This session welcomes modelling and observational studies that aim to investigate different aspects of urbanization (e.g. urban heat island, air quality, vulnerability to extreme events, urban/peri-urban agriculture) and its feedback on weather and climate systems, with a particular focus on application for sustainable adaptation plans. Novel methods that aim to assess urban representation and/or to bridge the different scales of the diversity of topologies are encouraged. The impact of cities on weather, air quality, climate and/or their extremes (e.g. drought, precipitation, air pollution episodes), as well as on climate change and on population and adaptation will also be discussed in this session.
Topics may include:
• New urban parameterizations, methods to derive urban parameters for numerical models.
• Implementation of climate mitigations, adaptation strategies (e.g. blue-green infrastructures) and self-government policies in cities and urban context.
• Impact of the different urban parameterizations on the atmospheric dynamics at different scales.
• Impact of the urbanization including estate and industrial on weather and/or climate extremes.
• Field measurements of urban climate, e.g. precipitation, CO2 concentrations and flux, boundary layer characteristics.
• Population vulnerability to urban climate and climate change.
• Extreme events' (e.g. drought, rainfall events, heat wave) impacts on urban areas.
• Urban emissions of climate forcers, air pollutants and anthropogenic heat.
• Urban air quality and meteorological interactions.
• Meteorology or air pollution modelling of all scales with focus on urban areas.
• Coupling and downscaling of global, regional and urban scale modelling approaches to quantify climate and atmospheric composition impacts and feedbacks.
• Integrated monitoring, modelling and forecast systems for urban hazards.
• Urban transition to cleaner fuels and their meteorological or AQ impacts.
• Crowd sourced data/novel data sources in cities
• Successes, challenges and limits of AI approaches for urban research
• Assimilation of 4D data and machine learning applied for air quality simulation
• Social science analyses of cities
Organised jointly with:
World Meteorological Organization (WMO) Global Atmospheric Watch Project GAW Urban Research in Meteorology and Environment (GURME)
WMO World Weather Research Programme (WWRP)
Extreme heat is increasingly recognized as one of the most severe climate-related risks affecting urban populations, with disproportionate impacts on public health, energy systems, and vulnerable communities. As heatwaves intensify under climate change, cities require near-real time, high-resolution and actionable information to support early warning systems, preparedness, and long-term adaptation. Addressing this challenge at urban-local scale demands not only methodological innovation, but also robust digital infrastructures capable of delivering consistent and interoperable climate intelligence across regions.
Destination Earth (DestinE), a strategic initiative of the European Union, represents a transformative step in this direction by providing global, high-resolution climate and weather simulations, through Digital Twins of the Earth System. By coupling advanced numerical models, Earth Observation (EO) data, and high-performance computing, DestinE establishes a common backbone for next-generation climate services. However, translating these powerful datasets into locally relevant, operational products for cities remains a critical challenge.
DE_395-Urban Heat Health Forecasting (UHHF) project addresses this gap by demonstrating how DestinE Extremes Digital-Twin outputs can be transformed into urban-scale user-oriented heat-health indicators through the operational use of Machine Learning (ML). The project applies ML-based downscaling techniques to near-surface air temperature (T2m) and relative humidity (RH) forecasts, enhancing spatial resolution from kilometre-scale to approximately 200 m. These downscaled fields are subsequently used to derive human-biometeorological indicators such as the Universal Thermal Climate Index (UTCI) and Thermal Stress Duration (TSD), supporting health-oriented risk assessment.
The UHHF framework integrates DestinE atmospheric drivers with EO-derived and geospatial predictors describing urban form, land cover, vegetation, and topography, including Local Climate Zones. Quality-controlled crowdsourced observations from citizen weather stations are combined with WMO reference data to constrain and validate the ML models, ensuring robustness under both average and extreme conditions. The approach is being implemented across four climatically and socio-environmentally diverse Functional Urban Areas, e.g. Naples, Chicago, Santiago, and Cape Town, enabling a systematic evaluation of models across continents.
By building directly on DestinE and complementary European programmes led by ECMWF, ESA, and Copernicus, drawing on both their data assets and operational services, UHHF aims to illustrate how these can be leveraged to develop affordable, scalable, and reproducible urban-scale climate information and services. The project highlights the strategic importance of climate data platforms in bridging the gap between global simulations and local decision-making, contributing to the development of interoperable urban climate and health services aligned with European and international resilience frameworks.
