A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. This study used an ensemble modeling approach with 24 setups of the Weather Research and Forecasting (WRF) model to better estimate wind variability, and quantify the role of various modeling components (such as atmospheric input forcing and sea surface temperature input) to the overall ensemble variability.
The full study is available via open access through Wiley.