The findings strike at the foundation of modern whale-vessel strike risk assessments, which typically model collision danger by overlaying whale distribution maps with AIS-derived traffic data. AIS was designed for navigational safety, not conservation science. International and national rules mandate its use only for large commercial ships, towing vessels above a certain size, and passenger vessels carrying more than 150 people. Everything smaller — the speedboats, skiffs, sailboats, and small commercial craft that crowd urban waterways — can broadcast voluntarily, but most simply do not. Yet these vessels are fully capable of inflicting lethal force on a whale, and they operate without the route restrictions that constrain cargo ships and tankers.

To measure the gap, a team from The Marine Mammal Center, San Francisco State University, and the conservation technology group ProtectedSeas deployed the Marine Monitor, or M2, a compact, solar-powered, shore-based platform perched at Point Bonita in Marin County. The system combines three technologies: a commercial off-the-shelf X-band marine radar that detects and tracks vessels regardless of whether they broadcast AIS, an AIS receiver that captures the broadcasts of participating ships, and a pan-tilt-zoom camera that the software autonomously steers toward each detected target, snapping images roughly every two minutes. Because radar returns contain no identity information, the researchers manually reviewed thousands of camera images to classify each non-AIS vessel by type — recreational, sailing, commercial, fishing, or unknown.

The radar antenna had a nominal range of 16 nautical miles, but elevated coastal bluffs restricted the effective study area to roughly two to three nautical miles of the Golden Gate Strait. Within that zone, the radar updated vessel positions every two to six seconds, producing fine-grained tracks of speed, course, and duration that AIS broadcasts — spaced two to ten seconds apart for transiting vessels — could only partially match. The team’s software linked radar and AIS detections when they coincided within 100 meters, within 15 seconds, and with closely matching speed and course, allowing each vessel to be counted once and cleanly separated into AIS and non-AIS categories.

Over 47 sampled days spanning September 2021 through August 2022, the researchers manually classified 3,297 non-AIS tracks, discarding erroneous records and duplicates to leave 3,030 valid tracks. Those non-AIS vessels accounted for 81 percent of all daytime traffic through the strait. The composition was striking: no commercial fishing vessels transmitted AIS at all, 98 percent of pleasure craft went silent, and 82 percent of other vessel types did the same. In contrast, every cargo ship, tanker, and tug-and-tow in the study broadcast AIS, exactly as regulations require. The message is unambiguous — the vessels most visible to conventional risk models are precisely the ones least representative of actual traffic in this whale habitat.

Seasonal patterns deepened the concern. AIS traffic stayed remarkably stable across the year, with median daily counts hovering between 10 and 20 vessels in every season. Non-AIS traffic told a different story: daily counts surged significantly from spring into summer, when the median daily count reached 111 and the maximum hit 232 vessels. On the single busiest day, non-AIS boats made up 95 percent of all traffic. That timing matters enormously, because summer is exactly when humpback whales — listed as endangered or threatened under the U.S. Endangered Species Act depending on their breeding population — most frequently enter San Francisco Bay to feed. A risk model built on AIS alone would capture the steady drumbeat of cargo traffic while missing the seasonal wave of small boats that peaks precisely when whales are present.

The movement characteristics the team measured added further nuance. Non-AIS vessels, particularly small motorized and recreational craft, reached higher maximum speeds on average — 19.4 and 18.1 knots respectively — than any AIS category, with cargo ships topping out at 14.7 knots. Small motorized vessels also recorded the highest acceleration and deceleration rates, around 0.45 knots per second. These traits cut both ways: rapid speed changes demand faster operator reaction times to avoid a surfacing whale and make vessels less predictable to the animals themselves, yet they also grant small boats greater ability to maneuver out of a collision course — provided the operator sees the whale in time.

San Francisco’s notorious fog provided an unexpected natural experiment. Because radar penetrates fog while human eyes cannot, the researchers compared vessel behavior in clear versus foggy conditions using 991 and 346 tracks respectively. The result was sobering: the highest speeds reached by non-AIS vessels in clear weather remained essentially unchanged in dense fog. Median maximum speed dropped by less than one knot, and the 90th-percentile speeds — the most extreme values — showed no significant difference at all. Mariners apparently trust their onboard radar to navigate around other vessels and structures, but whales do not appear on radar screens. In reduced visibility, when a helmsman’s visual detection range collapses, maintaining high speed may substantially increase strike risk precisely when whales are hardest to see.

