Statistical predictability of surface wind components

dc.contributor.authorMao, Yiwen
dc.contributor.supervisorMonahan, Adam Hugh
dc.date.accessioned2017-12-11T19:32:46Z
dc.date.available2017-12-11T19:32:46Z
dc.date.copyright2017en_US
dc.date.issued2017-12-11
dc.degree.departmentSchool of Earth and Ocean Sciencesen_US
dc.degree.levelDoctor of Philosophy Ph.D.en_US
dc.description.abstractPredictive anisotropy is a phenomenon referring to unequal predictability of surface wind components in different directions. This study addresses the question of whether predictive anisotropy resulting from statistical prediction is influenced by physical factors or by types of regression methods (linear vs nonlinear) used to construct the statistical prediction. A systematic study of statistical predictability of surface wind components at 2109 land stations across the globe is carried out. The results show that predictive anisotropy is a common characteristic for both linear and nonlinear statistical prediction, which suggests that the type of regression method is not a major influential factor. Both strong predictive anisotropy and poor predictability are more likely to be associated with wind components characterized by relatively weak and non-Gaussian variability and in areas characterized by surface heterogeneity. An idealized mathematical model is developed separating predictive signal and noise between large-scale (predictable) and local (unpredictable) contributions to the variability of surface wind, such that small signal-to-noise ratio (SNR) corresponds to low and anisotropic predictability associated with non-Gaussian local variability. The comparison of observed and simulated statistical predictability by Regional Climate models (RCM) and reanalysis in the Northern Hemisphere indicates that small-scale processes that cannot be captured well by RCMs contribute to poor predictability and strong predictive anisotropy in observations. A second idealized mathematical model shows that spatial variability in specifically the minimum directional predictability, resulting from local processes, is the major contributor to predictive anisotropy.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/8852
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectstatistical predictionen_US
dc.subjectpredictability of surface wind vectorsen_US
dc.subjectlinear regressionen_US
dc.subjectnonlinear regressionen_US
dc.subjectregional climate modelsen_US
dc.subjectpredictive anisotropyen_US
dc.titleStatistical predictability of surface wind componentsen_US
dc.typeThesisen_US

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