Exploring Lake-Enhanced Snowfall Events Using Ground-Based Observations and Numerical Weather Prediction Models (Invited)

The Great Lakes region experiences a diverse range of snowfall types, heavily influenced by proximity to the lakes, often resulting in extreme weather conditions. Lake-enhanced snow systems, which involve significant synoptic snowfall events intensified by lake surface and shore topographic processes, are notable for their long duration and intensity. Accurately estimating snow precipitation with radar-based quantitative precipitation estimates (QPE) is challenging due to the complex microphysics involved in these events. Additionally, numerical weather prediction (NWP) models often struggle with pinpointing the exact location, intensity, and duration of snow, complicating quantitative precipitation forecasting. Recent research using ground-based observations from the Micro Rain Radar (MRR) and the Precipitation Imaging Package (PIP) has revealed abrupt shifts in local macro- and microphysical attributes associated with these enhanced snow systems. These shifts lead to significant increases in snow particle size distributions and snow-liquid ratios as synoptic systems traverse the Great Lakes region. To address these challenges, we integrate surface-based observations from the NWS Marquette, MI office with NWP model simulations. This approach aims to improve our understanding of the regional cloud microphysics, boundary layer thermodynamics, and surface conditions that drive these impactful lake-enhanced snow systems and their unique microphysical properties. Our goal is to improve our understanding of the environmental factors that trigger and amplify snowfall rates and particle sizes to forecast snowfall accumulation more effectively.