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 area itself is almost engineered for fog. The Eastern Gangetic Plain spans roughly 94,000 square kilometres of flat land lying mostly below 100 metres, wedged between the Himalayas to the north, with peaks exceeding 8000 metres, and the Chotanagpur Plateau to the south. This basin-like geometry traps moisture and aerosols, while westerly and northwesterly winds in winter funnel pollutants from western India and Pakistan into the region like a low-level atmospheric river, supplying abundant condensation nuclei. Add elevated soil moisture from irrigation and river systems, recurrent cold-wave episodes, and weak pressure gradients that keep horizontal winds sluggish, and the ingredients for persistent dense fog assemble almost automatically each December and January. At Jay Prakash Narayan International Airport in Patna, situated centrally in the basin at about 53 metres above sea level, runway visual range regularly falls below operational minima, producing cascading delays, diversions, and cancellations.

Two contrasting fog episodes dominated the study window, and their differences turned out to be scientifically revealing. The first, from 16 to 19 December, was synoptically forced: a deep, positively tilted upper-level trough anchored over northwest India drove strong southwesterly jet flow above 20 metres per second, cold-air advection, and a spatially coherent signature of subsidence at 700 hectopascals, with pressure velocities between minus 10 and minus 20 times ten to the minus two pascal-seconds. This descending branch of an active western disturbance reinforced a capping inversion over the fog layer. The second episode, from 23 to 27 December, unfolded after the trough moved eastward and quasi-geostrophic forcing for ascent weakened. Vertical motion over Patna became fragmented and nearly neutral, and the boundary layer shifted into a radiative-thermodynamic regime, exquisitely sensitive to the diurnal cycle, yet paradoxically more stable and longer-lived than the first.

The turbulence diagnostics from the second episode were extraordinary. Turbulent kinetic energy, which peaked near 60 square metres per second squared around 23 and 24 December, collapsed to nearly zero by 25 December and stayed there. Wind shear fell from about 10 times ten to the minus three per second to between 3 and 5 times the same unit. The bulk Richardson number, a dimensionless ratio weighing buoyant suppression of mixing against mechanical shear production, climbed to roughly +300 on 24 December and then held between +100 and +200 for three days, the highest sustained values in the entire record. Ventilation coefficients, the product of boundary-layer height and 10-metre wind speed, crashed from about 6000 square metres per second to 1000 to 1500, indicating the most severe atmospheric dispersion limitation observed. Mixing heights were squeezed to just 250 to 350 metres. In effect, every mechanism that could erode the fog was switched off simultaneously.

From the radiosonde analysis emerged what the authors call a double-lock mechanism for fog persistence. Persistent dense fog required not one condition but a coordinated set: surface-based temperature inversions with bases below roughly 150 to 200 metres, moderate thermal strengths of 2 to 6 degrees Celsius, vertical depths exceeding 2000 metres, bulk Richardson numbers above 10 to 20, and ventilation coefficients below about 5000 to 6000 square metres per second. When normalized inversion strength and depth both exceeded 0.5 alongside these stability and ventilation constraints, persistent fog reliably followed. The 2000-metre depth threshold proved especially diagnostic, separating brief events lasting under six hours from prolonged ones exceeding fifteen hours, consistent with mixed-layer theory in which deeper capping inversions resist turbulent erosion far longer than shallow ones.

Perhaps the most striking single observation came on 29 December, after the main episodes had ended. A powerful inversion with a thermal strength of 10.5 degrees Celsius sat with its base above 700 metres, and it produced no fog at all. The reason is geometric rather than thermodynamic: an elevated inversion decouples the stable layer from surface moisture, so however strong the temperature jump, it cannot trap water vapour where fog droplets need to condense. Inversion placement, the study concludes, matters more than inversion intensity. This finding directly challenges forecasting practice that weights inversion magnitude alone, and it underscores the primacy of surface-atmosphere coupling in continental fog formation. During the same transition, the Richardson number swung from +60 to -50 within twenty-four hours, ventilation coefficients surged to 8000 to 10000 square metres per second, and inversion strength collapsed from 10.5 to below 2 degrees Celsius, a coordinated multi-parameter disaggregation triggered by synoptic rearrangement.

