Evaluating the Performance of Microphysics Schemes against Observations during High-Impact Lake-Effect Snow Events

Abstract In the Great Lakes region (GLR), lake-effect snow (LeS) events are a common occurrence, in which narrow, intense bands of convection cause snowfall downwind of the lakes. The shallow convection associated with LeS is dynamically different from deeper, synoptically driven snow, and the particle size distributions (PSDs) of the precipitation have different shapes, as well. This work considers whether or not the Thompson–Eidhammer microphysics scheme, which includes single-moment prediction of snow, is accurate in estimating the PSDs of LeS convection. The High-Resolution Rapid Refresh (HRRR) configuration of the Weather Research and Forecasting (WRF) Model is used to simulate three different LeS events in the GLR using two different microphysics schemes: the Thompson–Eidhammer “aerosol-aware” scheme and the Morrison double-moment scheme. Model-estimated PSDs are calculated and compared to observed PSDs at three locations in the region: Marquette, Michigan; Gaylord, Michigan; and Buffalo, New York. Model-predicted liquid water equivalent snowfall and snow density are also compared to observed products. It is found that parameterization performance varies depending on location, with Thompson struggling to create the correct PSD shape for Marquette. Both microphysics schemes do not perform well in predicting particles greater than 6 mm in diameter except in Buffalo, where both simulated and observed PSDs contain snow particles greater than 10 mm in diameter. Significance Statement In the Great Lakes region, lake-effect snow events can cause heavy snowfall with large accumulations over a narrow region downwind of the lakes. Current numerical weather prediction models struggle to exactly capture the large spatial variability of lake-effect snow and rely on parameterizations to model the characteristics of the snowfall. This study is the first to use a database of microphysical observations to evaluate how well a current operational forecast model is able to replicate the snow particle sizes in lake-effect snow events around the Great Lakes.