The Statistical Predictability of Surface Winds over Western and Central Canada

Abstract

Abstract Multivariate linear regression is used to downscale reanalysis-based midtropospheric predictors (wind components and speed, temperature, and geopotential height) to historical wind observations at 44 surface weather stations during the four calendar seasons. The model performance is assessed as a function of statistical feature of the wind, averaging time scale of the wind statistics, and wind regime (as defined by how variable the vector wind is relative to its mean amplitude). Despite large differences in predictability characteristics between sites, several systematic results are observed: consistent with recent studies, a strong anisotropy of predictability for vector quantities is often observed, although no obvious relation is found between large-scale topographic features and the anisotropy orientation or magnitude. The predictability of time-averaged quantities increases with decreasing averaging time scale. In general, the predictability of mean vector wind components is superior to that of mean wind speeds or subaveraging time scale vector wind variability. These results are interpreted through empirically and theoretically based analyses of the sensitivity of mean wind speed to changes in the vector wind statistics. On longer averaging time scales, the statistical features of the wind speed are found to be highly sensitive to subaveraging time-scale vector wind variability, which is poorly predicted. On shorter averaging time scales, the mean wind speed is found to be highly sensitive to the magnitude of the mean vector wind, a quantity whose predictability can be much lower than the individual mean vector wind components. These results demonstrate limitations to the statistical downscaling of wind speed and suggest that deterministic models that resolve the short-time-scale variability may be necessary for successful predictions.