The study examined nocturnal observations from the Amazon Tall Tower Observatory, or ATTO, in the central Amazon. Measurements were collected from 8 to 19 August 2022, while the nights of 20 and 21 August were excluded because several required variables, including longwave radiation and carbon dioxide flux observations, were unavailable. The network combined the 325 m ATTO tall tower with an 81 m Instant tower positioned 670 m away. Sensors at 14 height levels measured atmospheric variables, trace gases and turbulent fluxes, which were calculated at 30-minute intervals and corrected for outliers, temporal trends, wind-field rotation, time lags, spectral losses, crosswind effects and density fluctuations. The within-canopy measurements represented dense forest, whereas the tall tower stood in a more disturbed, open-forest environment. The researchers defined the aerodynamic canopy top as 32 m and treated the period from 18:00 to 06:00 local time as nocturnal.

To estimate the height of the nocturnal boundary layer, the authors applied five criteria based on vertical profiles of potential temperature, carbon dioxide, horizontal wind speed, gradient Richardson number and turbulent kinetic energy. Because observations were available at discrete heights, second-order logarithmic functions were fitted to the temperature, carbon dioxide and wind profiles, while Richardson number and turbulent kinetic energy were linearly interpolated. Across all methods, estimated heights ranged from 74 to 298 m, and the mean across the five criteria generally remained between 142 and 164 m during the night. The temperature-based estimate averaged 150 ± 27.1 m, the carbon-dioxide estimate 145 ± 42.2 m, and the wind-based estimate 156 ± 46.5 m. The Richardson-number method averaged 134 ± 29.5 m, whereas the turbulent-kinetic-energy method averaged 193 ± 50.9 m; the latter could be applied in only about 20% of the analyzed hours.

Wind conditions contributed to the variation among the height estimates. Weak nocturnal low-level jets occurred during the nights of 12–13 and 17–18 August, with maximum wind speeds of 5.5 m s−1. These nights had low-level jet nose heights between 80 and 100 m, wind speeds no greater than 3.0 m s−1 at the top of the tower and shallow boundary layers no higher than 135 m. On nights with nearly linear vertical wind profiles, including 13–17 August, wind-based boundary-layer estimates were at least 167 m. The overall results were consistent with previous ATTO studies, which reported nocturnal boundary-layer heights from 81 to 247 m. Differences were expected because each criterion responds to a different physical process. The study suggests that gradient-based approaches may provide useful estimates where detailed vertical turbulence measurements are unavailable, while noting that elevated turbulent layers can complicate the Richardson-number and turbulent-kinetic-energy methods.

The vertical structure evolved in opposing directions within and above the canopy. At the canopy-atmosphere interface, between 25 and 81 m, the potential-temperature gradient increased from 0.040 to 0.046 K m−1 through the night, while the carbon-dioxide gradient strengthened from −0.09 to −0.51 ppm m−1. Below the canopy, between 5 and 25 m, the temperature gradient decreased from 0.067 to 0.015 K m−1 and the carbon-dioxide gradient increased from −1.77 to −0.69 ppm m−1. Air above the forest therefore became more strongly stratified as the night progressed, while air within the canopy became better mixed. The authors associated the canopy mixing with a locally cold layer in the dense crown, roughly between 15 and 30 m, which can promote sinking motions. Turbulence observations in selected periods were consistent with downward movement of cold air within the canopy.

To distinguish the idealized stability regimes, the researchers applied six criteria involving potential temperature, carbon dioxide, wind speed, Richardson number, turbulent kinetic energy and friction velocity. Only eight hours, distributed across four nights with two consecutive hours per night, satisfied all six criteria for either regime. The mean boundary-layer height in the selected strongly stable profiles was 133 m, compared with 165 m in the weakly stable profiles. Between 25 and 50 m, the strongly stable regime contained a 2.08 K temperature increase, compared with 1.29 K in the weakly stable regime. The carbon-dioxide gradient between 38 and 79 m was −21.8 ppm in strongly stable profiles and −5.5 ppm in weakly stable profiles. Above roughly 200 m, differences in turbulent kinetic energy, Richardson number and friction velocity largely disappeared, suggesting that the regimes’ turbulent differences did not substantially affect the residual layer.

Budget calculations showed how mechanical mixing and buoyancy produced the contrasting regimes. In the weakly stable case, shear production at the canopy top was 6.20 × 10−3 ± 2.05 × 10−3 m2 s−3, far larger than the strongly stable value of 0.16 × 10−3 ± 0.52 × 10−3 m2 s−3. Buoyant consumption was −1.70 × 10−3 ± 0.94 × 10−3 m2 s−3 in the weakly stable regime and −0.30 × 10−3 ± 0.31 × 10−3 m2 s−3 in the strongly stable regime. Dissipation was also greater in the weakly stable case, at −4.69 × 10−3 ± 0.25 × 10−3 m2 s−3, compared with −0.43 × 10−3 ± 0.44 × 10−3 m2 s−3 in the strongly stable case. The turbulent-kinetic-energy tendency itself was 10 to 100 times smaller than the individual budget terms. These observations support a decoupled canopy beneath the stable layer during strongly stable conditions and a coupled, shear-mixed canopy during weakly stable conditions.

