the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Mar 2026
The AWAKEN wind farm benchmark, Part 2: Modeling results
Abstract. Accurately modeling wind farm performance in complex atmospheric flows remains a challenge. This paper presents the modeling results of the American WAKE experimeNt (AWAKEN) wind farm benchmark, a collaborative effort involving 16 research groups from academia and industry within the International Energy Agency Wind Technology Collaboration Programme Task 57. The study evaluates a diverse suite of simulation tools, ranging from fast-running engineering wake models to high-fidelity large-eddy simulations, against a diurnal case study observed during the AWAKEN campaign. The benchmark utilized a three-phase structure to progressively assess model performance as observational data availability increased. Initial blind predictions showed that higher-fidelity models did not uniformly outperform simpler simulation tools. A distinct spatial bias was observed where models struggled to resolve the interplay between a low-level jet, wakes, and terrain-induced flow acceleration. In subsequent phases, leveraging additional measurements for model improvement led to a reduction in mean absolute error across the model ensemble; however, this effect was most pronounced in engineering wake models, where targeted calibration reduced error by up to 40 %. Overall, the study demonstrates that inflow characterization remains a primary prerequisite for accuracy, particularly for models relying on coarse forcing datasets. While the limited ability to resolve local terrain-flow interactions under single-day conditions represent a recognized constraint, the overall findings on wake modeling and real-world validation still provide valuable guidance for model application and for mitigating this limitation.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (extended)
- RC1: 'Comment on wes-2026-34', Anonymous Referee #1, 26 Apr 2026 reply
Data sets
AWAKEN wind farm wake benchmark inputs Nicola Bodini https://zenodo.org/records/15623845
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This paper presents a highly valuable and extensive benchmark of wind farm flow models against the unique AWAKEN dataset. The multi-phase, blind approach is a significant strength, offering critical insights into model performance and the value of data for model improvement. The finding that inflow characterization is a primary prerequisite for accuracy is an important, well-supported conclusion. However, the framing of model performance, especially regarding the comparison between engineering and higher-fidelity tools, can be misleading and risks underrepresenting the fundamental research challenges that this benchmark uniquely exposes. Specific comments are as follows:
1. The manuscript makes statements such as "initial blind predictions showed that higher-fidelity models did not uniformly outperform simpler simulation tools" (Abstract) and "simpler engineering and steady-state models often matched or outperformed higher-fidelity mesoscale approaches" (Conclusions). While factually correct in terms of the bulk error metrics (e.g., MAE) for this specific case, this framing can be misleading without critical context.
2. The authors correctly note that engineering models directly ingested high-quality, single-point observations (from site A1) in Phase 1. Their performance is therefore not a triumph of simplified physics, but a demonstration of the effectiveness of empirical calibration against a known inflow. In contrast, the higher-fidelity models (WRF, LES) were tasked with a much harder problem: predicting the inflow ab initio from coarser boundary conditions. Their errors are primarily "inflow errors," not necessarily "physics errors" within the wake model. The text should more clearly distinguish between the performance of a model's inflow characterization strategy and its wake physics fidelity. Suggesting that an engineering model "outperforms" an LES model conflates a site-calibrated tool with a predictive one.
3. The results of this benchmark expose profound, fundamental research challenges for high-fidelity modeling that are mentioned but not centered as key findings. The paper should more forcefully articulate these challenges as critical outcomes of the study:
4. The stable case (06:00 UTC) demonstrates the breakdown of Monin-Obukhov Similarity Theory (MOST) (as noted for Participant 6), placing the turbine rotor layer outside the surface layer. Standard RANS models, which rely heavily on equilibrium boundary layer assumptions, are fundamentally challenged by such non-canonical conditions (e.g., low-level jets). The manuscript should explicitly state and discuss the crucial need for improved turbulence closures for wind energy applications.
5. LES is often driven by mesoscale simulations, which do not have turbulence content. This underscores the challenge of generating realistic, turbulent, site-specific inflows for LES, particularly in complex terrain where standard periodic boundary conditions or simplified precursor methods fail. It is suggested to discuss this issue in the revised paper.
6. The observation that terrain-induced flow acceleration frequently outweighed wake losses (e.g., at Site H) and caused specific waked turbines to overproduce is a critical finding. This demonstrates that resolving microscale terrain features is not just a detail but a first-order priority on par with wake parameterization. In LES of atmospheric turbulent flows, the near-wall region is often modelled rather than directly resolved. The classic wall model depends on the logarithmic law of the wall, which, however, fails in complex. This challenge should be highlighted in the paper as a fundamental research challenge.
7. The conclusion section can be strengthened by distilling the key results from the points above into a clear summary of high-priority, fundamental research needs, including such as inflow characterization and modeling, non-equilibrium and non-canonical boundary layer physics, and multiscale coupling of terrain and wake effects.
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