How to cite: Girão, I., Paixão, J., Miranda, V., Silva, F., Siemen, S., Di Napoli, C., and Oliveira, A.: Urban Heat Health Forecasting with Destination Earth: Leveraging Digital Twins and Machine Learning for Scalable Urban Climate Services, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-674, https://doi.org/10.5194/ems2026-674, 2026.
The canyon layer urban heat island effect (UHI) is the best known example of how urban surfaces affect the microclimate. Lots of studies have focused on the how the UHI magnitude is determined by the urban morphology such as e.g. green vegetation fraction and sky-view factor. Although these aspects play a crucial role, the UHI needs first of all an atmospheric state that promotes UHI formation, e.g. high solar insolation, clear skies, low synoptic wind speed, and stably stratified nights. Given the ongoing climate change the frequency of occurrence of these favorable conditions may change. This study explores trends in this so called “atmospheric potential” for the UHI for summer periods from 1950 till 2025 using ERA5 re-analysis data. The atmospheric potential is expressed in a ratio that contains solar radiation, diurnal temperature range (DTR), and wind speed as observed in the countryside. For Europe we find clearly spatially different patterns of changing UHI potential. We find that the atmospheric potential for the UHI has increased by 0.6 K, apart from the U.K. the Alps, Norway and Sicily (decrease of 0.1-0.4 K). Further analysis reveals that of the three governing terms, the trends in DTR and surface solar radiation are the dominant drivers for the increase in UHI potential. Apart from a band from the U.K. to Bulgaria, the wind speed term reduces the UHI potential across Europe, although only by ~0.1K. We conclude that apart from urbanization effects, also the atmospheric potential for UHI is subject to a substantial long-term trend.
How to cite: Steeneveld, G.-J., Peerlings, E., and Jongen, H.: Trends and regional European patterns in “atmospheric potential” for the urban heat island effect in the ERA-5 reanalysis., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-31, https://doi.org/10.5194/ems2026-31, 2026.
Cities play fundamental role in climate at local to regional scales through modification of heat and moisture fluxes, as well as affecting local atmospheric chemistry and composition, alongside air-pollution dispersion. Vice versa, regional climate change impacts urban areas and will affect cities and citizens increasingly in the next decades when the population in urban areas is growing projected to reach 70 % by 2050. This is critical in connection to extreme events, e.g. heat waves with extremely high temperatures exacerbated by the urban heat island effect, in particular during night-time, with significant consequences for human health.
Recent RCM development achieved resolution of city scales within convection permitting RCMs, parameterization of urban processes thus becomes more important. From the framework of CORDEX FPS, main aims and progress of FPS URB-RCC will be presented, especially the results of analysis of Stage-0 experiments using case studies of heat wave and convection episode within ensemble of about 40 simulations for Paris with CP RCMs. This experiment shows the effects of different implementation of urban parameterizations as well as of the different models and their settings on urban heat island under the heat wave, for convection case there is hard to analyse significant signal. Further outlook of long term (10 years – Stage 1 experiment) climate simulation with these models in common strategy to IMPETUS4CHANGE Horizon Europe Project will be presented. The development of Global Satellite Cities experiment with similar experiments for other big cities around the world will be introduced as another part of Stage 1 experiment.
How to cite: Halenka, T., Langendijk, G., Hoffmann, P., Belda, M., and Crespo, N.: CORDEX Flagship Pilot Study URB-RCC: Urban Environments and Regional Climate Change, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-729, https://doi.org/10.5194/ems2026-729, 2026.
Changes in the Mediterranean climate, particularly shifts in the wet season’s peak and increasing dry season intensity, have been extensively studied due to their direct implications for the local environment, agriculture, and populations. Comparatively less attention has been given towards changes in the transitions between these seasons, specifically the onset and conclusion of the dry and wet seasons. In this study, we analyzed over 80 years of historical daily rainfall records from twelve meteorological stations across Israel, spanning a north-south climatic gradient. We found a consistent, statistically-significant shift toward an earlier end to the dry season driven by an increase in frequency of synoptic systems that are locally associated with rain, effectively shortening the dry season in nearly all locations. One region was excluded: the growing urban center, where no trend change was observed. To investigate this anomaly, we used high-resolution three-dimensional mapping of the urban area to quantify the change in building surface area and associated energy balance. The rapid urban expansion heavily influences the Bowen ratio, especially in comparison to adjacent rural regions, driving an urban dry-island effect towards the end of the dry season, particularly at nighttime. We show how the relationship between increased urban surface area, sensible heat load, and boundary layer dynamics can be quantitatively linked to changes in the lifting condensation level, which provides a mechanistic explanation for rainfall suppression over the urban region. Our results highlight the dual influence of large-scale climatic trends and localized urban influences on rainfall during the Mediterranean transition from dry to wet season, offering a glimpse into both long-term changes in regional rainfall dynamics, and a pathway through which urbanization may be reshaping these patterns, during seasonal transition periods.
