The statistical prediction of local sea surface winds at a number of locations over
the global ocean (Northeast Pacific, Northwest Atlantic and Pacific, tropical Pacific
and Atlantic) is investigated using a surface wind statistical downscaling model based
on multiple linear regression. The predictands (mean and standard deviation of both
vector wind components and wind speed) calculated from ocean buoy observations on
daily, weekly and monthly temporal scales are regressed on upper level predictor fields
(derived from zonal wind, meridional wind, wind speed, and air temperature) from
reanalysis products. The predictor fields are subject to a combined Empirical Orthogonal
Function (EOF) analysis before entering the regression model. It is found that
in general the mean vector wind components are more predictable than mean wind
speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the
difference in predictive skill between mean vector wind components and wind speed is
not substantial. The predictability of wind speed relative to vector wind components
is interpreted by an idealized Gaussian model of wind speed probability density function,
which indicates that the wind speed is more sensitive to the standard deviations
(which generally are not well predicted) than to the means of vector wind component
in the midlatitude region and vice versa in the tropical region. This sensitivity of
wind speed statistics to those of vector wind components can be characterized by a
simple scalar quantity theta=arctan(mu/sigma) (in which mu is the magnitude of average vector
wind and sigma is the isotropic standard deviation of the vector winds). The quantity theta
is found to be dependent on season, geographic location and averaging timescale of
wind statistics.
While the idealized probability model does a good job of characterizing month-to-month
variations in the mean wind speed based on those of the vector wind statistics,
month-to-month variations in the standard deviation of speed are not well modelled.
A series of Monte Carlo experiments demonstrates that the inconsistency in the characterization
of wind speed standard deviation is the result of differences of sampling
variability between the vector wind and wind speed statistics. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4189 |
| Date | 28 August 2012 |
| Creators | Sun, Cangjie |
| 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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