# A multi-scale model with an atmosphere - soil - vegetation coupling to explore the effect of wind turbines on near-surface meteorological conditions

Paul Boumendil, Quentin Rodier, Pierre-Antoine Joulin, Valéry Masson

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Paul Boumendil, Quentin Rodier, Pierre-Antoine Joulin, Valéry Masson. A multi-scale model with an atmosphere - soil - vegetation coupling to explore the effect of wind turbines on near-surface meteorological conditions. Journal of Physics: Conference Series, 2025, 3016, pp.012002. 10.1088/1742-6596/3016/1/012002. [hal-05131507](https://HAL Id: hal-05131507)

Submitted on 26 Jun 2025

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# A multi-scale model with an atmosphere - soil - vegetation coupling to explore the effect of wind turbines on near-surface meteorological conditions

To cite this article: Paul Boumendil et al 2025 J. Phys.: Conf. Ser. 3016 012002

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# A multi-scale model with an atmosphere - soil - vegetation coupling to explore the effect of wind turbines on near-surface meteorological conditions

Paul Boumendil¹,², Quentin Rodier², Pierre-Antoine Joulin¹ and Valéry Masson²

¹IFP Energies Nouvelles, 1-4 Avenue du Bois Préau, 92852 Rueil-Malmaison, France

²Météo France, CNRM, 42 Avenue Gaspard Coriolis, 31057 Toulouse, France

E-mail: paul.boumendil@meteo.fr

## Abstract.

Numerical simulations, satellite measurements, and field measurements campaigns have demonstrated that wind turbine wakes can lead to changes in near-ground air temperature and humidity. However, in high-resolution LES simulations aimed at reproducing and better understanding these phenomena, the incident flows and surface conditions used are often idealized, and therefore diverging from the real-world conditions faced by wind turbines. In this study, we propose an online coupling between the realistic atmospheric model Meso-NH, a soil-vegetation-atmosphere transfer model, and an Actuator Disk with Rotation. The characteristics of the generated atmosphere and the meteorological variations induced by the wind turbine are validated using measurements from the VERTEX campaign conducted on a single 2MW wind turbine. The results reveal wake-induced variations in temperature and humidity near the ground under stable and unstable conditions, while no significant changes are observed for neutral conditions.

## 1 Introduction

Wind turbine wakes alter the natural stratification of the atmosphere, leading to local modifications of meteorological properties downwind of wind farms. Changes in temperature, humidity, turbulence, and heat fluxes have been observed using numerical simulations [1, 2, 3, 4, 5], satellite measurements [6], and field measurement campaigns [7, 8, 9, 10, 11]. Some of these meteorological variations could affect the production, loads, and lifespan of downstream wind turbines (WT). Moreover, such meteorological variations are not limited to the rotor area but also extend to near-ground air, raising questions about their influence on vegetation and land cover [4, 7]. Therefore, a better understanding of these phenomena is essential, not only to optimize wind farm production and durability but also to assess their environmental impact.

To better understand the physical mechanisms behind these near-ground meteorological variations, Large-Eddy Simulations (LES) coupled with actuator models have been widely used [3, 4, 12, 13]. However, these simulations typically rely on arbitrary initial atmospheric and surface conditions, resulting in a steady and synthetic representation of the ground and of the atmosphere. Such conditions fail to capture the dynamic nature of the real atmosphere, which evolves across multiple scales and exhibits temporal and spacial heterogeneity in stability, turbulence, flux, wind speed, and direction. Because wind turbine wake characteristics and near-ground atmospheric properties strongly depend on these atmospheric

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and surface conditions, we propose a multi-scale online coupling approach. This framework integrates a realistic atmospheric model, a soil-vegetation-atmosphere transfer model, and an Actuator Disk with Rotation (ADR), following the recommendations of Porté-Agel (2019) [14]. Such couplings have been used in the past, particularly to study wind turbine interactions with complex terrain [15, 16, 17, 18] or specific atmospheric phenomena [19]. Using atmospheric grid-nested domains, we simulate scales ranging from the mesoscale, capturing diurnal cycles, to the microscale, resolving the flow behavior around wind turbines while accounting for realistic features such as orography, surface cover, clouds, and radiation.

