This study aims to estimate nearshore wind conditions using multiple numerical models and evaluate their accuracy at heights relevant to offshore wind turbines. An intensive observation campaign was conducted from December 2021 to February 2022 at Mutsu Ogawara Port, Japan. The observed data were used to validate the accuracy of numerical models (mesoscale, computational fluid dynamics (CFD), and linear models) to estimate wind conditions and investigate thermal environments, including atmospheric stability. The results demonstrated that the accuracy of period-averaged wind speed estimation in the offshore direction improved significantly when using an offshore observation point as a reference, with biases within ±2.5% up to 5 km offshore for all models. However, the accuracy of vertical shear estimation varies widely among models, with several models overestimating vertical shear, particularly in the sea wind sector. The mesoscale model, which accounts for spatiotemporal variations in atmospheric stability, consistently achieves high estimation accuracy. In contrast, standalone CFD models, which typically assume neutral atmospheric stability, are difficult to estimate accurately. Nonetheless, incorporating specific atmospheric stability conditions into the CFD models significantly enhanced their accuracy. These findings underscore the importance of atmospheric stability when estimating offshore wind conditions, particularly in nearshore areas.

Wind resource assessment (WRA) in densely forested and complex terrain remains challenging due to strong canopy-induced turbulence and enhanced wind shear, which significantly affect wind flow characteristics and increase modeling uncertainties. Based on a scientific collaboration between Meteodyn and EDF Power, this study proposes a complete and reproducible Computational Fluid Dynamics (CFD) methodology based on an Iterative Model Adjustment (IMA) procedure to improve wind simulation accuracy in highly forested areas using standard industrial inputs. The approach relies on Reynolds-Averaged Navier–Stokes (RANS) simulations implemented in Meteodyn WT™ software. The IMA procedure iteratively adjusts forest model parameters using wind speed profile measurements from a single reference mast until the simulated shear matches observations. The methodology was evaluated across three sites located in Finland, France, and Scotland, resulting in six calibration and cross-prediction cases under heterogeneous forest and complex terrain conditions. Results show that cross-prediction uncertainties were reduced significantly leading to a global mean absolute error of approximately 1.1%. Beyond its practical applicability, the study provides new insight into the physical role and parameter sensitivity of the canopy drag force term within RANS-based forest models. The refined parameterization improves the representation of forest-induced momentum sink effects, leading to enhanced wind speed and vertical shear simulation. These findings demonstrate that robust and accurate Wind Resource Assessment can be performed in complex terrain and forested areas without using advanced remote-sensing-derived canopy density datasets, and thereby offering a pragmatic and industrially applicable alternative.

Wind resource assessment (WRA) in densely forested and complex terrain remains challenging due to strong canopy-induced turbulence and enhanced wind shear, which significantly affect wind flow characteristics and increase modeling uncertainties. Methods relying on Plant Area Density (PAD) or Leaf Area Density (LAD) estimation require costly airborne surveys and site-specific calibration, limiting their industrial applicability. Based on a scientific collaboration between Meteodyn and EDF Power, this study proposes a complete and reproducible Computational Fluid Dynamics (CFD) methodology built around an Iterative Model Adjustment (IMA) procedure implemented in Meteodyn WT™ to improve wind resource assessment accuracy in highly forested areas using standard industrial inputs. The IMA procedure iteratively calibrates the canopy drag coefficient and forest model parameters using wind speed profile measurements from a single reference mast until the simulated wind shear matches observations. The methodology was evaluated at three sites located in Finland, France, and Scotland, yielding six calibration and cross-prediction cases under heterogeneous forest and complex terrain conditions. Cross-prediction uncertainties were reduced significantly, with horizontal mean speed errors decreasing from the range [1.0–9.5%] to [0.5–2.2%] and a global mean absolute error of approximately 1.1%. The study provides new physical insight into the sensitivity of the canopy drag force term within RANS-based forest models, showing that both drag coefficient and canopy height have a comparable and jointly necessary influence on wind shear simulation. These findings demonstrate that robust and accurate wind resource assessment can be achieved in complex terrain and forested areas without relying on remote-sensing-derived canopy density datasets, providing a pragmatic and industrially scalable alternative.