Modeling the Atmospheric Boundary Layer for Wind Power

Understanding the details of locale-specific flow in the atmospheric boundary layer (ABL) is critical to both siting wind power plants and to making short term predictions of wind variability. The goal of this work is to demonstrate that assimilating the output of a Mesoscale Numerical Weather Prediction (NWP) model into a Computational Fluid Dynamics (CFD) model can improve the fine scale structure that can be modeled in the ABL. Mesoscale NWP with data assimilation has been used extensively to study mesoscale flow features. Scales finer than about 1 km, however, are not well captured by the turbulence physics of such models. CFD models have proven useful at those smaller scales, capturing the details of flow around features such as buildings and fine-scale topography. This work describes how the output of the WRF NWP model with four dimensional data assimilation (FDDA) is used to initialize and assimilate into CFD simulations with much finer grid spacing. Assimilating the NWP model data is critical to obtaining the spatially varying outer scale patterns. The CFD model is then able to accurately model flow around fine scale topographic features. In addition, a porosity model simulates the effects of vegetation, causing a more turbulent local surface layer. The technique is demonstrated in a case study. This approach of combining state-of-the-art techniques in NWP, CFD, and data assimilation provides a unique assessment of the utility of specific locations for wind power production.