The primary objective of this study is to validate whether our multi-scale coupling can accurately reproduce the meteorological conditions of a wind farm site. The secondary objective is to use these realistic atmospheres to analyze and compare wind turbine-induced meteorological variations with the VERTEX campaign. In this paper, we first introduce the VERTEX measurement campaign and outline its main objectives. The numerical setup used to replicate realistic atmospheric dynamics is then described. The resulting conditions, generated by the multiscale model, are compared with measurements, followed by an analysis of near-ground meteorological variations induced by wind turbines. Finally, we discuss the results and conclude with the key findings of this study.

## 2 Case study: The VERTEX campaign

The VERTEX (VERTical Enhanced MiXing) measurement campaign [11, 20] aims to verify whether vertical mixing near the ground is enhanced by the wind turbine. Although this mechanism has been widely held responsible for the meteorological variations observed in the wake [21, 10], it has never been demonstrated close to the ground. This campaign took place between August and November 2016, in the wake of a single wind turbine located in the city of Lewes, Delaware, on the east coast of the United States. The wind turbine is a Gamesa G90-2MW with a rotor diameter of $D = 90$ m and a hub height of 80 m. It is installed on the University of Delaware campus and surrounded by scattered small trees. To the west of the turbine lies a marsh, whereas to the east is the town of Lewes, consisting primarily of suburban residential areas. To the north, Delaware Bay connects to the Atlantic Ocean, and to the south, the landscape features a mixture of marsh, small forested areas, and agricultural fields (Figure 1). To measure the effects of wind turbine wake near the ground, 15 meteorological stations were placed in the marsh at a distance of 6, 8, 10, 12, 15, and 20 D downwind of the turbine. These stations were equipped to measure wind speed (at 3 and 10 m), temperature and humidity (at 2 m), and some stations also featured sensors to measure pressure, surface fluxes, as well as soil temperature and humidity. A meteorological mast tower located 377 m upwind of the turbine and equipped with sensors at heights of 10, 25, 33, 42, and 49 m was used to measure wind speed, temperature, and humidity to evaluate the conditions facing the wind turbine. The position of the wind turbine wake is captured using two LIDARs. The main distinguishing feature of the VERTEX campaign compared to a similar one is that all surface stations are positioned in a conical shape downstream of the turbine when the wind comes from ENE. Meteorological variations are evaluated by comparing measurements from stations located within the wake and those outside of it. This approach ensures that variations are directly attributed to the presence of the wake rather than differences in orographic features between the upwind and downwind areas of the wind turbine [7, 8, 10].

## 3 Numerical setup

Meso-NH is a non-hydrostatic and anelastic atmospheric model developed by the Centre National de Recherche Météorologique (CNRM - CNRS/Météo France) and the Laboratoire d'Aérologie (LAERO - UPS/CNRS) [22]. In this work, Meso-NH is used to simulate the atmosphere from mesoscale to metric scales to reproduce the VERTEX measurement campaign. To achieve this, four nested domains have been defined (Table 1). D1 and D2 (Figure 1) operate in a two-way nesting configuration and serve as a large-scale precursor simulations. D1 is initialized and forced at the lateral boundaries hourly with data from the ECMWF IFS model. The output from D2 is then used to force every five minutes the small-scale simulation which consists of D3 and D4. A one-way configuration was preferred to ensure consistent D3 inlet conditions between the simulations with and without the ADR. The wind turbine is modelled by an Actuator Disk with Rotation (ADR) and positioned in D4. This dissociation between small-scale and large-scale simulations was chosen for computational costs and because Meso-NH is not capable of changing vertical resolution between nested domains. Thus D3 and D4 can be refined vertically for altitude below 300 m where our ADR is located. The size of D4 was chosen to ensure that the turbulence is fully developed when the flow reaches the wind turbine and MetTower locations (turbulence spectra not shown).

