pls see the attachment.

### Review of WES-2025-32

**Impact of inflow conditions and turbine placement on the performance of offshore wind turbines exceeding 7 MW**

#### Authors

Konstantinos Vratsinis

Rebeca Marini

Pieter-jan Daems

Lukas Pauscher

Jeroen van Beeck

Jan Helsen

#### Overview:

#### Overview

This manuscript investigates the correlation between atmospheric inflow parameters—namely wind shear, veer, and turbulence intensity—and offshore wind turbine power production. The analysis is framed in the context of IEC-recommended normalization, with the goal of exploring performance dependencies across wind speed bins. The topic is clearly relevant for wind energy research and operations, particularly as offshore turbines exceed 7 MW in rated capacity and sit within more variable marine boundary layer conditions.

However, it is not clear what novel contribution this manuscript makes to the field or the central hypothesis being investigated. IEC normalization approaches are not intended to serve as predictive models, but rather to establish standard design and compliance conditions. The fact that these normalized parameters correlate weakly with power production is not necessarily surprising, and the authors need to contextualize their work more rigorously. If the intent of the paper is to show that IEC-style normalization underrepresents atmospheric variability or leads to weak statistical connections to power, that point must be made explicit and defended with clear analysis. Even better would be to outline a meaningful improvement to the approach outlined in the IEC Standards.

In its present form, the paper suffers from several methodological and framing issues that limit its scientific clarity. Many of the assumptions are not justified, key steps in the analysis are missing or difficult to understand, and several potentially confounding factors are not adequately discussed. 

#### Comments

- The nacelle-mounted lidar appears to be located much farther from the wind plant than indicated in the text. Figure 1 suggests a distance closer to 18–20 km rather than the 8 km noted. This large separation raises serious concerns about the representativeness of the inflow measurements used in the correlation analysis. The authors should justify the use of this lidar system for characterizing inflow to the plant or clearly state the limitations that this introduces.

- The manuscript lacks sufficient discussion of existing literature on nacelle-mounted lidar systems and their associated uncertainties, especially for continuous-wave systems used at long range. Important work should be cited and discussed to help the reader assess the quality and validity of the measurement inputs.  Letizia et al., 2021, is a good place to begin.

- The authors assume that wind shear and veer estimates from the distant lidar are representative of conditions across the entire wind plant. This is a strong assumption, especially in the offshore context, where wind direction and shear can vary significantly across distances of several kilometers. The authors should validate this assumption using available SCADA data, such as nacelle wind speed or yaw measurements, or else explicitly acknowledge and try to quantify the uncertainty introduced by this assumption.

- The data selection process is not fully described. Specifically, it is not clear whether or how dynamic atmospheric events (e.g., wind speed ramps, gusts, frontal passages, etc.) are filtered out of the data set. These events can have a strong impact on the correlation strength between inflow and power production. The authors should consider filtering based on time series dynamics (e.g., Hamilton, 2020) to isolate quasi-stationary atmospheric periods. This is likely to produce stronger correlations between observed quantities.

- In Figure 1, several turbines within the wind plant are shown in gray and appear to be excluded from the analysis, but the reason for this exclusion is not stated. Similarly, if there are turbines located closer to the source of atmospheric measurements, perhaps they should be considered instead.

- The paper mentions the use of DB-SCAN for outlier detection but provides no mathematical description or reference. At a minimum, a citation should be added, along with a brief description of how the DB-SCAN parameters were selected. Moreover, it appears that the DB-SCAN filtering may bias the power deviation metric discussed in Section 3.2. By excluding data points that deviate from the nominal power curve, the authors may artificially reduce the observed variability. This potential bias should be acknowledged and discussed.

- The analysis sector from 180° to 285° includes turbines that are likely to be in the wake of upwind turbines. The authors should discuss the extent to which wake effects influence turbulence intensity estimates and correlation strength. It may be appropriate to apply a wake-added turbulence model or include a wake filter to avoid spurious correlations driven by wake-induced variability.

- The correlation analysis itself is not clearly explained. The use of a 0.75 m/s sliding window appears central to the results, but it is not described in sufficient detail. Does the window represent a binning interval for mean values, a pairwise filter on measurement differences, or something else? How are variables with different units (e.g., shear, veer, TI) handled in this scheme? Further, what range of wind speeds is included in the analysis, and how is the resolution of the sliding window chosen? The authors may wish to define the correlation structure in terms of normalized wind speed (e.g., $U/U_{rated}$) to provide more generalizable insight.

- Figures 4–11 present a variety of correlations between atmospheric parameters and power output or power deviation, but the organization of these figures is unclear. The alternation between absolute power and deviation metrics should be explained more explicitly. In most cases, the correlation coefficients are below 0.4. While low correlations are not inherently uninteresting, the authors should explore and discuss the potential reasons for these weak results. For instance, are the variables themselves weakly coupled, is the inflow poorly characterized, or are key intermediate variables missing from the analysis? The paper would benefit from a thoughtful discussion of these points, which could help inform future efforts to better predict wind turbine performance from inflow metrics.