Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more efficient wind farms.

In a wind farm, wind turbines decrease the wind speed and add turbulence, reducing the efficiency of turbines behind them and the overall wind farm performance. This study organizes mathematical models and uses computer simulations to examine how turbines interact. The results will help improve wind farm design and better understanding of their environmental effects in the future.

Wind turbines add turbulence to the atmosphere behind them, especially 4–6 diameters downstream and near the rotor top. We propose an equation that predicts the distribution of added turbulence as a function of a turbine parameter (thrust coefficient) and an atmospheric parameter (undisturbed turbulence intensity before the turbine). We find that our equation performs well, although not perfectly. Ultimately this equation can be used to better understand how wind turbines affect the atmosphere.

We use scientific models to study the impact of ship emissions on air quality along the US East Coast. We find an increase in three major pollutants (PM_2.5, NO₂, and SO₂) in coastal regions. However, we detect a reduction in ozone (O₃) levels in major coastal cities. This reduction is linked to the significant emissions of nitrogen oxides (NO_x) from ships, which scavenged O₃, especially in highly polluted urban areas experiencing an NO_x-limited regime. 

Wind turbine wakes are important because they reduce the power production of wind farms and may cause unintended impacts on the weather around wind farms. Weather prediction models, like WRF and MPAS, are often used to predict both power and impacts of wind farms, but they lack an accurate treatment of wind farm wakes. We developed the Jensen wind farm parameterization, based on the existing Jensen model of an idealized wake. The Jensen parameterization is accurate and computationally efficient.