Linear and nonlinear regression prediction of surface wind components
Date
2018
Authors
Mao, Yiwen
Monahan, Adam H.
Journal Title
Journal ISSN
Volume Title
Publisher
Climate Dynamics
Abstract
This study compares the statistical predictability by linear regression of surface wind components using mid-tropospheric
predictors with predictability by three nonlinear regression methods: neural networks, support vector machines and random
forests. The results, obtained at 2109 land stations, show that more complex nonlinear regression methods cannot substantially
outperform linear regression in cross-validated statistical prediction of surface wind components. As well, predictive
anisotropy (variations in statistical predictive skill in different directions) are generally similar for both linear and nonlinear
regression methods. However, there is a modest trend of systematic improvement in nonlinear predictability for surface wind
components with fluctuations of relatively small magnitude or large kurtosis, which suggests weak nonlinear predictive
signals may exist in this situation. Although nonlinear predictability tends to be higher for stations with low linear predictability
and nonlinear predictive anisotropy tends to be weaker for stations with strong linear predictive anisotropy, these
differences are not substantial in most cases. Overall, we find little justification for the use of complex nonlinear regression
methods in statistical prediction of surface wind components as linear regression is much less computationally expensive
and results in predictions of comparable skill.
Description
Keywords
Statistical prediction, Linear regression, nonlinear regression, Predictability of surface winds
Citation
Mao, Y., & Monahan, A. H. (2018). Linear and nonlinear regression prediction of surface wind components. Climate Dynamics, 51, 3291-3309. https://doi.org/10.1007/s00382-018-4079-5.