The study’s authors are careful about limitations. The camera-based classification worked only in daylight, so nighttime traffic — dominated by large commercial ships that do broadcast AIS — was excluded, meaning the true AIS share of 24-hour traffic is likely somewhat higher than 19 percent. Vessel classification from images was inherently subjective, mitigated by a standardized rubric and a single analyst reviewing all tracks. And the radar’s line-of-sight geometry confined the study to a narrow corridor at the strait’s mouth. Even so, the 81 percent figure aligns with a growing global literature: radar and visual surveys have found AIS transmission rates of just 72 percent in Boundary Pass on the Canada–U.S. border, 43 percent in coastal Scotland, 32 percent in the San Juan Islands, and 17 percent in Denmark — variation that means AIS-based risk maps can be dramatically wrong in some places and merely incomplete in others.

The stakes extend beyond fatal collisions. Vessel traffic of all sizes triggers acoustic, behavioral, and physiological responses in cetaceans that can drain energy reserves, interrupt feeding, displace animals, and interfere with communication. The gray whales that have taken up seasonal residence in San Francisco Bay since 2018 are particularly vulnerable: their Eastern North Pacific population lost more than half its members during the 2019–2023 Unusual Mortality Event, falling from roughly 27,430 to 12,950 animals, and a recent analysis found vessel strike was the leading determined cause of death among Bay-area carcasses. The M2 platform — mobile, cheap relative to military radar, and deployable anywhere with a coastline view — offers a practical path forward. By fusing radar, AIS, and camera data, coastal managers can finally see the whole fleet, build risk assessments that reflect reality, and target interventions such as speed reduction zones and outreach to recreational boaters, the demographic the data show is most likely to be sharing the water with whales entirely off the record.

Subject of Research: Shore-based radar monitoring of AIS and non-AIS vessel traffic to improve whale-vessel strike risk assessments at the entrance to San Francisco Bay

Article Title: Using a shore-based radar system to improve whale-vessel strike risk assessments at the entrance to San Francisco Bay

Article References: Rodriguez, P. N., King, S. C., Lane, R. S., O’Hern, J. E., George, K., Keener, W., Tougher, B., Zetterlind, V., & Hines, E. (2026). Using a shore-based radar system to improve whale-vessel strike risk assessments at the entrance to San Francisco Bay. Frontiers in Mammal Science, 5, Article 1888831. https://doi.org/10.3389/fmamm.2026.1888831

Image Credits: AI Generated

DOI: 10.3389/fmamm.2026.1888831

Keywords: whale-vessel strike, San Francisco Bay, AIS, marine radar, humpback whales, gray whales, vessel traffic monitoring, conservation technology, Golden Gate Strait, recreational boats, marine mammal conservation, fog

The Namib’s fog forms offshore within a shallow marine boundary layer capped by a temperature inversion, where cool air over the cold Benguela Current sustains a persistent stratus deck. When this cloudy marine air is advected onto land, whether it becomes fog depends entirely on the height of the cloud base. If the base sits low enough, the stratus intersects the gently rising terrain and delivers fog water; if it is lifted, the cloud passes overhead and the moisture never reaches the ground. Hipler’s team set out to identify the meteorological controls of this stratus altitude, a quantity that no operational satellite sensor can retrieve with the accuracy needed to distinguish fog from elevated low cloud.

To build a reliable ground truth, the researchers combined two long-term measurement campaigns. The FogNet station Coastal Met, located just 17 kilometers from the sea at the edge of the high fog belt, is equipped with Juvik-type passive fog collectors that record accumulated fog water in two-milliliter increments, while a ceilometer and SODAR from the 2017 NaFoLiCA campaign provided cloud-base heights and wind profiles. Days with low clouds identified in SEVIRI satellite imagery between 04:00 and 06:00 UTC, the time of maximum fog occurrence, were then classified: those with any fog water collected at the coastal station were labeled fog days, the rest low-cloud days. The resulting dataset spanned July 2014 to December 2020 and contained 749 fog days and 623 low-cloud days, with consistency checks showing that 97 percent of the fog events coincided with relative humidity above 95 percent.

