Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction models, such as the Weather Research and Forecasting model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multiscale physical processes. Here we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast United States by varying sea surface temperature (SST) forcings and surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project experiment, are used to constrain the model results, and facilitate quantitative comparisons. Our analysis indicates that while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model's forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in the wind speed's spatial correlation, which underscores the impact of initial and boundary conditions on simulated winds. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the third Wind Forecast Improvement Project.