The research, led by Fatemeh Salehipour Bavarsad of the Czech Technical University in Prague together with colleagues at Shahrood University of Technology and Osaka Metropolitan University, tackles this blind spot head-on. The team developed high-resolution, site-specific urban weather datasets that explicitly account for the urban heat island effect, the well-documented phenomenon in which cities run several degrees warmer than their rural surroundings because asphalt, concrete and building mass absorb and re-emit heat while vegetation and moisture are scarce. Rather than treating the city as a passive backdrop, the researchers coupled statistically downscaled future climate projections with a physics-based urban canopy model known as the Urban Weather Generator, or UWG, to produce multiple urbanized future weather files tailored to a specific urban site.

The Urban Weather Generator works by taking an upstream rural weather signal and transforming it through a physically grounded representation of the urban fabric. It accounts for how much of the site is covered by buildings, how tall those buildings are, how closely they are packed, the thermal properties of their walls and roofs, and the heat released by human activity and traffic. As solar radiation heats horizontal surfaces during the day and those surfaces release stored energy at night, the model computes the resulting air temperature differences between the rural reference and the urban site, hour by hour. This means the output is not a generic city correction but a weather file that reflects the actual three-dimensional geometry and material composition of the neighborhood surrounding a given building.

To disentangle the many forces at play, the team designed a structured three-scenario framework. The first scenario isolates regional climate change, asking how a future atmosphere alone alters building behavior. The second adds the urban heat island effect on top of that changed climate, capturing the extra thermal burden that urbanization imposes. The third goes further and incorporates the surrounding urban morphology, meaning the actual arrangement of neighboring buildings and their radiative and shading interactions with the building under study. By comparing results across these scenarios, the researchers could attribute changes in building performance to each driver separately, and, crucially, observe how the drivers interact with one another in ways that simple additive assumptions miss.

The test case is a representative mid-rise office building in Osaka, Japan, a metropolis that combines a hot, humid summer climate with intense urban density, making it an ideal laboratory for studying the collision between global warming and urban heat. The team simulated the building’s energy use and indoor thermal conditions under conventional weather files and under their new urbanized future weather files, tracking metrics that include cooling energy demand, indoor overheating degree, indoor overcooling degree, indoor discomfort degree and thermal autonomy, a measure of how much of the year a building maintains comfort without active systems. The contrast between the datasets was stark and, in places, counterintuitive.

The headline finding is that conventional weather datasets systematically underestimate indoor thermal discomfort in dense urban settings. Because the reference data comes from cooler rural or airport locations, simulations driven by such files paint an optimistic picture of future comfort, understating both the frequency and the intensity of overheating episodes. For a building designer, that optimism is dangerous. It can lead to undersized cooling systems, insufficient attention to shading and facade design, and a false sense of thermal resilience that evaporates during the first serious heat wave. As climate change pushes regional temperatures upward, the penalty for ignoring the urban heat island grows larger, because the extra urban warming compounds the baseline shift rather than merely adding to it.

Yet the study also delivers a more nuanced and arguably more surprising message: urban form is not simply an enemy of thermal comfort. When the researchers included the surrounding three-dimensional urban morphology in their weather generation, they found compensating effects. Neighboring buildings shade one another from direct solar radiation, and surfaces exchange longwave radiation in ways that can moderate conditions at the facade. A building in a tight urban canyon may suffer from the heat island’s warmer air, but it also receives less sun on its walls and windows than an isolated building in an open field. These interactions are nonlinear, meaning their combined effect cannot be predicted by summing individual contributions, and they can materially alter both energy use and indoor thermal conditions depending on the specific geometry of the site.

This nonlinearity is precisely why the researchers argue that urban microclimate must be treated as an integral part of building performance simulation rather than an optional refinement. The interplay between regional warming, urban heat island intensity and urban geometry produces outcomes that no single-factor analysis can anticipate. A design decision that looks prudent under a rural weather file, such as a generous window-to-wall ratio to capture daylight, may perform very differently when the same glazing faces a heat-soaked urban canyon. Conversely, dense urban form that raises local air temperatures may simultaneously deliver shading benefits that reduce cooling loads. Capturing these trade-offs requires weather data that carries the fingerprint of the actual urban environment, which is exactly what the new framework produces.