The heat budget quantified differences in exchange across the interface. Turbulent heat-flux divergence at the canopy top was −3.62 ± 1.54 K h−1 in weakly stable periods and −1.23 ± 0.79 K h−1 in strongly stable periods, consistent with stronger mechanical mixing in the former. The atmospheric longwave cooling rate at the canopy top was −0.24 ± 0.35 K h−1 in weakly stable conditions and −0.35 ± 0.02 K h−1 in strongly stable conditions. Cloud cover may have reduced cooling during two weakly stable periods. Because longwave radiation was measured at only one height, the cooling rate was estimated with the RTE + RRTMGP radiative-transfer model driven by downscaled ERA5 temperature and moisture profiles. Comparisons at 75 m showed an absolute mean difference of 6.92 W m−2 and a standard error of 3.90 W m−2. The model-based estimate nevertheless introduced additional uncertainty, and the heat-budget residual remained substantial.

Carbon dioxide transport was similarly sensitive to canopy-atmosphere coupling. At the canopy top, turbulent carbon-dioxide transport averaged 33.94 ± 37.45 ppm h−1 in weakly stable periods and 23.39 ± 9.54 ppm h−1 in strongly stable periods, although the weakly stable estimate was much more variable. At 50 m, the mean nocturnal carbon-dioxide flux was 3.82 µmol m−2 s−1 during weakly stable conditions, with a maximum of 6.38 µmol m−2 s−1. In the decoupled strongly stable regime, the average was only 0.16 µmol m−2 s−1. Weakly stable mixing transported carbon-dioxide-rich air upward from the canopy, while strong stability inhibited that exchange and favored accumulation beneath the forest crown. The simplified carbon-dioxide budget omitted horizontal advection and other terms, and an estimated horizontal gradient could account for much of the remaining negative residual.

The findings are limited to one two-week measurement period in the 2022 dry season at a single central-Amazon site, where topography and heterogeneous forest structure limit direct extrapolation to the wider basin. Wet-season clouds, precipitation and turbulent activity differ from those observed here, while one year of measurements cannot characterize interannual variability. The budgets also assumed horizontal homogeneity and negligible subsidence, and their residuals included observational uncertainty, advection, pressure effects and other unmeasured processes. Nevertheless, the results provide an observational benchmark for testing nocturnal parameterizations. The authors call for schemes that explicitly represent tall, multilayer canopies, intermittent turbulent bursts and stability-dependent coupling, supported by measurements at intervals of five minutes or less and by high-resolution observations of the cooling layer between 20 and 30 m.

Subject of Research: Observation-driven analysis of Amazonian dry-season nocturnal boundary-layer dynamics and carbon dioxide transport

Article Title: From prototypical regimes to observed complex dynamics and CO2 transport in the Amazonian nocturnal boundary layer

Article References: Huitema, A. C., de Feiter, V. S., González-Armas, R., Hartogensis, O. K., van Asperen, H., Dias-Júnior, C. Q., & Vilà-Guerau de Arellano, J. (2026). From prototypical regimes to observed complex dynamics and CO 2 transport in the Amazonian nocturnal boundary layer. Atmospheric Chemistry and Physics, 26(20), 14725-14747. https://doi.org/10.5194/acp-26-14725-2026

Image Credits: AI Generated

DOI: 10.5194/acp-26-14725-2026

Keywords: Amazon rainforest, nocturnal boundary layer, carbon dioxide transport, canopy-atmosphere exchange, turbulent kinetic energy, atmospheric stability, Amazon Tall Tower Observatory, climate models, dry-season observations, intermittent turbulence, prototypical, regimes

The research, published as a preprint under review in the journal Wind Energy Science by Dachuan Feng, Wen Zhang, Minping Wan, and Simon J. Watson, reconstructs the atmospheric boundary layer evolution observed on 26 September 2023 at the King Plains wind farm during the American WAKE experimeNt, or AWAKEN, field campaign. Rather than treating the atmosphere as a static backdrop, the team employed measurement-assimilated large-eddy simulation, a computational technique that resolves the largest turbulent eddies in the flow while forcing the simulation to follow the actual conditions recorded in the field. This allowed them to capture the boundary layer during a genuinely non-equilibrium period — the fleeting hours when the atmosphere is neither fully stable nor fully unstable.

The atmospheric boundary layer is the lowest kilometer or so of the atmosphere, the region where the land and the free air above exchange momentum, heat, and moisture. At night, under clear skies, the ground cools faster than the air, producing a stable stratification in which layers of denser air sit beneath lighter air. Turbulence is suppressed, winds are smooth, and the wake behind a turbine behaves like a coherent, quasi-deterministic structure. After sunrise, solar heating flips the temperature gradient, buoyancy begins to drive vigorous vertical mixing, and the boundary layer transforms into a turbulent, well-stirred engine. Wind turbines living through this transition experience an inflow that changes faster than most engineering models assume.

To dissect the wake dynamics, the researchers identified three regimes across the morning transition: stable, transitional, and unstable. In each regime, they averaged the simulated wake flow over time and then applied a mathematical tool known as resolvent analysis. Resolvent analysis examines how the linearized Navier–Stokes equations, perturbed around the time-averaged flow, amplify forcing into coherent structures. In practical terms, it reveals the dominant shapes and frequencies with which the wake naturally oscillates — the hidden skeleton of the turbulence.

The results are striking. Under stable night-time stratification, the wake’s dominant mechanism is a single, low-rank, shear-driven coherent structure — essentially one regular swaying motion, generated by the velocity difference between the slowed wake and the faster ambient flow. As the boundary layer grows increasingly unstable, the picture changes completely: the dominant mechanism becomes higher-rank and buoyancy-driven, meaning many irregular modes compete simultaneously, each shaped by rising thermal plumes rather than by simple shear alone. The transitional regime, sandwiched between the two, exhibits hybrid behavior, carrying signatures of both shear-driven order and buoyancy-driven disorder.