In this work, the choice is made to solve the pseudo-compressible system of equations as defined by Durran in 1989 [23]. The equations are closed using a subgrid kinetic energy parameterization [24] based
<table><caption>Table 1: Domain dimensions of the numerical setup.</caption><thead><tr><th>Domain</th><th>N<sub>x</sub> × N<sub>y</sub> × N<sub>z</sub></th><th>Δx = Δy [m]</th><th>Δz<sub>0 m</sub> [m]</th><th>Δz<sub>300 m</sub> [m]</th><th>L<sub>z</sub> [km]</th><th>Δt [s]</th><th>WT</th></tr></thead><tbody><tr><td>D1</td><td>225 × 225 × 70</td><td>1000</td><td>4</td><td>~50</td><td>18</td><td>15</td><td>-</td></tr><tr><td>D2</td><td>200 × 200 × 70</td><td>125</td><td>4</td><td>~50</td><td>18</td><td>2</td><td>-</td></tr><tr><td>D3</td><td>500 × 500 × 152</td><td>25</td><td>4</td><td>4</td><td>2.6</td><td>0.3</td><td>-</td></tr><tr><td>D4</td><td>500 × 500 × 152</td><td>5</td><td>4</td><td>4</td><td>2.6</td><td>0.1</td><td>ADR</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Figure 1: Ground orography (top) and land cover (bottom) of the four nested domains. Position of the wind turbine (WT), MetTower and VERTEX stations (S-) and their numerical twin (in D4).</figcaption></figure>

on a mixing length formulation defined by Masson and Honnert (2021) [25]. Wind advection is handled with an explicit 4th-order Runge-Kutta time integration scheme and a 4th-order centered spatial scheme. Additional physical parameterizations include the ECRAD radiation scheme [26], a subgrid cloud model and the ICE3 microphysics scheme [27, 28] for water phase transformations. An online coupling with the SURFEX soil-vegetation transfer model [29] is employed to finely model surface properties such as albedo, surface fluxes, ground roughness, or leaf area index depending on land cover. Urban effects are modeled using the TEB model [30], while soil and vegetation effects are captured through the ISBA model [29]. Although the global 1 km resolution ECOCLIMAP database is typically used to define the land cover in SURFEX [31], its level of detail is insufficient for the resolution of D2, D3, and D4, as well as for accurate representation of the VERTEX campaign area. Therefore, a high resolution surface database was created for this study by combining OpenStreetMap data with the ECOCLIMAP nomenclature using the Geoclimate tool [32] (lower part of Figure 1). The topography is initialized using the USGS 10 m dataset (top part of Figure 1). The effects of wakes from trees [33] and urban buildings [34] (on surface cover "Wood", "Forest", and "Urban") are also modeled through the addition of drag forces, generating turbulence and improving the representation of the transition between marine and terrestrial boundary layers. The sea surface temperature of the IFS model was manually corrected to match the average measured by NOAA's sea buoy n°8557380, located in the nearby sea near the Lewes ferry terminal. Numerical stations and profilers are placed at the same locations as the stations and MetTower from the VERTEX campaign (see D4 in Figure 1). This allows us to record time series of the main meteorological parameters to enable a comparison between our simulated atmosphere and the conditions observed during the measurement campaign.

As in our previous study [13], a wind turbine of similar size is scaled to replicate the Gamesa G90,
preserving its original airfoil data as input for the ADR model. To achieve this, the blade geometry is uniformly scaled to a rotor diameter of $D = 90$ m and the rotation speeds are adjusted to maintain an iso tip-speed ratio. Finally, the twist angle is modified to obtain, as output, thrust $C_t$ and power $C_p$ coefficients close to those of the G90. The rotation speed, blade pitch angle, and rotor direction are updated every minute by a controller based on a lookup table, which depends on the average wind speed $U_∞$ measured one diameter upstream of the wind turbine. The ADR model is discretized as a disk with an azimuthal mesh ($N_θ = 71$) and a radial mesh ($N_r = 46$).

## 4 Validation of the atmosphere

The 4 September 2016 is selected for our validation case. This date corresponds to one of the case studies mentioned in [20]. On this day, the wind blew from the sea, and the wind turbine wake was located above some of the VERTEX stations. The atmosphere, initially unstable in the afternoon, gradually transitioned to neutral and then weakly stable in the evening, making it an interesting case study. A 24-hour large-scale precursor simulation was done over D1 and D2, starting at 4:00 (all times are given in 24-hour format and EST) on 4 September 2016. After 10 hours of spin up the output files from this simulation were then used to initialize and force three small-scale simulations over domains of the small-scale simulation (ie. D3 and D4), starting at 15:00, 18:00 and 20:40, respectively, to capture the three different stability regimes. The 5-minute averaged time series of some meteorological variables from the large and small-scale simulations are compared with measurements from MetTower or ground stations (Figure 2). Vertical profiles from our numerical simulation at the MetTower are compared with the measurements at 10, 25 and 49 m (Figure 3).