To understand how fog actually dies, the researchers built a rule-based classification framework assigning each of 49 dissipation events to radiative, subsidence-driven, mechanical, or mixed categories, ordered by precedence from synoptic vertical motion through surface wind and shear to residual radiative signatures. The result was unambiguous: mechanically driven processes, whether acting alone or jointly with other boundary-layer dynamics, dominated fog erosion. Mixed-mechanical events accounted for 51.0 percent of cases and pure mechanical dissipation another 20.4 percent, meaning wind shear intensification and turbulent mixing governed roughly 71 percent of all fog clearance at Patna. Mixed-subsidence pathways contributed 14.3 percent, while pure radiative, pure subsidence, and mixed-radiative mechanisms together managed only about 8 percent, a reflection of winter’s short days, low solar angles, and optically thick fog layers that starve the surface of warming radiation. A small residual of 6.1 percent remained diagnostically ambiguous, clustered under extreme stability where all dissipative signals fell below detection thresholds.

The framework was stress-tested and verified rather than merely asserted. A sensitivity analysis perturbing each classification threshold by plus or minus 10 and 20 percent of its interquartile range showed that radiative thresholds were highly stable, with zero reclassification for several parameters, while mechanical thresholds, ironically governing the dominant category, were the least robust, reclassifying up to 39 percent of events under the largest perturbation. Binary verification of the fog-favourable criterion, a transitional inversion with base between 50 and 500 metres, against 384 matched surface observations yielded an accuracy of 0.77, precision of 0.76, recall of 0.33, an F1-score of 0.46, and a Heidke Skill Score of 0.34, confirming genuine predictive skill beyond climatology. The high-precision, low-recall profile positions the tool as a conservative, low-false-alarm mechanism-attribution instrument rather than an exhaustive all-height fog forecaster, a distinction with real operational consequences for aviation safety managers who must balance disruption costs against missed warnings.

The practical payoff is a companion Fog Dissipation Potential Index that distills the multi-parameter picture into short-lead operational guidance, distinguishing transient from persistent fog and clarifying where predictability simply runs out under extreme stability. The authors benchmarked their diagnostic approach against a complementary decision-tree nowcasting system developed for Delhi’s Indira Gandhi International Airport, noting that the two converge on similar wind-speed regime boundaries and could be paired operationally, since the decision tree’s known failure mode, fog onset without relative humidity saturation at tower level, is precisely the signature this framework would attribute to subsidence or mechanical forcing. The team is candid about limitations: aerosol indicators such as PM2.5 and cloud condensation nuclei were deliberately excluded, turbulent kinetic energy cannot be resolved from standard ERA5 fields, thresholds are site- and season-specific, and some moisture parameters are collinear by construction. Even so, for one of the most fog-burdened and economically vital regions on Earth, the study delivers something rare: physically interpretable, quantitatively verified thresholds that explain not just when the fog comes, but why it stays, and what finally sets the plains free.

Subject of Research: Boundary-layer thermodynamic controls on winter fog persistence and dissipation over the Eastern Gangetic Plains

Article Title: Fog persistence and dissipation over the Eastern Gangetic Plains insights from boundary layer thermodynamics during 16 to 31 December 2025

Article References: Shankar, A., Kant, S., Anand, A., & Sarthi, P. P. (2026). Fog persistence and dissipation over the Eastern Gangetic Plains insights from boundary layer thermodynamics during 16 to 31 December 2025. Discover Geoscience, 4(1), Article 307. https://doi.org/10.1007/s44288-026-00684-2