The statistics of the inflow shift just as dramatically. As the boundary layer becomes increasingly unstable, the hub-height turbulence intensity increases by roughly an order of magnitude compared with stable night-time conditions. Meanwhile, the characteristic length scale of atmospheric motion grows from a few rotor diameters — eddies comparable in size to the turbine rotor itself — to the full depth of the boundary layer, structures spanning hundreds of meters to more than a kilometer. A turbine that spent the night bathed in small, gentle eddies is, by mid-morning, immersed in vast, energetic sweeps of air that engulf entire rows of machines.

These changes are not merely academic. The study shows that array-averaged power production, its variability, and its spectral content — the distribution of power fluctuations across frequencies — all change significantly over a period of just a few hours. Wake dynamics govern how much energy downstream turbines extract from the wind: a wake that meanders coherently may sweep across neighboring machines in predictable pulses, while a wake shredded into many irregular modes diffuses differently, altering both the losses and the fatigue loads experienced by the array. The finding that these quantities shift on timescales of hours means that control strategies and power forecasts calibrated to one stability regime can be badly miscalibrated a few hours later.

Perhaps the most consequential conclusion is a negative one: steady-state stability classifications do not adequately represent the dynamics of the diurnal cycle. Much of wind-farm engineering — wake models, layout optimization, power forecasting — implicitly assumes that the atmosphere sits in one equilibrium state at a time, and that a stable or unstable label fully characterizes the flow. The morning transition violates that assumption. During the non-equilibrium period, the wake is not in balance with its inflow, and the coherent structures identified by resolvent analysis evolve continuously between the stable and unstable archetypes. For grid operators, this means the hours around sunrise may be precisely when forecasts are least reliable unless the transition itself is modeled.

The work also demonstrates the power of measurement-assimilated simulation as a bridge between field campaigns and theory. The AWAKEN campaign, one of the most extensive efforts to instrument a working wind farm with lidars, met masts, and other sensors, provides the ground truth; large-eddy simulation provides the full three-dimensional, time-resolved flow field that no field campaign can measure everywhere at once. Assimilating the measurements into the simulation ensures the modeled boundary layer follows the real atmosphere’s trajectory through the morning, allowing the researchers to isolate cause and effect in a way that neither observations nor idealized simulations could achieve alone.

As wind power becomes a dominant pillar of electricity systems worldwide, the margins of performance increasingly lie in details like these. A wake that changes character within hours, shifting from one regular swaying motion to many irregular ones, is not a curiosity — it is a daily, predictable, and currently under-modeled feature of every wind farm on Earth. The authors argue that power forecasts and turbine controls should reflect these shifts, and the present study offers both the evidence and the analytical machinery, from resolvent analysis to regime classification, to begin doing so. The next sunrise over the plains will, once again, rewrite the wake — but now, at least, the transformation is on the record.

Subject of Research: How the diurnal atmospheric stability transition reshapes wind-turbine wake dynamics and power production

Article Title: Effect of atmospheric stability transition on wind-turbine wake dynamics

Article References: Feng, D., Zhang, W., Wan, M., & Watson, S. J. (2026). Effect of atmospheric stability transition on wind-turbine wake dynamics. https://doi.org/10.5194/wes-2026-184

Image Credits: AI Generated

DOI: 10.5194/wes-2026-184

Keywords: wind energy, atmospheric boundary layer, wake dynamics, atmospheric stability, large-eddy simulation, resolvent analysis, turbulence, AWAKEN field campaign, diurnal cycle, power forecasting, wind farm control, buoyancy-driven turbulence

Atmospheric ducts are anomalous stratification layers in the lower troposphere in which the vertical gradient of what meteorologists call modified refractivity turns negative. In ordinary conditions, the index of refraction of air decreases gently with height, causing radio waves to curve slightly downward and follow the Earth’s curvature over limited distances. When a duct forms, however, the refractivity drops so steeply within a thin layer that electromagnetic energy becomes trapped inside it, bouncing between the layer and the sea surface much like light in a fiber-optic cable. The effect can extend the detection range of coastal radars dramatically, but it also creates shadow zones where targets vanish from screens and radar clutter overwhelms displays — rendering standard propagation models unreliable precisely when they matter most.

The operational stakes of these hidden waveguides are not academic. Analysts investigating the April 2022 sinking of the Russian cruiser Moskva have suggested that anomalous ducting conditions may have facilitated the unexpected beyond-line-of-sight engagement by Neptune missiles, a reminder that refractivity structure can shape modern naval encounters. Military meteorologists have long tracked ducts for exactly this reason, and the ability to predict these blind zones or extended ranges is often described as decisive in electronic warfare. Yet most of what science knows about ducting comes from short field campaigns rather than long-term climate-scale analysis.

That gap is what motivated a team led by Xiaofeng Zhao and Chunshan Wei of the Army Engineering University in Nanjing, together with colleagues from the National University of Defense Technology, Sun Yat-sen University, the Southern Marine Science and Engineering Guangdong Laboratory and the Beijing Institute of Technology. Since the early 2000s, major experiments such as Wallops-2000, the Rough Evaporative Duct Experiment, the Tropical Air–Sea Propagation Study and the Coupled Air–Sea Processes and Electromagnetic Ducting Research have refined the understanding of how air–sea interactions build refractivity structures. But those campaigns captured weeks or seasons, not decades, and regional climatological studies — of Arctic sea ice influence or West African monsoon dynamics, for instance — left the global picture unexplored.

