Predicational biases investigation between the WRF and the COAWST models for offshore wind resource

The mesoscale numerical weather predictions have become crucial in offshore floating wind turbine design and installation. Therefore, rigorous assessments of the calculated model are essential for successfully integrating offshore renewable wind energy into existing complicated power systems. Utilizing a LiDAR buoy deployed off the coast of New York in 2019, we focus on evaluating the predictive performance of an ocean modeling system (COAWST: Coupled Ocean Atmosphere Wave Sediment Transport). This work also reports the numerical bias between the stand-alone atmospheric WRF model and the COAWST model at multiple heights above sea level during the summer season in the same ocean spots. The results indicate that both the time series of wind velocity and the average velocity calculated by the COAWST model demonstrate greater accuracy compared to the standalone atmospheric WRF ensemble model. Specifically, the average wind speed magnitude calculated by the COAWST model at 18 meters over 72 hours shows only a 2.39% error when compared to the LiDAR data.