The practical implications extend well beyond Osaka. Building performance simulation underpins energy codes, green building certification, HVAC system sizing and climate adaptation planning worldwide, and all of these applications currently inherit the rural bias of standard weather files. The framework proposed by the Bavarsad team offers a workflow that practitioners can follow: take future climate projections, downscale them statistically, pass them through an urban canopy model parameterized with local morphology, and feed the resulting urbanized weather files into standard building simulation tools. The approach is deliberately compatible with existing simulation practice, using formats and tools already familiar to the industry, which lowers the barrier to adoption. It also supports climate-responsive urban design, because by varying morphological parameters, planners can test how different configurations of street width, building height and density shape both neighborhood climate and building performance before anything is built.

The broader stakes are considerable. Heat is already the deadliest weather-related hazard in many regions, and the buildings people occupy are the first line of defense between urban populations and dangerous temperatures. As the Intergovernmental Panel on Climate Change has emphasized, even modest levels of global warming translate into substantial increases in extreme heat exposure, and cities concentrate that exposure. Studies of temperature-related mortality under Paris Agreement scenarios have shown that the difference between warming targets is measured in lives, and indoor conditions during heat waves are a critical part of that equation. A simulation methodology that understates urban heat therefore does not just mispredict energy bills; it risks misinforming the very decisions that determine whether buildings shelter people or endanger them during the extreme events of the coming decades.

What makes this study resonate beyond its technical contribution is the clarity of its central lesson: the city itself is a climate machine, and pretending otherwise corrupts every downstream calculation. The researchers demonstrate that accurate representation of urban microclimate, particularly the combined effects of the urban heat island and urban geometry, is essential for reliable assessment of building performance and thermal resilience under future climate conditions. Their three-scenario framework gives the field a reusable template for separating and recombining the effects of global climate change, urban warming and urban form, and their Osaka case study shows concretely how much conventional methods miss. As cities race to retrofit existing stock and design new districts capable of withstanding a hotter century, tools like the Urban Weather Generator, coupled with downscaled climate futures, offer a way to stop designing for a countryside that the building will never experience, and start designing for the city as it truly is, and as it is becoming.

Subject of Research: Integrating urban heat island effects and urban morphology into future weather files for building performance simulation

Article Title: Integrating urban heat island effects into future weather data for building performance simulation

Article References: Bavarsad, F. S., Mohajerani, M., Tywoniak, J., & Yuan, J. (2026). Integrating urban heat island effects into future weather data for building performance simulation. Theoretical and Applied Climatology, 157(10), Article 630. https://doi.org/10.1007/s00704-026-06571-7

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06571-7

Keywords: urban heat island, building performance simulation, Typical Meteorological Year, Urban Weather Generator, climate downscaling, urban morphology, overheating risk, thermal comfort, Osaka, future climate scenarios, energy demand, urban microclimate

The research, led by Gabriel M. Magalhaes of the University of Minho together with colleagues from the Singapore-ETH Centre, CNRS@CREATE, the Singapore-MIT Alliance for Research and Technology and the Arts et Metiers Institute of Technology, forms part of the broader Cooling Singapore project and the DesCartes programme, both supported by Singapore’s National Research Foundation. The team’s central question was deceptively simple: when two state-of-the-art computational fluid dynamics models, built on fundamentally different simulation philosophies, are fed the same weather data and asked to reproduce the same three hot, sunny days, how closely do their outputs match what thermometers and radiation sensors actually recorded on the ground?

The two models represent contrasting approaches to a notoriously difficult problem. urbanMicroclimateFOAM, developed at ETH Zurich’s Chair of Building Physics as an extension of the open-source OpenFOAM toolkit, uses Reynolds-averaged Navier-Stokes equations, known as RANS. This technique solves for time-averaged flow fields, trading the fine texture of turbulent fluctuations for computational speed. PALM-4U, the urban extension of the Parallelized Large-Eddy Simulation model developed in Germany, takes the opposite route: it resolves the largest, energy-carrying eddies explicitly and models only the smallest scales, capturing the churning, transient character of urban airflow at a much higher computational cost. Comparing a RANS model and a large-eddy simulation against the same field data is therefore a test not just of accuracy but of whether the extra realism of LES justifies its expense in practical planning workflows.

Neither model can operate in isolation. Both require realistic initial and boundary conditions that describe what the atmosphere is doing at the edges of the simulated domain, and the researchers supplied these by nesting the microscale simulations within the Weather Research and Forecasting model, or WRF, configured with its multi-layer urban canopy parameterization. WRF, running at the mesoscale, captures the regional weather over Singapore and passes down profiles of wind, temperature and humidity that drive the street-level simulations. This mesoscale-to-microscale coupling is one of the most active frontiers in urban climate science, because errors introduced at the larger scale propagate directly into the neighborhood-scale predictions that planners ultimately use.

