Snow-to-Liquid Ratios in the Upper Great Lakes: Regime Dependency and Nowcasting Applications

The snow-to-liquid ratio (SLR), whereby the liquid equivalent amount of snowfall is converted to its geometric/accumulation depth, is a crucial winter weather forecasting parameter. In this research project, a 2015-2023 dataset of snowfall measurements from the Precipitation Image Packaging (PIP) and Micro Rain Radar (MRR) instruments located at the Marquette, Michigan National Weather Service was analyzed to quantify SLR variability at various time scales. Additionally, SLR variability associated with different Great Lakes snowfall regimes (e.g., lake-effect, lake-orographic, lake-enhanced, and synoptic) was also examined. Regime partitioning was performed using MRR reflectivity profiles, NEXRAD and GOES observations, and other ancillary datasets. The SLR results created from 1-minute PIP observations offer important refinements to previous SLR studies based on manual 6-hour and 24-hour snowfall measurements and confirm considerable regime-dependent SLR variability. For example, while the PIP analysis shows that synoptic snow events possess mean SLR values near the conventionally applied 10:1 value, lake-effect (~15:1) and lake-orographic (~15:1) snow events have systematically higher mean SLR and display considerably more variability. Extremely high SLR values exceeding 30:1 are also documented. Further variability within each regime is evident on different time scales (e.g., 6-hour, 24-hour, monthly, interannually). Comparisons between PIP and manually measured SLRs, as well SLRs derived from short term forecasting methods (e.g., the Cobb Method and Kuchera ratio), will also be presented. This study illustrates that SLR values derived from combined PIP and MRR observations not only provide a robust way to quantify regime-dependent SLR values, but also demonstrate how SLR derived from ground-based observations at a high temporal cadence can benefit short-term forecasting applications.