Synoptic-Scale Predictability of a Near-Freezing Precipitation Event during the WINTRE-MIX Field Campaign

The Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) 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 the Saint-Lawrence River Valley. Intensive observation period (IOP) 4 of the campaign occurred 2300 UTC 17 Feb – 1000 UTC 18 Feb 2022 in association with an upper-level trough positioned over the north central U.S., and a surface cyclone that traversed the region along a warm frontal boundary extending northeast of the cyclone. The timing of precipitation type transitions during the event was consistently too slow within operational forecast models at 2-5 day forecast lead times. Consequently, 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 and amount during IOP4. To do so, an ensemble of operational forecasts from the Global Ensemble Forecasting System (GEFS) initialized 5 days prior to IOP4 was divided into three clusters according to the strength and position of the frontal zone over the Saint-Lawrence River Valley during the event. Ensemble sensitivity analysis and spatial composites suggest that differences in the position of the frontal zone between clusters are dynamically linked to differences in the structure of the associated upper-level trough and its associated synoptic-scale dynamics prior to the start of the IOP. Differences in the diabatic erosion of upper-level potential vorticity between clusters also help explain uncertainty in the track of the surface cyclone, the position of its attendant frontal boundaries, and the precipitation type observed across the Saint-Lawrence River Valley. This study demonstrates how uncertainties in the forecast of upper-level conditions prior to near-freezing precipitation events can lead to small differences in the position of associated surface frontal zones, and subsequently, substantial variations in the precipitation type distribution that characterizes an event.