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Abstract Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state critical-level regime in comparison to other downslope windstorm–generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the United States and annually averaged for each year between 2008 and 2019. 

Abstract Linear theory has long been used to study mountain waves and has been successful in describing much of their behaviour. In the simplest theoretical context, that of two‐dimensional steady‐state flow with constant Brunt–Väisälä frequency (N) and horizontal wind speed (U), finite‐amplitude effects are relatively minor until wave breaking occurs. However, in more complex environmental profiles, significant finite‐amplitude effects occur below the wave‐breaking threshold. We constructed a linearized version of a fully nonlinear time‐dependent model, thereby facilitating direct comparisons between linear and finite‐amplitude solutions in cases with upstream profiles representative of typical real‐world events. Beginning with the simplest profile that includes a tropopause, namely an environment with constant upstream wind speed and two layers of constant static stability, we progressively investigate more complex profiles that include vertical wind shear typical of the midlatitude westerlies. Our results demonstrate that, even without wave breaking, finite‐amplitude effects can play an important role in modulating the mountain‐wave amplitude and gravity‐wave drag. The modulation is a function of the tropopause height and is most pronounced when the cross‐ridge flow increases strongly with height. 

Abstract We investigate the sensitivity of mesoscale atmospheric predictability to the slope of the background kinetic energy spectrum E by adding initial errors to simulations of idealized moist midlatitude cyclones at several wavenumbers k for which the slope of E (k) is significantly different. These different slopes arise from 1) differences in the E (k) generated by cyclones growing in two different moist baroclinically unstable environments, and 2) differences in the horizontal scale at which initial perturbations are added, with E (k) having steeper slopes at larger scales. When small-amplitude potential temperature perturbations are added, the error growth through the subsequent 36-h simulation is not sensitive to the slope of E (k) nor to the horizontal scale of the initial error. In all cases with small-amplitude perturbations, the error growth in physical space is dominated by moist convection along frontal boundaries. As such, the error field is localized in physical space and broad in wavenumber (spectral) space. In moist midlatitude cyclones, these broadly distributed errors in wavenumber space limit mesoscale predictability by growing up-amplitude rather than by cascading upscale to progressively longer wavelengths. In contrast, the error distribution in homogeneous turbulence is broad in physical space and localized in wavenumber space, and dimensional analysis can be used to estimate the error growth rate at a specific wavenumber k from E (k). Predictability estimates derived in this manner, and from the numerical solutions of idealized models of homogeneous turbulence, depend on whether the slope of E (k) is shallower or steeper than k^ −3 , which differs from the slope-insensitive behavior exhibited by moist midlatitude cyclones.