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

Abstract

Abstract 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 non-time-lagged 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 Centre for Medium-Range Weather Forecasts Reanalysis version 5 using SOMs. Clusters reaffirmed the presence of midlevel 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. The 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 toward 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 documented here may be beneficial to model developers in refining HREF member snowfall forecasts. Significance Statement High-resolution numerical weather prediction (NWP) models generally have limited predictive skill for mesoscale snowband forecasts. Even so, some snowbands are forecast by NWP models with much greater skill than others. In this work, we apply artificial intelligence to group snowband events based on atmospheric conditions and then determine whether different groups are easier or harder for models to predict. Identification of these groups could help forecasters know when to trust or be skeptical of NWP output and help developers improve snowband formation processes in NWP models.