Evaluating the Performance of Operational Microphysics Parameterizations Using Observations During Significant Lake-Effect Snow Events

In the Great Lakes Region (GLR), lake-effect snow (LeS) events are a common occurrence, in which narrow, intense bands convection cause snowfall downwind of the lakes. LeS events can be high-impact and difficult to forecast, with snowfall rates as high as 6 in. hr.^-1 in extremely localized areas. 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. The question asked in this study is whether or not the Thompson-Eidhammer microphysics scheme (currently used in the operational High-Resolution Rapid Refresh, or HRRR, model), which includes single-moment prediction of snow, is accurate in estimating the PSDs of LeS convection.

The HRRR configuration of the Weather Research and Forecasting model (WRF) 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, Gaylord, and Buffalo. 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: Thompson does well in Buffalo and Gaylord, but struggles creating the correct PSD shape often seen in Marquette. While Morrison does better estimating greater counts of large particle sizes (diameter > 4 mm), both microphysics schemes do not perform well in predicting particles greater than 6 mm in diameter.