<figure><img src="image_7.png" /><figcaption>Figure 2: 5-min averaged time series of (a) Wind speed, (b) Wind direction, (c) TKE, (d) Turbulent Heat Flux, (e) Temperature and Humidity, (f) Cloud fraction, (g) Static Stability between 10 and 49 m and (h) Downward and Upward Short Wave Heat Flux at the MetTower or Station S1 for the VERTEX campaign, Domain D2 and D4 between the 3-4 September 2016.</figcaption></figure>

The comparison between experimental measurements and simulations shows good agreement. The small discrepancy observed between 12:00 and 14:30 may be due to a delay in the arrival of the sea breeze front over Lewes, which occurs around 12:00 in the measurements but between 14:30 and 15:00 in the simulation. After that, the atmosphere at the MetTower became highly turbulent until solar radiation decreased around 18:00 (Figure 2c). Although the turbulence levels in our simulation are slightly lower than the measurements, they remain of the same order of magnitude. The turbulent heat flux (THF) is accurately captured during the day and becomes negative near the ground after sunset (Figures 2d
<figure><img src="image_8.png" /><figcaption>Figure 3: 30-min averaged vertical profiles of (a) Wind speed, (b) Wind direction, (c) Temperature, (d) TKE, (e) THF, (f) Relative Humidity, at the MetTower in the Domain D4</figcaption></figure>

and 3e). During the afternoon, the wind blows from the northeast at approximately 10 m s⁻¹, gradually decreasing in the evening to 6 m s⁻¹ while shifting towards the north. Relative humidity throughout the day remained between 65–75%, slightly underestimated compared to the measurements. Even if the Coriolis effect is taken into account by Meso-NH, the synoptic atmospheric conditions of September 4th do not show the development of an Ekman spiral, either in the experimental measurements or in the wind direction profiles from the numerical simulations (Figure 3b). The humidity profile, initially relatively homogeneous due to mixing within the unstable boundary layer, becomes increasingly stratified as the atmosphere stabilizes (Figure 3f). The air near the surface becomes more humid, while the air at the wind turbine rotor level becomes drier. The temperature followed a diurnal cycle, with warming in the morning and cooling in the evening. Although the warming phase is accurately captured in the simulation, the cooling phase is underestimated (Figure 2e). Nighttime 2-meter temperatures appear to be more influenced by the sea surface temperature than by land surface temperature in the simulation. This overestimation of near-surface temperature prevents the simulation from generating a strong temperature inversion in the surface layer (0–10 m) and, consequently, a near-ground stability comparable to those measured during the VERTEX campaign (Figures 2g and 3c). Satellite observations from MODIS, CloudSat, and CALIPSO (not shown) confirm that September 4 was cloudy over Delaware. A layer of ice clouds at around 5 km covered the area for most of the day. Significant fluctuations in short-wave radiation measured during the VERTEX campaign also indicate the presence of low-level water clouds that intermittently shaded the area (Figure 2h). These two cloud layers are present in our simulation (at approximately 1 km and between 5 and 10 km). However, the density of low clouds in the simulation is likely too low to replicate the observed VERTEX short-wave radiation oscillations. This undercooling of the near-ground air is perhaps caused by too high soil temperature due to the strong short wave down during the day caused itself by the underestimation of the cloud density. Unfortunately, this explanation cannot be verified because the soil temperature probes of the VERTEX campaign located in the march at station S15 were at that time under a flood and then measuring the evolution of the water temperature rather than the soil. Overall, our multi-scale numerical setup with Meso-NH successfully reproduces the meteorological variations observed on September 4, 2016. However, slight discrepancies, particularly in temperature and TKE, highlight the challenges of accurately reproducing specific cases [15, 19].

## 4.1 Meteorological variation induced by the wind turbine

The meteorological variations induced by the wind turbine are computed as the difference between small-scale simulations with and without an ADR. After a 15-minute spin-up period, a 30-minute window is selected to compute the mean fields for the unstable (Figure 4), neutral (not shown), and stable (Figure 5) cases. A quantitative comparison of the three stability cases is plotted in Figure 6 using the evolution of meteorological variations at 2-meters above the ground along the streamwise and spanwise directions. The aerodynamic coefficients of the wind turbine for the three stability cases are summarized in Table 2.