The team then asked what distinguished the two classes on the large scale, using the ERA5 reanalysis to build median composites of meteorological conditions across all events. A striking dipolar pressure signature emerged. On fog days the South Atlantic High, the anticyclone that steers the stratus-producing winds, was weaker than on low-cloud days, while pressure over the southern African continent was higher. Because these differences held the same sign in all four seasons, the researchers could attribute them to a physical link with stratus altitude rather than mere seasonal coincidence. The two high-pressure systems, one over the ocean and one over the interior, thus emerged as the central characters in the Namib fog story.

The ocean-side mechanism works through the marine boundary layer itself. When the Atlantic High is strong, as in low-cloud situations, coast-parallel southeasterly winds at around 30 degrees south intensify by roughly 27 percent, driving enhanced cold-air advection over the relatively warm ocean. This sharpens the air-sea contrast and boosts upward latent and sensible heat fluxes, which increase buoyancy and vertical mixing and deepen the developing boundary layer. On fog days, the weaker Atlantic High means weaker winds, less cold advection and reduced upstream heat fluxes, so the boundary layer arrives at the Namibian coast up to 130 meters shallower along the entire coastline. A shallower boundary layer means a lower capping inversion and therefore a lower stratus base, allowing the cloud to scrape the ground at the coast instead of floating above it.

Over the continent, the higher pressures on fog days act on the desert’s distinctive thermotopographic circulation. At night the bare mountain surfaces of the Great Escarpment cool rapidly, generating a shallow, cold katabatic mountain-plain wind that drains toward the sea at depths of up to about a kilometer, with peak speeds between 250 and 500 meters above ground. Previous work had shown that this flow can mix with, undercut or even block the advected cloudy marine air, producing the puzzling near-surface northerly winds long observed during inland fog events. The new composites indicate that elevated continental pressure under clearer high-pressure skies strengthens this nocturnal drainage, shaping how the marine air interacts with the land. Because the katabatic air is cool, it cannot cap the marine layer from above; the inversion-lowering signal in the composites is therefore attributed mainly to the upstream ocean processes.

An out-of-season case study revealed a second, more dramatic mechanism. On the night of 22 to 23 September 2017, during the low-cloud season, fog formed at the coastal station with a cloud base consistently below 100 meters. A ridging high-pressure system over the subcontinent, following the passage of a mid-latitude cyclone, produced hot and dry subsiding easterly winds, a bergwind-type flow, with easterly speeds reaching about 20 meters per second and strong downward motion over the Escarpment. This adiabatically heated continental air pressed down on the top of the shallow coastal boundary layer, strengthening the inversion to temperatures of up to 28.7 degrees Celsius and confining the stratus to a narrow strip just 20 to 30 kilometers wide along the coast. The subsidence and inversion intensification in this single event exceeded even the composite mean for fog, illustrating how synoptic forcing can compress the entire fog system into a thin coastal band.

To test whether these identified drivers carry real predictive information, the team trained a logistic regression to classify fog versus low-cloud days using just three reanalysis-derived features: the strength of the Atlantic High, the strength of the continental High and a regional east-west pressure gradient. In cross-validation with carefully separated training and test windows, the simple model achieved 69.1 percent accuracy, beating a climatological baseline that guesses from seasonal probabilities by about 10 percent, with an F1-score of 0.622 versus 0.545. Notably, skill dropped during 2019 and 2020, coinciding with the Benguela Niño of October 2019 to January 2020, when coastal sea surface temperature anomalies exceeding 2 degrees Celsius disrupted the normal coupling between the pressure pattern and the boundary layer. Because the model relies solely on pressure-based features, it cannot capture such ocean-driven shifts, pointing to sea surface temperature information as a promising addition for future forecasts.

The findings matter well beyond the Namib. Climate models project that southern Africa will become warmer and drier, and any reduction in fog frequency would ripple through an ecosystem exquisitely tuned to this moisture source, from dune beetles that drink fog droplets to succulents and microbial crusts. By establishing that the altitude of the advected stratus, and hence the geography of fog delivery, is controlled by the interplay of the Atlantic and continental highs, the study provides a mechanistic basis for anticipating how fog patterns might shift as these pressure systems respond to climate change. It also demonstrates, more broadly, that reanalysis data combined with modest ground observations can resolve distinctions that satellites alone cannot, opening a path toward spatially coherent fog maps for one of Earth’s most enigmatic coastal deserts.