To close it, the team turned to ERA5, the European Centre for Medium-Range Weather Forecasts reanalysis that reconstructs the global atmosphere hourly from 1940 to the present on a quarter-degree grid. Using model-level profiles, they identified the lowest trapping layer below 2,500 meters at every location and time, extracting three key quantities: how often ducts occur, how strong they are, and how high their tops sit. Duct strength is measured in M-units, a dimensionless convention that expresses the modified refractive index used throughout propagation research. At each grid cell, annual values were regressed against year, with significance judged by a two-tailed t-test at the 95 percent confidence level, and the results were cross-checked with corrections for temporal autocorrelation and spatial multiple comparisons.

The findings are striking. Over the oceans between roughly 60 degrees south and 60 degrees north, duct occurrence probability, strength and top height all show widespread, statistically significant increases. The enhancement accelerated alongside the rapid global warming that began in the late 1970s and noticeably slowed during the so-called warming hiatus from 1998 to 2012 — a temporal fingerprint that immediately suggested a warming-driven cause. Over land, the changes are milder, with the largest positive shifts in duct frequency and strength appearing over South America. The Southern Ocean and Antarctica break the pattern entirely: there, ducting characteristics decline markedly, a regional exception that turns out to reinforce the physical explanation.

That explanation rests on a well-understood asymmetry in how warming changes atmospheric stability. The rate at which temperature falls with height — the lapse rate — is central to duct formation, and the dry-adiabatic lapse rate is essentially temperature-independent. Warming therefore changes the environmental lapse rate mainly by reducing the moist-adiabatic lapse rate, which governs how air cools as it rises when condensation is involved. Over humid oceans, where vertical mixing follows moist-adiabatic processes closely, this reduction increases static stability in the marine boundary layer, favoring the formation of temperature inversions that trap moisture near the surface and steepen vertical humidity gradients — the recipe for more frequent and more intense ducts. Over dry continents, mixing stays closer to dry-adiabatic and stability changes little, which is why land sees only modest shifts.

Antarctica behaves differently for its own reasons. The cold, dry polar atmosphere is already so stably stratified that convection and turbulent mixing are suppressed, limiting vertical heat transport. Preferential near-surface warming then weakens the existing temperature inversion and reduces lower-tropospheric stability. On top of that, warming-driven sea ice loss weakens surface radiative cooling, further damping stability and cutting duct occurrence. The team also notes that rising duct top heights, which a stability-only argument would predict to fall, are consistent with a weakening of large-scale tropical subsidence under global warming: since ducts preferentially form in subsidence regimes near the top of the boundary layer, reduced subsidence can lift the boundary layer and raise the ducts with it. Reconstruction of the leading variability mode tied to global temperature shows a roughly 3 percent increase in duct occurrence probability and nearly a 60-meter rise in duct top height over mid- to low-latitude oceans, with duct strength strengthening by about 0.8 M-units overall.

Separating a long-term climate signal from natural ups and downs required care, because interannual drivers such as the El Niño/Southern Oscillation clearly leave their mark. The team applied empirical orthogonal function analysis to seasonally averaged duct characteristics and then used partial correlations against a suite of climate indices, including ENSO, the Arctic and North Atlantic Oscillations, the Pacific Decadal Oscillation, the Indian Ocean Dipole and global mean surface temperature. When the long-term trend was retained, the leading mode of duct variability correlated significantly and positively with global temperature; once the trend was removed, that correlation largely vanished. ENSO, by contrast, dominated the second mode, with correlations against the Niño 3.4 index exceeding 0.9 and its strongest responses concentrated over the equatorial Pacific, where changes in convective rainfall alter stability and humidity gradients. But because ENSO swings on short timescales and over shifting regions, its net contribution to the global mean trend is limited. Warming, the authors conclude, is the dominant driver of the long-term evolution.

The study carries practical caveats — the analysis tracks only the lowest trapping layer, and with ERA5’s lowest model level at 10 meters, it cannot resolve shallow evaporation ducts — but its implications reach well beyond meteorology. Shipping, maritime radar, coastal surveillance, navigation and satellite-to-sea communications all depend on predictable radio propagation, and a background atmosphere that grows steadily more stratified means longer anomalous ranges, deeper shadow zones and noisier displays over much of the world’s ocean. If warming continues on its current path, the marine boundary layer will keep sliding toward the highly stratified regime the study describes, turning a once-marginal propagation quirk into a first-order consideration for anyone who designs, operates or defends the world’s wireless links at sea. Data and code from the study have been released via Zenodo, allowing forecasters and engineers to begin translating the finding into operational practice.

Subject of Research: Warming-driven intensification of atmospheric ducting over global oceans

Article Title: Warming-driven lower-tropospheric stratification intensifies ducting over global oceans

Article References: Zhao, X., Wei, C., Zhu, X., Wang, D., Lyu, M., Yang, P., & Liu, Y. (2026). Warming-driven lower-tropospheric stratification intensifies ducting over global oceans. Nature Climate Change. https://doi.org/10.1038/s41558-026-02774-y

Image Credits: AI Generated

DOI: 10.1038/s41558-026-02774-y

Keywords: atmospheric ducting, climate change, marine boundary layer, radio propagation, radar, ERA5 reanalysis, El Nino Southern Oscillation, atmospheric stability, refractivity, oceans, telecommunications, electromagnetic waveguides

The research, led by Alexandros Palatos-Plexidas of the von Karman Institute for Fluid Dynamics together with colleagues at the Vrije Universiteit Brussel, the Royal Meteorological Institute of Belgium, and partner institutions, focuses on the 3.7 gigawatt Belgian-Dutch offshore wind farm cluster in the Southern Bight of the North Sea. This dense collection of neighboring wind farms is the largest offshore cluster in operation today, which makes it a prime natural laboratory for studying how wind farms interact with each other and with the atmosphere above them. The team ran the Weather Research and Forecasting model, known as WRF, at a horizontal resolution of one kilometer for three full years, from 2021 through 2023, a configuration that is both unusually long and unusually fine for offshore wake research.

