting in the Alps: The advances achieved by the Mesoscale Alpine Programme

“…In all four models, subgrid-scale convection was parametrized for horizontal resolutions of 18 and 6 km, whereas convection was explicitly resolved in the innermost grid with a horizontal resolution of 2 km. Cloud microphysics was treated with the Reisner-Thompson scheme in MM5 and WRF (Reisner et al, 1998;Thompson et al, 2004), which has five hydrometeor classes (cloud water and ice, rain, snow and graupel) and a two-moment scheme for cloud ice. The MOLOCH microphysics scheme is described in Drofa and Malguzzi (2004) but has recently been updated to include separate prognostic variables for graupel and hail.…”