To ground-truth the simulations, the team identified three consecutive hot, sunny days from 2 to 4 June 2020 within an extended field observation campaign, a period chosen because clear skies and weak synoptic forcing make the urban radiation and heat balance as clean as possible to model. Three weather stations deployed within the study area recorded air temperature, relative humidity, wind and radiation throughout the days, providing the observational benchmark against which both models were scored using standard statistics including root-mean-square error.

The results were strikingly encouraging for both simulators. Outputs from PALM-4U and urbanMicroclimateFOAM agreed very well with the measurements at all three stations, and both reproduced the characteristic diurnal rhythm of the key climatic variables: the morning warm-up, the midday peak of radiant load, and the slow nocturnal release of stored heat from concrete and asphalt. For air temperature, the root-mean-square error stayed below 0.79 degrees Celsius. Relative humidity errors remained under 7.61 percent, a notable achievement in a climate where moisture fluxes from vegetation, soil and evaporating surfaces strongly shape thermal comfort. Most impressively, mean radiant temperature, the variable that often dominates human heat stress outdoors and that models routinely struggle with, was simulated with errors below 9.05 degrees Celsius.

That success with radiant temperature matters because it is the hardest variable to get right. Mean radiant temperature integrates shortwave solar radiation reflected and re-emitted from facades, pavements and the sky with longwave radiation from every surrounding surface, and it can swing by tens of degrees between a shaded courtyard and a sun-blasted plaza just meters away. Capturing that spatial heterogeneity requires the models to handle shading, surface albedo, emissivity and view factors with precision. The sub-degree accuracy in air temperature and the sub-ten-degree accuracy in radiant temperature across both a RANS and an LES framework suggest that, with careful boundary forcing, either tool can be trusted for evaluating shading strategies, vegetation placement and surface materials in tropical contexts.

Wind proved to be the weak point. Both models performed worst when estimating wind conditions, a familiar challenge in urban microclimate work. In a compact high-rise environment, airflow is governed by complex interactions between building-induced wakes, channeling effects between towers, thermal circulation and the turbulent boundary layer aloft, and small errors in the inflow profiles handed down from WRF can translate into substantial discrepancies at street level. The authors note that the performance of both models was influenced by the forcing boundary conditions, underscoring that the quality of the mesoscale driver remains a limiting factor even for the most sophisticated microscale physics. For applications such as pedestrian wind comfort or pollutant dispersion, this finding signals that coupling schemes and inflow generation deserve continued attention.

The broader significance of the study lies in its geography. Most validation studies of urban climate models have been conducted in mid-latitude cities with distinct seasons, moderate humidity and comparatively low-rise morphologies. Tropical compact high-rise districts, with their intense solar radiation, year-round heat load, high latent fluxes and canyon-like street geometry, stress the models in different ways, and a tool validated in a European or North American context cannot simply be assumed to transfer. By demonstrating that both PALM-4U and urbanMicroclimateFOAM can reproduce observed conditions in exactly such an environment, the study grants what the authors describe as the suitability of these specific models in tropical urban areas, and it gives planners in Singapore and comparable cities a validated basis for testing heat mitigation strategies digitally before pouring concrete.

For the growing community of researchers and practitioners working on urban heat, the message is pragmatic rather than partisan. The choice between a faster RANS solver and a more physically complete but costlier large-eddy simulation need not be ideological; in this tropical test, both delivered trustworthy temperature, humidity and radiation fields when driven by the same mesoscale forcing, and both stumbled on wind in similar ways. As cities across the tropics grapple with rising temperatures and densifying skylines, the ability to simulate street-level climate with this level of fidelity, and to know precisely where that fidelity ends, is a quiet but consequential step toward designing cities that people can live in comfortably under a warming sky.

Subject of Research: Validation and comparison of two urban microclimate CFD models in a tropical high-rise city

Article Title: Evaluation and comparison of PALM-4U/WRF and urbanMicroclimateFOAM in tropical high-rise urban areas

Article References: Magalhaes, G. M., Adelia, A. S., Acero, J. A., Singh, V. K., Huljak, B., Nobrega, J. M., & Chinesta, F. (2026). Evaluation and comparison of PALM-4U/WRF and urbanMicroclimateFOAM in tropical high-rise urban areas. Theoretical and Applied Climatology, 157(10), Article 666. https://doi.org/10.1007/s00704-026-06573-5

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06573-5

Keywords: urban microclimate, PALM-4U, urbanMicroclimateFOAM, WRF, computational fluid dynamics, large-eddy simulation, RANS, Singapore, tropical climate, mean radiant temperature, urban heat island, model validation

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