Predictive Anisotropy of Surface Winds by Linear Statistical Prediction




Mao, Yiwen
Monahan, Adam H.

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Journal of Climate


This study considers characteristics of the statistical predictability of surface wind vectors by linear regression using midtropospheric climate fields as predictors. Specifically, predictive anisotropy, which refers to unequal predictability of wind components projected onto different directions, is considered. The spatial distribution of predictability of surface wind components is determined at 2109 land surface meteorological stations across the globe. The results show that predictive anisotropy is a common feature that is spatially organized in terms of both magnitude and direction. The relationships between predictability and potential influential factors (topographic complexity, mean surface wind vectors, and standard deviation and kurtosis of wind components) are considered. It is found that poor predictability of wind components is generally associated with wind components characterized by relatively weak and non-Gaussian variability. While predictive anisotropy is often found in regions characterized by complex topography, marked predictive anisotropy also occurs away from evident surface heterogeneity. The relationships between predictability, variability, and shape of distribution of surface wind components are described using an idealized statistical model of large-scale and local influences on surface wind.



Regression analysis, Statistics, Wind effects


Mao, Y. & Monahan, A. (2017). Predictive Anisotropy of Surface Winds by Linear Statistical Prediction. Journal of Climate, 30(16), 6183-6201.