Reanalysis products and numerical weather prediction (NWP) models are widely used for wind resource predictions. However, their differences—particularly for metrics beyond wind speeds—remain underexplored, especially for environments with limited observations such as offshore. This study compares the performance of two datasets – the 2-km, NWP-based National Offshore Wind dataset (NOW23) and the global reanalysis dataset ERA5—for wind resource predictions across offshore, coastal, and terrestrial regions. Using near-surface wind observations across the northeastern United States and lidar-based offshore wind measurements, we evaluate key wind energy metrics, including wind speed, wind direction, and their variations (shear and veer) across the rotor layer under different seasonal, diurnal, and atmospheric stability conditions. Results show that NOW23 outperforms ERA5 in simulating near-surface winds for elevated and coastal locations and provides better estimates of mean hub-height and rotor-equivalent wind speeds, accounting for veer, offshore. However, NOW23 exhibits higher absolute errors and an amplified diurnal cycle due to overestimated land-sea temperature contrast. ERA5, on the other hand, systematically underestimates turbine-height wind speeds and overestimates the duration of prolonged wind energy shortages (droughts), which is critical for energy system planning. Both models underestimate wind shear and veer across the rotor layer, particularly under stable atmospheric conditions, though NOW23 performs better than ERA5 under neutral and unstable conditions. By linking these differences between reanalysis and NWP datasets to model resolution, parameterizations, and underlying physics, this study provides valuable insights for advancing wind resource modeling. It also offers guidance on the effective use of numerical models in wind energy development.