### 4.1.1 Unstable
The unstable nature of the atmosphere is clearly visible in the large fluctuations in the instantaneous wind speed field (Figure 4 b) and the high levels of TKE in front of the turbine. The instantaneous field also reveals the development of turbulence at the entrance of D4 due to the difference in resolution between D3 and D4 (Figure 4a). The drag from trees and buildings is evident
<figure><img src="image_9.png" /><figcaption>Figure 4: Unstable case with ADR : September 4 between 15:50 and 16:20. First column : horizontal cross-sections (at hub height 86 m or at 2 m above the ground), second column : vertical cross-sections along the wake, third column : cross-sections across the wake at 6D downstream of the wind turbine. The position of the ADR is represented by a bold black line, and the rotor projection is indicated by a dashed black circle. The terrain is represented in black and altitude zero is equal to sea level.</figcaption></figure>

<table><caption>Table 2: Average wind speed face by the wind turbine $U_∞$, trust coefficient $C_t$, power coefficient $C_p$, maximum mean temperature variations $\Delta T$ and relative humidity variations $\Delta HR$ at 6D downstream of the ADR for the 3 stability case ($\Gamma_{LR}$ : lapse rate between 10 and 49 m, equation (1)). Standard deviations are indicated in parentheses.</caption><thead><tr><th>Simulation</th><th>Reference period</th><th>U<sub>∞</sub> [m/s]</th><th>C<sub>p</sub></th><th>C<sub>t</sub></th><th>Γ<sub>LR</sub> [K/m]</th><th>ΔT [K]</th><th>ΔHR [%]</th></tr></thead><tbody><tr><td>Unstable</td><td>15:50-16:20</td><td>9.75</td><td>0.46</td><td>0.68</td><td>0.0186</td><td>-0.072(0.023)</td><td>-0.19(0.20)</td></tr><tr><td>Neutral</td><td>18:15-18:45</td><td>7.35</td><td>0.52</td><td>0.81</td><td>0.0084</td><td>0.012(0.01)</td><td>-0.53(0.20)</td></tr><tr><td>Stable</td><td>20:55-21:25</td><td>6.31</td><td>0.50</td><td>0.81</td><td>0.0071</td><td>0.088(0.03)</td><td>-2.3(0.65)</td></tr></tbody></table>

through the increase in TKE and the reduced wind speed near the surface at 3D and -1D of the ADR (Figures 4b and 4h). The wind speed deficit generated by the wind turbine wake is visible in figures 4d, 4e, and 4f. Above and below the rotor, an increase in wind speed is observed due to the blockage effect induced by the turbine [2, 35, 36]. In the upper part of the wake, TKE increases, while in the lower part and below the rotor, it decreases by up to -0.4 m² s⁻² (Figure 6d), confirming the findings of [12, 20, 11]. The warming of the surface by incoming solar radiation heats the air near the ground, making it less dense than the air above. This density difference gives rise to convective motions,
also known as thermals, which are visible in the vertical wind velocity field as alternating ascending and descending bands (Figure 4m). The vertical movements induced by the wind turbine add to these convective movements and enhance vertical mixing. At the time of our simulation, the wind turbine is embedded in a descending air mass, which explained the downward deflection of the wake (Figure 4e) and a wind speed deficit of almost 1 m s⁻¹ near the ground (Figure 6a). The temperature variation fields for the unstable case are highly noisy (Figure 4p). Investigating this phenomenon revealed that these differences in the temperature field between simulations with and without the wind turbine originate at the base of the turbine and propagate upstream, reaching urban areas located upwind of the turbine (visible in Figure 4p). These small differences induced by the turbine are significantly amplified by the turbulent nature of the atmosphere. However, the signal of temperature variation downstream of the turbine is stronger than the ambient noise and appears to indicate a maximum cooling of -0.05 K on one side of the turbine (Figure 6e). Nevertheless, it is challenging to confirm whether these variations are due to the presence of the turbine or to the strong heterogeneity of the unstable atmosphere. Only small variations in relative humidity are observed in the wake at the rotor level, with slight drying in the lower part and slight moistening in the upper part (Figure 4t). The very small drying in the lower rotor region appears to affect the near-ground air starting from 6 D downstream of the turbine (Figure 6g), but it remains extremely weak. These very small and uncertain meteorological variations in the case of an unstable atmosphere align with different findings in the literature [4, 7, 9, 11, 20].

