An Evaluation of Snowband Predictability in the High-Resolution Rapid Refresh Weather and Forecasting

Narrow regions of intense, banded snowfall present hazardous travel conditions due to rapid onset, high precipitation rates, and lowered visibility. Despite their importance, there are few verification studies of snowbands in operational forecast models. The objective of this study is to evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to predict snowbands in the United States east of the Rocky Mountains. An automated band-detection algorithm was applied to a 3-yr period of simulated and observed radar reflectivity to compare snowband climatologies. This algorithm uses the distributions of reflectivities in contiguous precipitation regions to determine a band intensity threshold. The predictability of snowbands on a case-by-case basis was also evaluated using an object-oriented approach. The distribution of HRRR forecast banding resembles that of the observations, but with a significant positive frequency bias. This may partially be due to underrepresentation of observed bands in our verification dataset due to limited radar coverage in portions of the central United States. On a case-by-case basis, traditional skill metrics indicate limited predictability, but allowing for small timing discrepancies dramatically improves scores. Object-oriented verification yields mixed results, with 30% of forecasts receiving a score indicative of a well-predicted event. However, 69% of cases have at least one forecast lead demonstrating skill, suggesting the HRRR is successful in depicting environments conducive to band formation. These results suggest adopting a probabilistic, ensemble approach, and indicate that the deterministic HRRR is best suited for the identification of regions of elevated snowband risk and not precise timing or location information. Grant no. NA16NWS4680003