A statistical downscaling approach based on multiple linear-regression is used to investigate the predictability of land surface winds over the Canadian prairies and Ontario. This study's model downscales mid-tropospheric predictors (wind components and speed, temperature, and geopotential height) from reanalysis products to predict historical wind observations at thirty-one airport-based weather surface stations in Canada. The model's performance is assessed as a function of: season; geographic location; averaging timescale 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 observed, while some components are generally well predicted, others have no predictability. The predictability of mean quantities is greater on shorter averaging timescales. In general, the predictability of the surface wind speeds over the Canadian prairies and Ontario is poor; as is the predictability of sub-averaging timescale variability. These results and the relative predictability of vector and scalar wind quantities are interpreted with theoretically- and empirically-derived wind speed sensitivities to the resolved and unresolved variability in the vector winds. At most sites, and on longer averaging timescales, the scalar wind quantities are found to be highly sensitive to unresolved variability in the vector winds. These results demonstrate limitations to the statistical downscaling of wind speed and suggest that deterministic models which resolve the short-timescale variability may be necessary for successful predictions. / Graduate

The Georgia General Assembly, like many states, has enacted pre-defined, comprehensive, drought-mitigation apparatus, but they need rainfall outlooks. Global circulation models (GCMs) provide rainfall outlooks, but they are too spatially course for jurisdictional impact assessment. To wed these efforts, spatially averaged, time-smoothed, daily precipitation observations from the National Weather Service cooperative network are fitted to eight points of 700 mbar atmospheric data from the NCEP/NCAR Reanalysis Project for climate downscaling and drought prediction in the Apalachicola-Chattahoochee-Flint (ACF) river basin. The domain is regionalized with a factor analysis to create specialized models. All models complied well with mathematical assumptions, though the residuals were somewhat skewed and flattened. All models had an R-squared > 0.2. The models revealed map points to the south to be especially influential. A leave-one-out cross-validation showed the models to be unbiased with a percent error of < 20%. Atmospheric parameters are estimated for 2008–2011 with GCMs and empirical extrapolations. The transfer function was invoked on both these data sets for drought predictions. All models and data indicate drought especially for 2010 and especially in the south.