The technical heart of the study is the Fitch wind farm parameterization scheme, the most widely used method for representing wind turbines inside mesoscale weather models. Because a one-kilometer grid cell cannot resolve individual turbines, the scheme imposes a momentum sink on the mean flow within cells containing turbines, converting a fraction of the extracted kinetic energy into electricity and the remainder into turbulent kinetic energy. The researchers ran two otherwise identical simulations side by side within a single model run, one with the wind farms switched on and one without them, allowing the wake effect to be isolated as the normalized difference between the two wind speeds. The model was initialized and driven at its boundaries by ERA5 reanalysis data at 30-kilometer resolution, with three nested domains stepping down from 9 to 3 to 1 kilometer, and 80 vertical levels densely packed in the lowest few hundred meters of the atmosphere.

What sets this study apart is the observational benchmark. Four vertical profiling lidars, operated by different institutions, surround the cluster in a nearly perfect alignment along the prevailing southwesterly wind direction. The Westhinder lidar, installed on a survey platform roughly 40 kilometers southwest of the cluster, samples the incoming freestream flow. The Borssele B lidar, mounted on a TenneT grid platform inside the cluster, records what happens within the farms themselves. Two further lidars on the Europlatform and Lichteiland Goeree platforms to the northeast capture the far wake. Together they provide an unusually complete picture of upstream, intra-farm, and downstream conditions, all interpolated to a common reference hub height of 107 meters, typical of the turbines in the region.

The results show that switching on the wind farm parameterization substantially improves model accuracy where it matters most: inside and immediately downstream of the turbines. At the Borssele B location, under the dominant southwesterly flow, the wind farm simulation reduced the wind speed bias by about 82 percent compared with the no-farm run and by about 73 percent compared with raw ERA5 data. The Earth Mover’s Distance, a statistical measure of how well two distributions match, improved by roughly 74 percent and 65 percent respectively at the same site. Wind direction bias at Borssele B also dropped by about half when the turbines were represented. Across the wider domain, however, the picture is more nuanced: the Fitch scheme introduces an additional wind speed deficit downstream of the cluster, so the no-farm run can appear slightly more accurate at far-wake locations, a pattern consistent with earlier studies showing that the scheme tends to produce prolonged wakes.

Atmospheric stability emerged as a crucial controlling factor. Using the Obukhov length, a standard measure of whether buoyancy or wind shear dominates turbulence production in the boundary layer, the team classified conditions into seven categories and evaluated model performance in each. Very unstable conditions, in which buoyancy drives strong vertical mixing, prevailed over the three-year period, particularly in January and during late summer and autumn. The model performed best under near-neutral and moderately stable conditions, with correlation coefficients above 0.80, while accuracy degraded under the most extreme stratification, where correlation fell to between 0.69 and 0.77 and the root-mean-square error climbed. Notably, the wind farm simulation outperformed the no-farm run at the intra-cluster site across all stability regimes, even under the very stable conditions known to produce the longest and most persistent wakes.

The analysis also revealed striking directional asymmetries in wake behavior. Under southwesterly winds, the strongest wind speed deficit originates in the densely built Belgian wind farms, where turbine density averages 10 to 12 megawatts per square kilometer, and remains nearly unchanged as the flow crosses the more widely spaced Borssele zone, where density is only 4 to 5 megawatts per square kilometer. Under northeasterly winds the sequence reverses: the deficit builds gradually through Borssele and then intensifies sharply as the flow enters the dense Belgian cluster. At wind speeds above 11 meters per second, the maximum simulated deficit reached about 2.75 meters per second under southwesterly flow but about 4.06 meters per second under northeasterly flow. The study also documented blockage effects, the modest slowing of wind upstream of the farms, extending roughly 8 to 10 kilometers ahead of the front row under southwesterly conditions and about 5 kilometers under northeasterly flow, with the effect weakening as wind speeds increase.

To test the model against an entirely independent data source, the team compared simulated wake signatures with synthetic aperture radar images from the European Space Agency’s Sentinel-1 satellites. These radar instruments measure the roughness of the sea surface, which reflects centimeter-scale waves generated by the instantaneous wind stress, allowing wind speed at 10 meters height to be retrieved at 500-meter pixel resolution. For four carefully selected wake events, the wind farm simulation captured the larger-scale structure and extent of the wakes visible in the satellite images, including one case in which a prolonged wake extended more than 50 kilometers downstream under stable-neutral stratification. Quantitatively, the mean absolute error along transects through the cluster improved by 81 percent in the intra-farm region and 85 percent downstream compared with the no-farm simulation in the best-captured event.

