Quantitative Evaluation of Reanalysis and Numerical Weather Prediction Models for Wind Resource Assessment in Offshore Environments

Abstract Reanalysis products and numerical weather prediction (NWP) models are widely used for wind resource assessment. However, their differences in environments with limited observations such as offshore regions remain underexplored particularly for metrics beyond near‐surface wind speed. This study compares the performance of two data sets—the 2‐km, NWP‐based National Offshore Wind data set (NOW23) and the global reanalysis data set ERA5—for offshore wind resource assessment. Using lidar‐based offshore wind measurements from three sites across the northeastern United States, 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 mean hub‐height and rotor‐equivalent wind speeds, accounting for wind speed and direction shear (veer). However, NOW23 exhibits higher root‐mean‐square error in rotor‐layer wind speeds and an overly 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 to model resolution and parameterizations, such as ocean surface roughness and coastal thermal gradients, this study provides valuable insights for advancing wind resource modeling and offers guidance on the effective use of numerical models in offshore wind energy development.