How to cite: Rez, L., Alpert, P., and Yakir, D.: Urbanization counteracts large-scale climatic trend in earlier rainfall in the East-Mediterranean, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-132, https://doi.org/10.5194/ems2026-132, 2026.
Extremely high temperatures pose a growing threat to human health, especially in urban areas, where limited nighttime cooling restricts recovery from daytime heat stress. By contrast, suburban and rural areas often experience lower nighttime temperatures, which can partly alleviate heat stress. This study examines current heat exposure in urban and rural areas of five Swiss cities – Basel, Bern, Geneva, Lausanne, and Zurich – and how this might change in a warmer future climate. Using the heat warning level concept developed by MeteoSwiss, we evaluate the changes in the frequency and intensity of heatwaves at urban-rural station pairs under global warming of 1.5°C, 2.0°C, and 3.0°C, as projected by the Swiss Climate CH2025 Scenarios. Our results indicate that continued global warming will lead to higher daytime and nighttime temperatures in summer, resulting in substantially greater exposure to heat in both urban and rural areas. Under present-day conditions, heatwaves are relatively rare in these cities. On average, they occur less than once a year in rural areas and once a year in urban areas, lasting approximately three days. Under warmer conditions, they will become more frequent, longer, and more intense, particularly in urban environments, where higher warning levels will be reached more often. At GWL3.0, rural areas are projected to experience an average of three heatwaves per year, whereas urban areas may experience an average of four. Heatwaves will also last longer in urban areas, with an average duration of 14 days compared to 10 days in rural areas. In addition, the season conducive to heatwaves is projected to lengthen. These findings emphasise the growing significance of urban heat exposure as a climate-related health issue in Switzerland. They also underline the importance of robust heat warning systems in supporting timely communication, preparedness, and adaptation planning in urban environments that are becoming increasingly prone to heat.
How to cite: Senoner, A. E., Burger, M., and Kotlarski, S.: Heat without relief? Future projections of heat exposure in Swiss cities in a warmer climate, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-378, https://doi.org/10.5194/ems2026-378, 2026.
Urban outdoor air temperatures have increased as a consequence of climate change, resulting in adverse health effects to urban dwellers and increased energy cooling demand. However little is known whether, and to what extent, summertime indoor temperatures have increased due to climate change. This study presents modelled summertime indoor temperatures for 80 houses in Amsterdam for the period 1951-2025, and for future projections. A physics-inspired statistical model was driven with ERA5 and local automatic weather station data (KNMI-AWS) to model indoor temperatures. The model accounts for solar radiation, outdoor air temperature, thermal radiation and wind speed as drivers. We find that the summer-mean living room temperature has significantly increased by on average 0.36 °C per 10 summers (1951-2025). Moreover, we find an acceleration of indoor temperature rise after 1988 (0.54 °C per 10 summers). In addition, we find that daily mean living room temperatures start to exceed 26 °C for more than six days per summer in the 21th century. Also we find analogue indoor temperature trends if the model is ran for northerly (Groningen, Den Helder) and more southerly (Eindhoven, Maastricht) cities. Finally we use KNMI 2023 climate scenarios, to explore the evolution of indoor summertime temperatures in the future. This appears to be 5.1 °C increase on average for living room temperature for the Hd (high emission, dry climate) scenario for 2100. Across the entire study, we find equivalent results between bedroom and living room temperatures. This study concludes that climate change has reached indoors over the past 75 years.
How to cite: Nieuwendijk, Q., Peerlings, E., and Steeneveld, G.-J.: Is climate change found in indoors temperatures?, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-50, https://doi.org/10.5194/ems2026-50, 2026.
The structure and behavior of the urban boundary layer are critical for a wide range of applications ranging from urban air quality to urban heat. There still however exists large uncertainties on how the urban boundary layer responds to surface forcing, how this influences ventilation at various spatial scales, and how these processes should optimally be simulated. Recent advancements in computational fluid dynamics have made it possible to examine the urban boundary layer and its structure in detail. This highlights how large eddy simulation modelling can bring comprehensive insights into the urban boundary layer at both local and city scales, demonstrating also its practical application in urban planning or implementation of urban atmospheric measurements.
How to cite: Järvi, L.: Turbulence in cities - from microscale ventilation to urban boundary layer structures, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-822, https://doi.org/10.5194/ems2026-822, 2026.