The study is candid about its limitations. The model inherits a systematic negative wind speed bias from its ERA5 boundary conditions, which propagates into both simulations, and some of the apparent accuracy of the no-farm run likely reflects error cancellation with that biased input. The choice of the turbulent kinetic energy coefficient in the Fitch scheme remains debated; the team tested values of 0, 0.25, and 1 in a six-month sensitivity analysis and found that no single value consistently outperformed the others, settling on the WRF default of 0.25. The Obukhov length itself becomes uncertain under strongly stable conditions, when Monin-Obukhov similarity theory is known to break down. The authors suggest that future work should explore alternative parameterizations, such as the explicit wake parameterization scheme, and different boundary layer physics configurations.

For the offshore wind industry, the implications are significant. Wakes are the dominant driver of power losses in offshore wind farms, cutting energy extraction by 10 to 20 percent relative to lone-standing turbines, and as more clusters are built in each other’s vicinity, neighboring farms will increasingly sit inside each other’s wakes. Reliable multi-year simulations at kilometer scale, validated against real measurements, are essential for siting decisions, power forecasting, and estimating how future installations will affect existing ones. This three-year experiment demonstrates that a well-configured mesoscale model with a wind farm parameterization can do that job credibly, particularly within the waked regions themselves, while also mapping out precisely where the remaining uncertainties lie. As Europe races to multiply its offshore wind capacity, tools like this will help ensure that the next generation of wind farms is planned with a clear-eyed view of the atmosphere they must share.

Subject of Research: Long-term validation of mesoscale wind farm wake modeling in the North Sea using lidar and satellite radar observations

Article Title: Assessing the accuracy of a 3-year high-resolution mesoscale wind farm wake simulation with lidar and satellite radar data

Article References: Palatos-Plexidas, A., Gremmo, S., van Beeck, J., De Cruz, L., & Munters, W. (2026). Assessing the accuracy of a 3-year high-resolution mesoscale wind farm wake simulation with lidar and satellite radar data. Wind Energy Science, 11(9), 3555-3585. https://doi.org/10.5194/wes-11-3555-2026

Image Credits: AI Generated

DOI: 10.5194/wes-11-3555-2026

Keywords: offshore wind energy, wind farm wakes, WRF model, Fitch parameterization, North Sea, lidar, synthetic aperture radar, atmospheric boundary layer, atmospheric stability, mesoscale modeling, wind speed deficit, Sentinel-1

The research, led by Lennart Vogt of the University of Stavanger together with Julia Gottschall of Fraunhofer IWES and Jasna Bogunović Jakobsen of the University of Stavanger, draws on measurements collected at the Testfeld BHV site in Bremerhaven, on the east bank of the Weser estuary along the North Sea coast. The site hosts an Adwen AD8-180 prototype wind turbine and an IEC-compliant meteorological mast equipped with sonic and cup anemometers at several heights. Crucially, the team also deployed three synchronized short-range continuous-wave lidars based on the Short-Range WindScanner technology developed at the Technical University of Denmark. Between October 2021 and April 2022, the three lidars scanned a bow-tie-shaped pattern in a vertical plane 125 metres from the turbine, centred at hub height, allowing the full three-dimensional wind vector to be reconstructed at points spread across the plane.

The scale of the dataset is one of the study’s most striking features. The researchers analysed nearly 500 hours of lidar measurements acquired over about 90 days of scanning, alongside 533 days of mast data collected between January 2021 and August 2022. After rigorous quality control, which removed wake-affected samples, non-stationary records, and cases with unrealistic fluctuations, roughly 7,500 hours of sonic anemometer data, 8,000 hours of cup anemometer data, and 700 hours of lidar data remained available for spectral analysis. From these records, the team derived the fundamental building blocks of turbulence characterisation: auto-spectra describing how turbulent energy is distributed across frequencies, integral length scales describing the size of the largest coherent eddies, and spatial coherence describing how correlated the wind is at two points separated in space.

The technical approach involved reconstructing the three wind velocity components from the line-of-sight velocities measured by each lidar, using the known scanning azimuth and elevation angles of the instruments. Each bow-tie scan took two seconds, with the lidars sampling radial velocities at 322 hertz. Time series were extracted at grid cells along the scanning trajectory, with an effective temporal resolution of 1 hertz at the centre of the pattern. The spectra were then classified according to atmospheric stability, quantified by the non-dimensional stability parameter zeta, the ratio of measurement height to the Obukhov length, which expresses the balance between buoyancy-driven and shear-driven turbulence production. Fifteen stability classes were used, spanning highly unstable convective conditions through neutral stratification to highly stable nighttime regimes.

The results reveal how profoundly atmospheric stability reshapes the turbulence that wind turbines experience. Under unstable, convective conditions, the combined influence of mechanical shear and thermal buoyancy produces a pronounced spectral plateau in the horizontal velocity components, a broad band of enhanced low-frequency energy that the standard Kaimal model used in turbine design does not capture. As conditions approach neutral, the plateau narrows and eventually gives way to the familiar single spectral peak. Under stable stratification, negative buoyancy suppresses microscale turbulence, shifting the spectral peak toward higher frequencies while a distinct spectral gap opens up, separating the turbulence peak from a reservoir of low-frequency energy attributed to mesoscale motions. These large, quasi-two-dimensional structures are typically neglected in aeroelastic simulations, yet they become increasingly relevant for floating wind turbines whose platform natural frequencies lie on the order of several minutes.

The integral length scales tell a parallel story. Under neutral conditions, the along-wind length scale ranged from roughly 100 to 250 metres, the cross-wind scale from 30 to 100 metres, and the vertical scale from 10 to 50 metres. Moving from neutral to slightly unstable conditions, all three length scales increased, consistent with buoyancy enhancing turbulence generation. From neutral to slightly stable conditions, they decreased substantially as buoyancy flipped from amplifying to suppressing turbulence, and the ratio of along-wind to vertical length scales roughly doubled, reflecting the flattening of turbulent eddies as vertical mixing weakened. Intriguingly, under highly stable conditions the horizontal length scales increased slightly again, which the authors attribute to mesoscale motions being captured within the 30-minute averaging window.

