Representation of Blowing Snow and Associate Visibility Reduction in an Operational High-Resolution Weather Model

Blowing snow is an issue for motorists on roads because of its rapid reduction of visibility. High-resolution weather prediction models do not capture the movement of snow once it has reached the ground, but visibility reductions due to blowing snow can be predicted based on model-resolved land surface and environmental conditions that correlate with blowing snow occurrence. Recently, a diagnostic framework for forecasting blowing snow concentration and the associated visibility reduction was developed. This framework empirically determines the blowing snow flux in the saltation layer and assumes a power law to represent the blowing snow flux above this thin layer. This study applies this framework to High Resolution Rapid Refresh model output to predict blowing snow related surface visibility reduction for eight events from 2018 to 2023 with various background meteorological conditions and intensities. This analysis shows that visibility reductions due to blowing snow are overpredicted, caused by the fact that the framework assumes full driftability of the snowpack. This study refines this framework with a method that diagnoses snowpack driftability based on the recent (≤ 12 h) history of snowfall, wind, and skin wet-bulb temperature. We used three different ad hoc “weighting” methods to assess how driftable the recently fallen snow is. This driftability implementation show minor improvements especially in the upper thresholds of visibility. To make further progress with the model-based diagnosis of blow-snow-induced visibility reductions, empirical work is needed to determine the relation between snowpack driftability and the recent history of precipitation and other weather conditions.