Synoptic-Scale Predictability of Two Near-Freezing Precipitation Events during the WINTRE-MIX Field Campaign

Wintertime precipitation under near-freezing surface conditions frequently results in costly societal and environmental impacts. Accurately forecasting precipitation type and its transition during these events, however, continues to pose a major challenge for numerical weather prediction models. The WINTRE-MIX (Winter Precipitation Type Research Multi-Scale Experiment) field campaign was conducted during February – March 2022 to observe the multi-scale processes impacting the variability and predictability of precipitation type and amount under near-freezing conditions over northern New York and Southern Québec. Intensive observation periods (IOPs) 4 and 5 of the campaign occurred in association with a broad upper-level trough positioned over Canada and the U.S., whose development was preceded by upper-level ridge amplification over the eastern Pacific and the presence of a strong, persistent surface cyclone over the central Pacific. This synoptic-scale flow pattern facilitated a transition from rain to snow with intervening periods of freezing rain and ice pellets during IOP 4, whereas IOP 5 exhibited a transition from freezing rain and ice pellets to rain with considerable ice accumulations. The timing of the precipitation type transitions that occurred during both IOPs was consistently too slow within operational forecast models at synoptic-scale lead times (2–5 days). This study aims to understand how forecast model representations of dynamical and thermodynamical processes on the synoptic- to mesoscale may have influenced the predictability of precipitation type during these IOPs. To do so, we will use ensemble sensitivity analysis to associate model forecast uncertainty in the low-level temperature and pressure distributions across the St. Lawrence River Valley with uncertainties in the evolution of the upstream synoptic-scale flow pattern several days prior to each IOP.