One of the study’s central contributions is a careful cross-comparison of the three measurement technologies. The sonic anemometers, sampling at 20 hertz, delivered reliable spectra up to approximately 2 to 6 hertz. The cup anemometers, despite operating at only 1 hertz, agreed well with the sonics up to about 0.07 hertz, above which spatial averaging over the cup geometry and temporal filtering by the rotor inertia attenuated the high-frequency signal. The lidar system showed strong agreement with the sonic measurements at low and intermediate frequencies for all three velocity components, but its finite probe volume, ranging from 10 to 40 metres depending on focus distance, caused attenuation above roughly 0.04 hertz. This meant the along-wind turbulence peak was captured across all stability regimes, but the vertical velocity peak could not be fully resolved. The authors caution that lidar-based spectral estimates should be interpreted carefully in the absence of a sonic reference, particularly for the vertical component.

Perhaps the most valuable outcome concerns spatial coherence, the two-point statistic that governs how aerodynamic loads distribute across a rotor. The bow-tie scanning strategy proved highly suitable for coherence analysis, offering flexibility in separation direction, distance, and height that conventional mast-based measurements cannot match. Vertical and lateral coherence were estimated for separations ranging from 10 to 160 metres at heights up to 230 metres. The coherence of all velocity components decreased from unstable to stable conditions as turbulent structures shrank, and lateral coherence was systematically higher than vertical coherence, especially under stable stratification. Notably, the high-frequency attenuation that plagued the auto-spectra had little effect on coherence estimates, because the attenuation occurred at frequencies beyond those governing coherence decay, and partly cancelled when cross-spectra were normalised by the correspondingly attenuated auto-spectra.

From these observations, the team built an improved coherence model. The classical Davenport formulation assumes coherence decays exponentially with a normalised wavenumber and reaches unity at zero frequency, an assumption that fails for large separations and low heights. By introducing a scaling coefficient that permits coherence below unity at zero wavenumber, and by expressing both the scaling and decay coefficients as exponential functions of the ratio of separation distance to mean measurement height, the researchers arrived at a three-parameter model that accurately reproduces the observations for all velocity components. The fitted coefficients, tabulated as functions of atmospheric stability, are intended as inputs for generating synthetic wind fields in dynamic turbine response simulations. The model is recommended for normalised separations up to 0.5, with additional measurements at larger separations suggested to extend its validity.

The implications for wind energy are considerable. Previous studies have shown that reduced lateral coherence increases tower torsion on large turbines, while highly coherent inflow amplifies surge and pitch motions and mooring line fatigue on floating platforms. By providing stability-dependent spectral and coherence parameters grounded in nearly two years of field data, this work gives turbine designers a more realistic picture of the winds their machines will actually encounter, particularly in the coastal transition zone where flows alternate between overland and overwater fetch. The observed diurnal cycle, with unstable conditions peaking near 50 percent around midday and stable conditions dominating nighttime hours at up to 75 percent, underscores how dramatically the turbulence climate can shift within a single day. As turbines continue to grow and floating platforms push into deeper waters, datasets of this kind will become essential for ensuring that the synthetic winds used in certification simulations reflect the full richness of the real atmosphere.

Subject of Research: Atmospheric turbulence characterization for wind energy using triple-lidar and mast measurements in a near-coastal environment

Article Title: Turbulence characterization in near-coastal environment using triple short-range lidars and mast anemometry

Article References: Vogt, L., Gottschall, J., & Bogunović Jakobsen, J. (2026). Turbulence characterization in near-coastal environment using triple short-range lidars and mast anemometry. Wind Energy Science, 11(9), 3587-3614. https://doi.org/10.5194/wes-11-3587-2026

Image Credits: AI Generated

DOI: 10.5194/wes-11-3587-2026

Keywords: wind energy, atmospheric turbulence, lidar, spatial coherence, atmospheric stability, wind spectra, integral length scales, boundary layer meteorology, wind turbine loads, Kaimal spectrum, coastal site, anemometry

The study, carried out by Pravat Rabi Naskar of the Meteorological Watch Office Kolkata at the India Meteorological Department and Gyan Prakash Singh of the Department of Geophysics at Banaras Hindu University, draws on radiosonde and radiowind observations collected at 00 UTC for every winter season from December 1990 through February 2022. These balloon-borne instruments, launched routinely to profile temperature, humidity and wind through the depth of the troposphere, provide one of the longest continuous records of vertical atmospheric structure available anywhere in the region. The authors supplemented this archive with ERA5 reanalysis vertical velocity data from the Copernicus Climate Change Service, allowing them to connect the observed inversion behavior to the large-scale motions of the atmosphere above the Gangetic delta.

Thermal inversions come in several flavors, and distinguishing them matters enormously for forecasting. A surface-based inversion forms on clear, calm nights when the ground radiates heat to space and chills the air in contact with it. A subsidence inversion, by contrast, forms aloft when air in a high-pressure system sinks slowly from above; as the descending air compresses, it warms at a rate faster than the environmental lapse rate, creating a warm layer that floats above the cooler air below. The Kolkata analysis found that the winter inversions between the 1000 and 500 hectopascal pressure levels — a layer stretching from near the surface to roughly five kilometers altitude — are overwhelmingly of the subsidence type, pointing to the dominant role of large-scale atmospheric descent rather than purely local nighttime cooling.

