Accurate modelling of wind flow in complex terrain remains a significant challenge in wind resource assessment. Traditional linear models, such as those used in Wind Atlas Analysis and Application Program (WAsP), often fail to capture non-linear effects like recirculation, separation, and stability-driven phenomena typical of steep or mountainous sites. Computational Fluid Dynamics (CFD) methods based on Reynolds-Averaged Navier–Stokes (RANS) equations offer improved accuracy but must be carefully verified and validated for reliability. This thesis evaluates the predictive accuracy of steady-state RANS simulations using PyWakeEllipSys against field measurements from the Perdigão campaign, characterised by complex double-ridge terrain. Three atmospheric stability regimes (stable, neutral, unstable) were simulated, employing various turbulence closures, including standard k–ε and Monin–Obukhov-based models (k–ε–MO). Grid convergence studies ensured robust simulation accuracy at turbine-relevant heights. Results indicate that unstable conditions are modelled most effectively, particularly in predicting terrain-induced speed-up and turbulence intensity profiles. Stable conditions were reasonably well captured in turbulence intensity and flow patterns but showed consistent underprediction of speedup due to overly persistent recirculation zones. Neutral conditions exhibited inconsistent accuracy across all metrics. Wind direction variability, especially bimodal flow patterns observed in the valley, was not captured by steady-state RANS, highlighting limitations in representing time-dependent, thermally driven flow mechanisms. The outcomes reinforce that steady-state RANS simulations, particularly when stability-adjusted turbulence models are employed, provide strong predictive capabilities for wind resource assessments in complex terrain, although inherent limitations related to transient phenomena must be acknowledged.