Improving simulations of precipitation phase and snowpack at a site subject to cold air intrusions: Snoqualmie Pass, WA

Low‐level cold air from eastern Washington often flows westward through mountain passes in the Washington Cascades, creating localized inversions and locally reducing climatological temperatures. The persistence of this inversion during a frontal passage can result in complex patterns of snow and rain that are difficult to predict. Yet these predictions are critical to support highway avalanche control, ski resort operations, and modeling of headwater snowpack storage. In this study we used observations of precipitation phase from a disdrometer and snow depth sensors across Snoqualmie Pass, WA, to evaluate surface‐air‐temperature‐based and mesoscale‐model‐based predictions of precipitation phase during the anomalously warm 2014–2015 winter. Correlations of phase between surface‐based methods and observations were greatly improved (r2 from 0.45 to 0.66) and frozen precipitation biases reduced (+36% to −6% of accumulated snow water equivalent) by using air temperature from a nearby higher‐elevation station, which was less impacted by low‐level inversions. Alternatively, we found a hybrid method that combines surface‐based predictions with output from the Weather Research and Forecasting mesoscale model to have improved skill (r2 = 0.61) over both parent models (r2 = 0.42 and 0.55). These results suggest that prediction of precipitation phase in mountain passes can be improved by incorporating observations or models from above the surface layer. Key Points Cold air intrusions challenge precipitation type partitioning methods in mountain passes Information about atmosphere aloft is required to improve phase prediction at the surface