Subject of Research: Meteorological controls of marine stratus altitude and fog occurrence in the Namib Desert

Article Title: Synoptic and regional-scale meteorological controls of stratus altitude in the Namib Desert

Article References: Hipler, V., Andersen, H., Spirig, R., Vogt, R., Piketh, S., Adler, B., & Cermak, J. (2026). Synoptic and regional-scale meteorological controls of stratus altitude in the Namib Desert. Atmospheric Chemistry and Physics, 26(19), 14367-14384. https://doi.org/10.5194/acp-26-14367-2026

Image Credits: AI Generated

DOI: 10.5194/acp-26-14367-2026

Keywords: Namib Desert, fog, marine stratus, boundary layer, South Atlantic High, katabatic wind, ERA5 reanalysis, bergwind, temperature inversion, satellite remote sensing, logistic regression, coastal meteorology

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, 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

The stakes could hardly be higher. Every video stream, industrial sensor reading, autonomous vehicle decision, and healthcare monitoring alert now traverses a layered computing infrastructure in which computation may happen in a hyperscale data center, a regional fog node, or a device at the very edge of the network. Task scheduling, the process of deciding which computing resource will execute which job and when, directly determines latency, throughput, cost, and energy consumption. Load balancing, its close companion, spreads work evenly across available resources to prevent bottlenecks and failures. When either process falters, the consequences ripple outward: service level agreements are violated, users experience lag, servers idle wastefully or overheat, and in safety-critical applications such as connected vehicles or remote surgery, delays can be dangerous. The survey emphasizes that quality of service, resource efficiency, and overall system performance all hinge on how well these two intertwined problems are solved.

What distinguishes this survey from its predecessors is its insistence on a unified, comparative lens. Earlier reviews, the authors note, frequently examined cloud, fog, and edge environments in isolation, obscuring the trade-offs that emerge when workloads migrate between layers. By contrast, the new analysis traces how the optimal scheduling strategy shifts with position in the continuum. In the cloud, where resources are abundant but network latency to end users is significant, meta-heuristic algorithms dominate the literature. These nature-inspired optimizers, including genetic algorithms, particle swarm optimization, ant colony optimization, artificial bee colony methods, grey wolf optimizers, whale optimization algorithms, and cuckoo search, iteratively explore vast search spaces to find near-optimal assignments of tasks to virtual machines. The survey catalogues their underlying mechanisms in detail, from the mutation and crossover operators borrowed from biological evolution to the physics-inspired strategies modeled on phenomena such as simulated annealing and gravitational search.

In fog and edge environments, the picture changes dramatically. Resources are scarce, geographically dispersed, and volatile; network conditions fluctuate; and decisions must often be made in milliseconds. Here, the survey finds, the literature increasingly leans on artificial intelligence and machine learning, particularly reinforcement learning, in which an agent learns a scheduling policy through trial-and-error interaction with its environment, and deep reinforcement learning, which uses deep neural networks to handle enormous state spaces that would overwhelm tabular methods. The reviewed studies span Q-learning approaches for energy-efficient cloud scheduling, deep Q-network variants with improved exploration strategies, multi-agent deep reinforcement learning for distributed fog systems, soft actor-critic methods for task offloading in the internet of vehicles, and meta-reinforcement learning techniques designed to adapt rapidly to unseen edge scenarios. Applications range from smart factories and smart cities to healthcare management systems, where prioritizing critical patients over routine workloads can be a matter of life and death.

The technical anatomy of these learning-based schedulers is one of the survey’s richest contributions. In a typical deep reinforcement learning formulation, the state captures the current load of servers, queue lengths, task characteristics, and network conditions; the action space represents possible task-to-resource assignments or offloading decisions; and the reward function encodes objectives such as minimized makespan, reduced energy consumption, lower latency, or fewer service level agreement violations. Because real deployments rarely optimize a single metric, multi-objective formulations have proliferated, often combining reinforcement learning with meta-heuristics in hybrid schemes. One reviewed study, for example, coupled an artificial bee colony algorithm with reinforcement learning to balance competing objectives in cloud environments, while others integrated fuzzy logic to handle the uncertainty inherent in fog-cloud workflow scheduling. The authors also document the role of federated reinforcement learning, which allows edge nodes to learn collaboratively without centralizing sensitive data, and long short-term memory networks used for proactive scheduling of streaming applications before demand spikes materialize.