<figure><img src="image_10.png" /><figcaption>Figure 5: Same as figure 4 but for the stable case, September 4 between 20:55 and 21:25.</figcaption></figure>

4.1.2 *Stable* After sunset, the ground cools down and turbulence significantly decreases, leading to the formation of a weakly stable atmosphere. Therefore, as mentioned in the literature, the wake of the wind turbine (Figure 5 b) remains coherent over a much longer distance [36]. Similarly to the unstable case, a slight acceleration is observed just below and above the wind turbine. Although the overall TKE
levels are much lower in a stable atmosphere, there is still an increase in TKE in the upper part of the rotor and a decrease in the lower part. Except in the immediate vicinity of the wind turbine (Figure 6), near-ground TKE and wind speed levels remain similar to those upstream of the turbine: there is no "touchdown effect" before -0.05 D, as seen in the unstable case or reported in Wu et al. (2023) [3]. Due to the stable nature of the atmosphere, vertical motions are damped. Only vertical movements driven by changes in terrain, urban areas, and trees can be distinguished (Figure 5m). In this context, the vertical motions induced by the presence of the wind turbine and the counterclockwise rotation of the wake are clearly distinguishable from the ambient vertical motions. (Figures 5m, 5n, and 5o as seen in [13, 35, 37]). The fields of temperature and humidity variations in the stable atmospheric case show warming and drying in the lower and right part of the wake (seen from downstream) and cooling and humidification in the upper and left part. The decrease in relative humidity is about 2-3%, with a maximum around 1D behind the turbine at the lower tip height and on the right-hand side of the wake, consistent with the drone measurements and LES simulations of [4, 9]. At a height of 2-meters and 6D downstream of the wind turbine, temperature variations of up to 0.1 K and relative humidity variations of up to -2% are simulated (Figure 6), aligning with the findings of the VERTEX campaign and the literature [4, 7, 9, 11, 20]. Additionally, the warming and drying zones correspond to areas where the vertical wind speed is downward, while the cooling and humidifying zones are associated with upward vertical wind speed.

### 4.1.3 Neutral
The variations along the near-ground air are shown in Figure 6. As noted in the literature, the neutral case is characterized by the absence of significant differences in meteorological variables near the ground between the upstream and downstream of the wind turbine. In our simulation, only slight changes in TKE and relative humidity can be observed (Figures 6c and 6g), but these variations are not significant, particularly when compared to the stable and unstable cases. The neutral case thus serves as a reference and highlights the critical role that atmospheric stability plays in driving the meteorological variations caused by wind turbine wakes.

<figure><img src="image_11.png" /><figcaption>Figure 6: 30-min average 2-meters variation along the same lines as figure 4. The position of the ADR is represented by a bold black line.</figcaption></figure>

## 5 Discussion and perspectives

The maximum temperature and relative humidity variations in the spanwise direction at 6D downstream of the ADR for the three stability cases are summarized in Table 2. It is worth noting that the wind speed and therefore the operating conditions of the wind turbine differ for the three cases. To compare these temperature variations with those measured during the VERTEX campaign, we adopted the methodology used by [20]. Temperature variations at 6D downstream of the ADR are plotted as a function of the lapse rate between 10 and 49 m (equation (1) [11]). If $\Gamma_{LR}$ is above the dry adiabatic lapse rate $\Gamma_d = 0.0098 \text{ K}^{-1} \text{ m}$, the stratification of the atmosphere is defined as unstable, if it is below the stratification
<figure><img src="image_12.png" /><figcaption>Figure 7: 2-m temperature difference at $x = 6$D behind the ADR for our 3 stability cases depending on the lower-rotor lapse rate between 10 and 49 m (equation 1). Experimental data from Wu and Archer 2021 [20].</figcaption></figure>

of the atmosphere is defined as stable. When $\Gamma_{LR}$ is close to $\Gamma_d$ with a margin of ±0.002, the atmosphere is defined as neutral [20]. Consistent with [13], both numerical and experimental points exhibit a similar trend: warming under stable conditions and cooling under unstable conditions (Figure 7). The relatively weak warming observed in our stable case compared to the literature can be attributed to the weakly stable atmosphere on the evening of 4 September driven by sea winds. Additional simulations under stronger stability conditions are necessary to confirm this trend and investigate variability under other parameters, such as wind speed and turbulence.

