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 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 February – March in 2022 to observe the multi-scale processes impacting the variability and predictability of precipitation type under near-freezing conditions over northern New York and Southern Québec. Intensive observation periods (IOPs) 4 and 5 of the campaign both featured a broad upper-level trough positioned over Canada and the U.S., whose development was preceded by the amplification of an upper-level ridge over the eastern Pacific and the presence of a strong, persistent surface cyclone over the central Pacific. This synoptic-scale flow pattern led to 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 rain to snow with considerable freezing rain accumulations. The timing of the precipitation type transitions that occurred during both IOPs was consistently too slow among operational forecast models capable of resolving mesoscale processes. 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 plan to evaluate forecast model predictions of vertical temperature profiles across the study region against ground-based observations collected during WINTRE-MIX, and to subsequently associate model forecast errors in the vertical temperature profile with errors in the evolution of the upstream synoptic-scale flow pattern.