Statistical downscaling prediction of sea surface winds over the global ocean

• Main Author: Sun, Cangjie
• Other Authors: Monahan, Adam Hugh
• Format: Thesis
• Language: English
• Published: 2012
• Subjects: Statistical downscaling prediction; Sea surface winds; Pacific; Northwest Atlantic
• Online Access: http://hdl.handle.net/1828/4189

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 ...