Convection-Permitting Ensemble Forecasts of the 10–12 December 2013 Lake-Effect Snow Event: Sensitivity to Microphysical Parameterizations

Lake-effect snow (LeS) presents a substantial forecast challenge for convection-permitting models, due in part to uncertainties in the parameterization of microphysical processes. Here we focus on understanding these uncertainties for an LeS event that occurred during 10–12 December 2013 as part of the Ontario Winter Lake-effect Systems (OWLeS) field campaign. Throughout this event, long-lake-axis-parallel snowbands persisted downwind of the eastern shore of Lake Ontario, leading to snowfall accumulations as high as 101.5 cm (liquid precipitation equivalent of 62.5 mm) on the Tug Hill Plateau.

We run nested simulations of the 10–12 December 2013 LeS event at 12-, 4-, and 1.33-km horizontal grid spacing using the Weather Research and Forecasting (WRF) model configured in the same manner as the High-Resolution Rapid Refresh model. Sensitivity experiments are conducted by simulating the event multiple times with different microphysical schemes. The primary difference between these microphysics experiments is found in the LeS band intensity and morphology, with relatively smaller differences arising in the band position. Maximum storm-total liquid precipitation equivalent amounts among microphysics ensemble members range from 30 to 60 mm.

Results from the WRF simulations are compared to detailed observations from OWLeS, including NEXRAD radar data and surface snowfall and crystal habit observations. Measurements from the University of Wyoming King Air aircraft, including vertically pointing cloud radars and in-situ flight-level thermodynamic and microphysical observations, are used to compare observed and modeled cloud structures. By analyzing the cloud microphysical properties, such as the supercooled liquid water content, hydrometeor size distributions, and precipitation fallspeed, we reconcile the observed and modeled LeS band structures and assess which microphysics schemes are most accurately simulating cloud processes during this LeS event.