Assessing Variations in the Predictive Skill of Ensemble Snowband Forecasts with Object-Oriented Verification and Self-Organizing Maps

We used object-oriented verification and self-organizing maps (SOMs) to identify patterns in environmental parameters correlating with mesoscale snowband predictive skill by the High-Resolution Ensemble Forecast (HREF) system between 2017 and 2022. First, HREF snowband forecasts for 305 banding events were verified based on similarities between forecast and observed feature properties. HREF members performed comparably, demonstrating large positional errors, but the High-Resolution Rapid Refresh member demonstrated the greatest overall skill. Observed banding events were clustered by 500-hPa geopotential height anomalies, mean sea level pressure, vertical velocity, frontogenesis, and saturation equivalent potential vorticity from the European Center for Medium-Range Weather Forecasts Reanalysis version 5 using SOMs. Clusters reaffirmed the presence of mid-level frontogenesis, ascent, and reduced stability in most banding cases, and the predominant synoptic environments conducive to band development. Clusters were compared to determine whether patterns in the variables were correlated with predictive skill. Strength of upward motion was correlated with skill, with the strongest upward motion cases verifying 10% better than the weakest upward motion cases due to smaller positional error. Additionally, events with a single region of strong upward motion verified better than events with disorganized, but comparably intense, upward motion. Magnitude of frontogenesis was uncorrelated with skill, but events with more upright frontogenesis collocated with the band centroid were better predicted than events with shallower slopes and low-level frontogenesis displaced towards warmer air. The skill variance associated with different vertical motion magnitudes could assist forecasters in modulating forecast confidence, while the most common types of errors may be beneficial to model developers in refining HREF member snowfall forecasts.