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00684-2

Keywords: fog, Indo-Gangetic Plain, boundary layer, temperature inversion, Richardson number, ventilation coefficient, radiosonde, ERA5 reanalysis, aviation meteorology, fog dissipation, Patna Airport, western disturbances

The study drew on an unusually rich observational foundation. The researchers compiled routine and special aviation weather reports issued every thirty minutes from three airports serving Istanbul: Sabiha Gökçen, identified in the study by its ICAO code LTBA, situated on the city’s Anatolian side; Istanbul Airport, coded LTFM, on the northwestern shore near the Black Sea; and Atatürk’s successor regional field LTFJ in the eastern inland part of the metropolitan area. These half-hourly reports, which record visibility, cloud base, temperature, dew point, wind, and pressure, were supplemented by radiosonde soundings that profile the vertical structure of the lower atmosphere and by microphysics satellite imagery capable of distinguishing cloud properties at night. From this combined dataset, the team applied an objective physical typing algorithm that sorts every fog event into one of five mechanistic categories: advection fog, radiation fog, precipitation fog, cloud-base lowering, and evaporation fog.

The classification matters because each fog type carries a distinct physical signature and a distinct forecasting challenge. Advection fog forms when moist air flows horizontally over a cooler surface, chilling the air to its dew point; it is the classic fog of coastal regions where warm, humid maritime air drifts across cold water or cold land. Radiation fog, by contrast, is born on clear, calm nights when the ground radiates heat skyward, cooling the air layer in contact with it until condensation begins. Precipitation fog arises when rain falls through drier air and evaporates, pumping moisture into the boundary layer. Cloud-base lowering describes stratus decks that descend until they effectively become fog at the surface, while evaporation fog forms when water evaporates from a warm surface into cooler air above. Sorting events into these categories allowed the researchers to trace each airport’s fog regime back to its thermodynamic roots.

The spatial contrasts the analysis uncovered are striking. Over the extended historical period from 2012 to 2023, the eastern inland station LTFJ logged an average of 17.2 fog days per year, more than five times the 3.2 fog days recorded annually at the southern urban station LTBA. The dominant fog mechanism also differed fundamentally between the two sites. At LTFJ, advection fog accounted for 53 percent of events, a signature of moist air being transported into the area and chilled over locally cool surfaces. At LTBA, radiation fog dominated at 55 percent, and the researchers attribute this to the interplay of urban heat island effects and topographic shielding around the airport. The urban heat island raises nighttime minimum temperatures, making it harder for the near-surface air to reach saturation, while surrounding terrain can block or channel the drainage flows and moist intrusions that feed fog elsewhere in the city.

When the analysis was restricted to the common period from 2019 to 2023, during which all three airports provided overlapping records, the three-way comparison sharpened the picture further. LTFJ recorded 52 fog-affected days over that window, LTBA just 12, and LTFM, the northern airport closest to the Black Sea coast, 57. Advection fog again led the statistics at both LTFJ, where it made up 50 percent of events, and LTFM, where it reached 56 percent. The elevated fog frequency at LTFM fits its coastal geography: proximity to the Black Sea supplies abundant low-level moisture, and the temperature contrasts between sea and land provide the cooling mechanism that advection fog requires. That two airports separated by a few dozen kilometers within the same metropolitan area can differ by nearly a factor of five in annual fog days underscores how strongly microclimate and siting control fog occurrence in complex coastal terrain.

The temporal structure of Istanbul’s fog is equally distinctive. The analysis demonstrated a pronounced winter peak, with fog activity concentrated in November and February, and a complete absence of fog during the summer months. This seasonality reflects the underlying thermodynamics. Winter brings longer nights, weaker solar heating, lower sea-surface and land-surface temperatures, and a greater supply of moist air masses moving across relatively cool waters, all of which favor the saturation of the boundary layer. Summer, with its strong insolation and warm surfaces, keeps the near-surface air well above its dew point even under maritime influence. For aviation planners and forecasters, the message is that Istanbul’s fog risk is not evenly distributed through the year but clusters sharply in the cold season, with November and February emerging as the critical months for low-visibility operations.