$$ \Gamma_{LR} = - \frac{T_{49 \text{ m}} - T_{10 \text{ m}}}{49 - 10} \qquad (1) $$

Although the VERTEX stations were equipped with humidity sensors, previous studies primarily focused on temperature variations [11, 20]. Humidity variations have been less studied, particularly under stable conditions [5, 10, 40, 41]. One of the most comprehensive studies using drone measurements was conducted by Adkins [4, 9]. They showed that under a positive humidity lapse rate (moisture air near the surface) and a statically stable atmosphere (as in our stable case, see profiles in Figure 3), a relative humidity decrease of about -3% occurs below the hub, while an increase is observed above it. In contrast, under unstable and neutral conditions, no significant differences were detected between upstream and downstream measurements. The streamwise and spanwise drone measurements by [9] exhibit similar shapes and amplitudes to our simulations (Figure 6h and 5t). [4] further highlighted the strong relationship between the temperature lapse rate within the lowest 100 meters and humidity variations induced by the wind turbine wake.

Early investigations into wind turbine-induced meteorological variations utilized large-scale turbine modeling [1, 42]. These studies identified the phenomenon of wake-generated turbulence, which enhances vertical mixing and disrupts temperature and humidity distributions. With advancements in fine-scale turbine models, such as ADR models, it became possible to capture detailed wake characteristics, enabling more precise analyses. Notably, it has been demonstrated that wind turbines induce vertical motions due to wake rotation and rotor flow divergence, generating both upward and downward movements [13, 35, 37]. Depending on the stratification of temperature and relative humidity below and at the rotor level, these vertical movements result in different meteorological variations. In stable conditions, the near-ground temperature lapse rate is sub-adiabatic, and the relative humidity lapse rate is negative (due to radiative cooling and surface processes at night). Vertical motion transports moist, cool near-surface air upward and dry, warm rotor-level air downward, causing warming and drying below the rotor and cooling and moistening above [9]. The opposite effect occurs under unstable conditions, though these effects are quickly attenuated and dissipated by strong convection and turbulence. In neutral conditions, the absence of significant temperature and humidity stratification results in negligible variations.

## 6 Conclusion

In an effort to better understand the interactions between wind turbines and the atmosphere, a multi-scale model with atmosphere-soil-vegetation coupling was developed. The ability of this coupling to reproduce
realistic atmospheric conditions was validated using measurements from the VERTEX campaign. Subsequently, the ADR method was employed to investigate the impact of wind turbines on temperature and humidity variations, particularly near the ground. The simulations confirmed the significant role that air stratification plays in the near-ground in the sign and magnitude of meteorological variations induced by wind turbines. For stable conditions, variations in temperature and relative humidity of up to +0.1K and -2.5% respectively, were observed at ground level 6D downstream of a single turbine. In the unstable case, only slight drying and cooling of around +0.07K were found, although it is difficult to assert that these variations were only due to the presence of the turbine. Finally, under neutral conditions, a very slight warming of +0.01K and a drying of -0.53% were observed. These results confirm that the impact of wind turbines is most significant under stable conditions. However, it should be noted that only one case per stability regime was studied in this work. In the future, a more statistical and rigorous approach will be necessary to study streamwise variations, particularly by employing methods such as quantitative analysis of humidity and temperature progression (time and laterally averaged), as done by [4]. In future work, the dependence of meteorological variations on other atmospheric and orographic properties, such as wind speed, turbulence, or land cover, will be investigated. Subsequently, the cumulative effects induced by consecutive wind turbines will also be studied. Additionally, it could be interesting to use Lagrangian particle tracking to determine the altitude of air masses responsible for temperature and humidity variations (as done by [4]). Based on these air mass movements, an analytical model could be developed to quantify potential temperature and humidity variations as a function of meteorological profiles, as well as the dimensions and characteristics of wind turbines.

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