Model aerosol sensitivity studies and microphysical interactions in an orographic snowfall event

Mesoscale simulations of winter orographic cloud structure and precipitation using the CSU Regional Atmospheric Modeling System (RAMS) allow continuing development of parameterized and explicit modeling techniques to represent microphysical processes. Specific emphasis for the current study is on the role of pollution (aerosol concentrations) upon the initial cloud droplet spectrum and subsequent impacts on ice crystal growth. The initial concentration and size of aerosol particles can strongly modulate the droplet spectrum and degree of riming. An ensemble of RAMS simulations is run with maximum cloud condensation nuclei (CCN) concentrations varying from 100 – 2000 /cm3, so as to examine the impact of pollution on the riming process that contributes significantly to accumulated snowfall. RAMS is run at 600m grid spacing centered over Storm Peak Lab (SPL) and covering the Park Range in northwest Colorado. In addition, a newly developed binned riming approach in RAMS is being incorporated for the first time for these case studies. This binned approach provides realistic collection efficiencies for numerous hydrometeor size categories for the collisions of snow crystals and cloud droplets as riming occurs; this is an improvement over the bulk riming representation that most mesoscale models employ, in which a single riming efficiency is applied to the snow and cloud droplet distributions. The sensitivity studies demonstrate the impact of aerosol concentrations for different airmass characteristics, and the interplay between updraft dynamics, supersaturation, temperature structure across the mountain barrier, and the relative concentrations of cloud droplets and ice crystal types. Case study analysis and verification is accomplished using in-cloud microphysical measurements and snowfall observations that were obtained during February 2005 at SPL and surrounding area of the Park Range. Results of this study are integrated with snow density estimation methods obtained from previous related research collaborations with NWS Grand Junction (GJT) forecasters to improve short-term prediction of snow density and snowfall accumulations. Initially, these result findings are examined on several archived case study events from February 2005 with the Weather Event Simulator (WES) at GJT. Improved algorithms and techniques from this study are then applied to “Smarttools” used in the Graphical Forecast Editor of the Interactive Forecast Preparation System (IFPS) NWS framework.