As offshore wind energy development accelerates in the U.S., it is important to assess the accuracy of hub- height wind forecasts from numerical weather prediction models over the ocean. Leveraging approximately two years of Doppler lidar observations from buoys in the New York Bight, we evaluate 80-m wind speed forecasts from two weather models: the High-Resolution Rapid Refresh (HRRR) atmospheric model and the Global Forecast System (GFS) coupled atmosphere-ocean model. These models have different horizontal grid spacing, vertical layering, initialization methods, and parameterizations of boundary layer mixing and surface-atmosphere interactions. Despite these differences, the models demonstrate similar and highly skillful short-term forecasts at three measurement sites. At the Hudson Southwest location that provides a full year of data, the performance is statistically indistinguishable: root mean square error RMSE = 2.1 m/s and the Pearson correlation coefficient r = 0.89 for 24-hour forecasts of both models; and RMSE = 2.6 m/s and r >= 0.83 for 48-hour forecasts. Twenty-four-hour forecasts also exhibit skill in predicting quiescent winds and winds associated with maximum turbine power. By Day 10, GFS forecasts on average have almost no skill. Short-term forecast skill by the HRRR and GFS does not strongly depend on season or time of day, yet we find some dependence of the models' performance on near-surface stability. Additionally, 4-14 day forecasts by the GFS exhibit lower RMSE during summer relative to other seasons. The high skill of the HRRR and GFS short-term forecasts establishes confidence in their utility for offshore wind energy maintenance and operation.