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
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3908 |
| Date | 26 April 2012 |
| Creators | Culver, Aaron Magelius Riis |
| Contributors | Monahan, Adam Hugh |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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