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Current wind turbine simulations successfully use turbulence generating toolsfor modeling behavior. However, they lack the ability to reproducevariabilities in wind dynamics and inherent stochastic structures (liketemporal and spatial coherences, sporadic bursts, high shear regions). Thisnecessitates a more realistic parameterization of the wind that encodeslocation-, topography-, diurnal-, seasonal and stochastic affects. In thiswork, we develop a hierarchical temporal and spatial decomposition (TSD) oflarge-scale meteorology data to construct a low-dimensional yet realisticstochastic wind flow model. Starting from a large data set (Crop Wind-energyEXperiment), a low dimensional stochastic parameterization is constructed. Theresulting time-resolved stochastic model encodes the statistical behaviorexhibited by the actual wind flow. Moreover, the temporal modes encode thevariation of wind speed in the mean sense and resolve diurnal variations andtemporal correlation while the spatial modes provide deeper insight intospatial coherence of the wind field - which is a key aspect in current windturbine sizing, design and classification. Comparison of several importantturbulent properties between the simulated wind flow and the original datasetshow the utility of the framework. We envision this framework as a usefulcomplement to existing wind simulation codes.

Constructing low-dimensional stochastic wind models through hierarchical spatial temporal decomposition