The increasing use of hectometric-scale (≈100 m) simulations for urban applications raises important questions about their capability to accurately represent turbulent processes. In this study, we evaluate the performance of large-eddy simulations (LES) within the WRF model against the widely used Yonsei University (YSU) planetary boundary layer (PBL) scheme at 100 m horizontal and 50 m vertical resolution. Model outputs are compared with eddy-covariance (EC) measurements collected in Enschede, the Netherlands. Two contrasting atmospheric conditions are analyzed: a windy, shear-dominated case and a calm, high-temperature case driven by buoyancy. Both LES and YSU reproduce the temporal evolution of near-surface meteorological variables reasonably well. However, clear differences emerge in the representation of turbulence. Under convective conditions, LES captures realistic turbulent variability, as reflected in energy spectra and resolved fluxes, while YSU produces overly smooth flux fields. LES resolves key turbulent structures, including roll-like features in the windy case and cellular convection in the calm case, although near the surface it still relies partly on subgrid-scale parameterization. In contrast, the YSU scheme does not generate organized convective structures in the shear-driven case and only partially represents convective cells under calm conditions, resolving a limited fraction of the turbulent transport. This partial resolution conflicts with the underlying assumptions of one-dimensional PBL schemes and may lead to inconsistencies such as double counting of turbulence. During stable nighttime conditions, neither approach adequately resolves turbulence, and both depend primarily on parameterized processes. Furthermore, the resolved structures in LES enhance horizontal transport, contributing to the redistribution of heat between urban and surrounding rural areas. Overall, the results indicate that hectometric WRF-LES can realistically represent urban turbulence and its impact on boundary-layer processes during convective periods, supporting its application in urban climate studies, including heat stress and precipitation analysis (Gadde et al. 2026, https://doi.org/10.1016/j.buildenv.2026.114365).
How to cite: Gadde, S., Steeneveld, G.-J., and Timmermans, W.: Evaluating Urban Turbulence Representation at Hectometric Scale Using WRF-LES and Eddy-Covariance Observations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-481, https://doi.org/10.5194/ems2026-481, 2026.
Microscale urban climate simulations provide detailed insights into thermal conditions within cities, supporting research and planning for climate adaptation. However, high spatial resolution simulations are computationally demanding, limiting their applicability for large urban domains. City-wide assessments often require coarser grid resolutions, where explicit representation of three-dimensional urban structures becomes impractical due to computational constraints and impacts the quality of the modelling results especially in the case of large-eddy simulations. Another issue arises from the limited availability of high-quality geodata for building resolving simulations.
To address these challenges, urban parameterisation schemes can be employed in the PALM model system to capture urban effects while significantly reducing computational costs. This allows for the simulation of larger domains without explicitly resolving three-dimensional structures such as buildings and trees. The required input data for the parametrisation schemes can be generated based on Local Climate Zone (LCZ) maps. Furthermore, the LCZ maps can serve as a basis for idealised three-dimensional setups in cases where high-quality geodata are unavailable.
In this study, we systematically compare various urban parameterisation schemes implemented in PALM, as well as an idealised LCZ-based setup, against a baseline scenario with explicitly resolving the three-dimensional urban structure. The simulations are conducted for a summer situation with initial and boundary conditions derived from the ICON-D2 model. Simulations are evaluated using observational data from Dortmund, Germany. Our analysis identifies the suitability and limitations of each approach across different spatial resolutions, providing guidance on when to parameterise and when to resolve urban structures in PALM simulations. Initial results will be presented.
How to cite: van der Linden, L., Anders, J., Bechtel, B., and Maronga, B.: Shedding light on the urban LES grey zone: when to parameterise and when to resolve buildings in PALM simulations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-210, https://doi.org/10.5194/ems2026-210, 2026.
Accurate characterisation of local urban wind flows is relevant for applications like pedestrian comfort, microclimate modelling, and pollutant dispersion. High-resolution computational fluid dynamics simulations provide detailed flow fields, but are highly uncertain due to model assumptions, boundary conditions, and turbulence parameterisations. By incorporating observations into these models, we can quantify this uncertainty, particularly in complex urban geometries, and make corrections to the model itself and its output.
This work is part of the UrbanAIR project, which aims to develop model representations of urban environments (Digital Twins), high-resolution physics-based computational models that mirror the urban atmosphere. By combining complex atmospheric models with city geometry, simulations of mesoscale climate, field measurements, and user input, the project aims to support decision-makers in designing climate-resilient cities.
