Large-Scale Errors and Mesoscale Predictability in Pacific Northwest Snowstorms

Abstract

Abstract The predictability of lowland snow in the Puget Sound region of the Pacific Northwest is explored by analyzing the spread in 100-member ensemble simulations for two events from December 2008. Sensitivities to the microphysical and boundary layer parameterizations in these simulations are minimized by estimating the likely precipitation type from the forecast 850-hPa temperatures and the established rain–snow climatology. Results suggest that the ensemble spread in events such as these, which were triggered by amplifying short waves, may develop a significant fraction of both rain-likely members and snow-likely members at forecast lead times as short as 36 h. The perturbation kinetic energy of the ensemble members about the ensemble mean () is not maximized at small scales. Instead, the power in the initial spectrum of produced by the authors’ ensemble Kalman filter (EnKF) data assimilation cycle increases with increasing horizontal scale. The power in subsequently grows with time, while maintaining approximately the same spectral shape. There is no evidence of small-scale perturbations developing rapidly and transferring their influence upscale. Instead, the large-scale perturbations appear to grow more rapidly during the first 12 h than those at the smallest resolved scales.