Measuring success in this field is itself a subtle science, and the survey devotes substantial effort to standardizing the metrics by which scheduling algorithms should be judged. Its appendix enumerates formal definitions for a comprehensive battery of indicators: makespan, the completion time of the last task in a batch; throughput, the number of tasks completed per unit time; response time and turnaround time; resource utilization; degree of imbalance; energy consumption; monetary cost; migration time; reliability, defined as the ratio of successful executions to total executions; availability; and service level agreement violation, computed from host active time and performance degradation. By assembling these definitions in one place, the authors provide researchers with a common vocabulary, addressing a long-standing problem in which studies claiming improvements often measure incomparable quantities across incomparable testbeds.

That testbed problem receives its own treatment. The survey reviews the simulation ecosystems on which the field depends, from the venerable CloudSim and its extensions such as CloudSim Plus and CloudAnalyst, to iFogSim and FogNetSim++ for fog scenarios, PureEdgeSim for the full cloud-fog-edge spectrum, and GreenCloud, iCanCloud, DCSim, and SCORE for energy-aware data center modeling. The authors classify these tools by environment, features, implementation language, open-source status, and graphical interface, noting that reproducibility and cross-paradigm evaluation remain weak points. Many studies validate their algorithms only within a single simulator tuned to a single layer, making it difficult to know whether a scheduler that excels in a cloud testbed will survive contact with the unpredictable wireless links and device heterogeneity of a real edge deployment.

From this synthesis, the survey distills a set of critical findings and uncomfortable gaps. Performance trade-offs emerge everywhere: meta-heuristics can approach optimal solutions but may converge slowly or demand heavy computation; reinforcement learning agents adapt dynamically but require extensive training and can behave unpredictably under distribution shift. More fundamentally, the authors identify the lack of cross-layer scheduling metrics as a key deficiency, since no standard measure exists to evaluate a decision that spans cloud, fog, and edge simultaneously. They also flag the insufficient handling of dynamic network conditions, pointing out that many algorithms assume stable connectivity even as real-world edge systems contend with fading links, mobility, and congestion. The absence of unified benchmarks spanning the full continuum, and the scarcity of studies addressing security and privacy constraints in scheduling decisions, round out a research agenda that the authors lay out for the community.

The future directions the survey champions read like a blueprint for the next decade of distributed computing. AI-driven adaptive scheduling, in which schedulers continuously learn and re-learn as workloads and network conditions evolve, stands at the forefront, alongside energy-aware optimization that treats power consumption as a first-class objective rather than an afterthought. The authors envision deeper integration of meta-learning so that scheduling agents can generalize across environments, wider adoption of federated approaches that preserve data sovereignty at the edge, and hybrid architectures that combine the global search power of meta-heuristics with the real-time responsiveness of learned policies. As billions of devices join the internet of things and applications from autonomous transport to industrial automation demand guaranteed latency, the survey argues, the systems that manage computation must themselves become intelligent. This work, published as volume 29, article 738 of Cluster Computing, positions itself as a foundational resource for the researchers and practitioners who will build that intelligent infrastructure, offering them a single map of a field that has, until now, been scattered across three separate territories.

Subject of Research: Task scheduling and load balancing techniques across cloud, fog, and edge computing systems

Article Title: Task scheduling and load balancing in cloud, fog, and edge computing systems: a survey

Article References: Zorgati, R., Hamdi, H., & Omri, M. N. (2026). Task scheduling and load balancing in cloud, fog, and edge computing systems: a survey. Cluster Computing, 29(13), Article 738. https://doi.org/10.1007/s10586-026-06513-y

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06513-y

Keywords: task scheduling, load balancing, cloud computing, fog computing, edge computing, reinforcement learning, deep reinforcement learning, metaheuristics, quality of service, energy optimization, internet of things, artificial intelligence