Predicting the Snow-to-Liquid Ratio of Surface Precipitation Using a Bulk Microphysics Scheme

Abstract

ulk microphysics parameterizations play an increasingly important role for quantitative precipitation forecasting (QPF) in operational numerical weather prediction (NWP). For wintertime, numerical prediction of snowfall amounts is done by applying an estimated snow-to-liquid ratio to the liquid-equivalent QPF from the NWP model. A method has been developed to use prognostic fields from a detailed bulk scheme to predict the instantaneous snow-to-liquid ratio of precipitating snow. By exploiting aspects of the parameterization of the large crystal/aggregate (snow) category, which allow for a prediction of the mean particle size and a corresponding realistic bulk density, combined with pristine ice and graupel fields, the total volume flux of ice-phase precipitation (excluding hail) is computed, independently from the computation of the total solid mass flux. Ultimately, the accumulated unmelted solid precipitation quantity is thus predicted without having to estimate the average snow-to-liquid ratio for a given event, as is typically done for wintertime QPF.The new technique has been implemented into the two-moment version of the Milbrandt?Yau microphysics scheme, which was used in a high-resolution (2.5 and 1 km) NWP modeling system over the Vancouver?Whistler region of Canada in support of forecasting for the Vancouver 2010 Olympic and Paralympic Games. Experimental fields were produced including the instantaneous snow-to-liquid ratio and the snowfall accumulation predicted directly from the scheme using the new approach. Subjective evaluation indicates that the model can discriminate between low-density and high-density snow for instantaneous precipitation. Comparison of the predicted snow-to-liquid ratio to observed climatologies indicates that the scheme produces a realistic probability distribution.