One of the most striking trends the researchers documented concerns multiplicity. The percentage of winter days on which more than one thermal inversion appears within that 1000 to 500 hectopascal column is increasing at a rate of approximately one percent per year. Multiple stacked inversion layers are a well-known signature of complex vertical structure in the lower troposphere, and studies from eastern China and northwest China have linked such multilayer configurations to severe haze episodes, because each warm layer acts as a separate barrier to vertical mixing. Over Kolkata, the steady rise in double-inversion days suggests that the lower troposphere is becoming increasingly stratified in winter, a structural change that could compound the region’s already serious wintertime pollution problems.

The geometry of the inversions is changing too, and in a consistent direction. Over the 33-winter record, the height of the inversion top has been dropping at roughly 28 meters per year, while the base of the inversion has descended at about 20 meters per year. Because the top is falling faster than the base, the inversion layer itself is thinning by approximately 7 meters annually. A thinner inversion layer is not a weaker one; on the contrary, compressing the temperature contrast into a narrower vertical span can sharpen the stability of the layer, making it a more effective lid on the atmosphere beneath. The authors also found that inversion intensity — the temperature difference across the inversion — is increasing, although the inversion gradient, the temperature change per unit height, shows no statistically significant trend, presumably because the intensifying temperature jump is being spread across a shrinking layer.

What physical mechanism could drive this systematic downward migration and intensification? The most likely candidate is a change in the large-scale subsidence pattern over the region, which the ERA5 vertical velocity data were brought in to examine. Sinking motion in the mid-troposphere warms the air through compression, and the altitude at which that warming is strongest determines where the inversion forms. If the descending branch of the regional circulation has shifted lower, or if subsidence has strengthened in the layers just above the boundary layer, the inversion would be expected to form closer to the ground and to grow warmer relative to the air beneath it. Such changes in atmospheric stagnation have been flagged in recent work on Indian air pollution, including studies linking circulation-induced stagnation changes to surface PM2.5 concentrations across the subcontinent.

Perhaps the most consequential finding of the study concerns the planetary boundary layer — the shallow, turbulent skin of the atmosphere that responds directly to the surface and within which nearly all human emissions of pollutants are first released. The researchers found that the planetary boundary layer height over Kolkata airport sits consistently below the base of the subsidence inversion, and that it has been decreasing significantly at a rate of around 5.5 meters per year over the study period. This relationship is physically coherent: a subsidence inversion whose base lies just above the boundary layer suppresses the turbulent entrainment that normally allows the boundary layer to grow during the day. As the inversion base sinks, it effectively presses down on the boundary layer, compressing the volume of air into which vehicle exhaust, industrial emissions and dust are injected.

The implications for air quality are direct and sobering. A lower boundary layer means a smaller mixing volume, so the same quantity of emissions produces higher surface concentrations. A stronger, thinner inversion overhead makes that confinement even more effective by preventing any exchange of air across the stable layer. Research from around the world — from the Sichuan Basin in China to Hanoi, Istanbul, Tehran and the Canary Islands — has repeatedly documented the tight coupling between inversion characteristics and particulate pollution, and a recent national cohort study in China even linked thermal inversion exposure to increased risk of incident cardiovascular disease. For Kolkata, a megacity whose winter air already ranks among the most polluted anywhere, a trend toward stronger and lower inversions represents a slow tightening of the atmospheric vise.

Fog and aviation are equally implicated. The same authors previously documented a dense fog episode over Kolkata airport in January 2025 and traced its synoptic origins, and the present study provides the climatological backdrop against which such events unfold. Inversions trap moisture near the surface overnight, and shallow, intense inversions favor the radiative cooling and moisture convergence that produce dense fog — the single most disruptive weather phenomenon for airport operations in northern and eastern India. A boundary layer that is steadily losing height, capped by an inversion that is steadily losing altitude and gaining strength, is a boundary layer primed for more frequent and more persistent fog, with cascading effects on visibility, flight delays and safety margins during the December-to-February peak travel season.

What makes this study particularly valuable is its length and its focus on a single, operationally critical location. Thirty-three winters of consistent 00 UTC soundings from one site remove much of the noise that afflicts shorter or geographically scattered records, and the airport setting ties the results directly to a real-world stakeholder. The picture that emerges is of a lower troposphere in structural transition over the Bengal delta: inversions multiplying, sinking, thinning and strengthening, while the boundary layer beneath them contracts year by year. Whether these trends are driven by shifting regional circulation, by warming sea surface temperatures in the Bay of Bengal, by aerosol-induced changes in the radiation balance, or by some combination of all three remains an open question — but the direction of change is unambiguous, and it points toward winters in which the air over Kolkata becomes steadily harder for the atmosphere to flush clean.

Subject of Research: Long-term trends in winter thermal inversion characteristics and planetary boundary layer height over Kolkata airport

Article Title: Characteristics of winter thermal inversions over Kolkata airport

Article References: Naskar, P. R., & Singh, G. P. (2026). Characteristics of winter thermal inversions over Kolkata airport. Theoretical and Applied Climatology, 157(10), Article 645. https://doi.org/10.1007/s00704-026-06575-3

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06575-3

Keywords: thermal inversion, Kolkata airport, radiosonde, planetary boundary layer, subsidence, air pollution, fog, ERA5 reanalysis, winter climate, atmospheric stability, India, boundary layer height