Perhaps the most consequential finding concerns spatial coherence. Between 2019 and 2023, the researchers evaluated how often fog occurred simultaneously at all three observation nodes, and the answer was remarkably small: only four days in five years saw all three airports shrouded at once. In other words, Istanbul’s fog is profoundly fragmented, appearing at one airport while the others remain clear. This localized fragmentation has direct operational implications. Flight diversions, holding patterns, and ground delays are typically triggered by visibility conditions at a single destination airport, so a fog climatology built on city-wide averages would badly misrepresent the actual risk. A pilot approaching one Istanbul facility may encounter dense fog while another facility across the city operates normally, a pattern that only station-specific, mechanism-aware forecasting can capture.

The study’s methodology also highlights the value of combining conventional surface observations with remote sensing. Radiosonde profiles reveal the depth and structure of the temperature inversion layers that cap and nourish radiation fog, while night microphysics satellite imagery allows researchers to track fog patches across the metropolitan area between routine observation times, confirming events that a single surface station might miss or mischaracterize. Supplementary analyses in the paper examined the distribution of fog-affected observations by cloud cover, cloud base height, runway visual range, and surface pressure, along with satellite composites of notable fog episodes in February 2019 and November 2021, building a multi-layered empirical baseline for the region.

Istanbul is far from alone in facing the forecasting puzzle of urban fog, and the study situates itself within a growing international literature. Comparable event-based climatologies have been constructed for New York City, Casablanca, Abu Dhabi, Cape Town, Belgrade, Lisbon, Perth, Shanghai Pudong, and airports across South Korea, Turkey’s northeast, and Argentina, each confirming that fog regimes are tightly bound to local geography. Recent work has pushed further, exploring machine learning approaches to low-visibility prediction, Bayesian decision networks for airport fog forecasting, and the dramatic forecast improvements that come from including irrigation effects in numerical weather prediction over northern India. The Istanbul analysis contributes a critical empirical foundation for such efforts in the eastern Mediterranean, where complex topography and competing maritime influences make statistical or machine-learning fog prediction especially dependent on high-quality, mechanism-resolved training data.

The broader significance of the work lies in what it says about fog as an urban boundary-layer phenomenon. In a megacity, the surface is a patchwork of heat-retaining concrete, vegetated hills, cool water bodies, and engineered drainage basins, and the atmosphere within the lowest few hundred meters responds to that patchwork in ways that standard synoptic-scale models struggle to resolve. By demonstrating that fog frequency varies fivefold within a single metropolitan area and that simultaneous city-wide fog is a rarity, the Istanbul study makes a compelling case for high-resolution, station-specific fog forecasting grounded in physical typing rather than raw visibility statistics. For the aviation sector, which loses millions of dollars annually to low-visibility delays worldwide, and for the growing community of researchers developing fog decision support systems, the message from Istanbul is clear: to predict fog in a megacity, one must first understand that the megacity is not a single climate but an archipelago of microclimates, each with its own fog story written in temperature, moisture, wind, and terrain.

Subject of Research: Long-term fog climatology and physical typing across three airports in the megacity of Istanbul

Article Title: A comparative long-term analysis of fog characteristics in the megacity of Istanbul

Article References: Yoğun, B., Aktaş, C., Bakır, G., Bozdemi̇r, R., Çeli̇k, Ş., & Özdemi̇r, E. T. (2026). A comparative long-term analysis of fog characteristics in the megacity of Istanbul. Theoretical and Applied Climatology, 157(10), Article 685. https://doi.org/10.1007/s00704-026-06617-w

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06617-w

Keywords: fog climatology, Istanbul, boundary-layer meteorology, advection fog, radiation fog, urban heat island, aviation meteorology, METAR observations, urban microclimate, low visibility, Türkiye, Theoretical and Applied Climatology