In this context, we explore ensemble-based data assimilation strategies to improve the estimation of key drivers of urban flow, focusing on reduced-order formulations such as boundary condition and parameter estimation. These ensemble-based methods are considered, given the strong nonlinearities and high dimensionality of the system, while maintaining computational tractability. Synthetic experiments are used to investigate how effectively limited observations can constrain model states and parameters, and to assess the role of observation type, availability, and location.
In steady RANS urban simulations, preliminary results indicate that ensemble smoothing can recover the inflow wind angle distribution in synthetic twin experiments, but the performance of measurement locations is strongly dependent on the type of observable. In particular, velocity magnitude appears to carry directional information primarily in wake regions, leading to non-unique inversions elsewhere, whereas local velocity components provide more complementary information.
This work contributes to the connection between high-resolution urban modelling and observational data, supporting the development of integrated Digital Twin frameworks for urban climate and air quality applications.
How to cite: Plazas, D., Patil, A., Garcia-Sanchez, C., and Vossepoel, F.: Ensemble Data Assimilation for High-Resolution Urban Simulations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-605, https://doi.org/10.5194/ems2026-605, 2026.
Outdoor heat exposure of citizens in urban areas becomes more critical to understand due to the compounding of urbanization and climate change. Outdoor heat load depends on the urban morphology, e.g. the reflectivity of the walls. However, spatial datasets of wall albedo values in cities are so far lacking, but street view images may act as potential sources to estimate albedo and develop such gridded datasets.
In the “Urban-M4” eScience project we explored how street view imagery can be used to derive radiative properties for use in urban weather models. This data is widely available, not only from proprietary sources (Google’s Streetview) but also from crowd-sourcing platforms such as Mapillary and Kartaview. With modern computer vision models, these images can be “segmented”; partitioning an image into district groups based on properties such as material, objects or other concepts. These segmentation techniques provide new opportunities to extract urban parameters from (street view) imagery, but the quality of these segmentations has yet to be established. We present a new tool to quickly evaluate segmentations of street view imagery.
With the ‘streetscapes’ Python package project users can download street view images and segment them, as well as export aggregated data for further analysis. However, it was difficult for end users to review the segmentation results and images within the Python interface. Therefore, we designed and built the “streetscapes explorer”, a browser based visual interface for analyzing the street view images as well as the segmentation results. Users can view the images on a map and filter these for information such as image source, tags, or segmentation labels. This allows users to quickly and easily assess the results produced by computer vision models. If after curation, the user is satisfied with the results, these can be exported to a format compatible with GIS programs for further analysis or processing such as rasterization.
How to cite: Schilperoort, B., Kalverla, P., Verhoeven, S., Hadjiivanov, A., Sandvik, B., Hafkamp, V., and Steeneveld, G.-J.: Interactive evaluation of street view image segmentations for urban parameter extraction - an illustration for Amsterdam, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-551, https://doi.org/10.5194/ems2026-551, 2026.
With climate change heat waves are becoming more frequent and therefore heat stress in especially urban areas increases. To locate heat spots in cities high-resolution urban atmospheric modelling is key, but the variability of one important factor influencing the temperature is often ignored. The albedo of walls is by default assumed to be constant in most urban schemes used in simulation models such as the Weather Research and Forecasting (WRF) model, while the amount of reflection from different surfaces can have a significant effect on the local temperature. Estimating or measuring the albedo of every street facet in an entire city can be very time-consuming. Although there have been studies where albedo was derived from airborne measurements, these have failed to capture the albedo of walls due to a limited view from above. In this study the use of crowdsourced street view images (SVI) from Mapillary is explored to determine if these images could be used to obtain estimates of wall albedo.
This study builds upon previous work of the Urban-M4 project, which is a project focused on developing a python package for segmenting and analysing street view images for urban modelling applications. This code was used in this study to segment the street view images using a combination of the GroundingDINO model for object detection and the Segment Anything Model (SAM) for instance segmentation to select the building façades. Afterwards, the relative brightness of the selected pixels was calculated in three different ways. The first method used the Lightness component from the HSL model, the second the Value from the HSV model and the third used the luminance of the building in comparison to the brightest pixel in the image. To determine the accuracy of each calculation-method, field measurements were done in Amsterdam and Wageningen and first results show that the albedo calculated with the HSV model is closest to the albedo from field measurements.
More information on the Urban-M4 project can be found here: https://research-software-directory.org/projects/urban-m4
How to cite: Hafkamp, V., Sandvik, B., Kalverla, P., Timmermans, W., and Steeneveld, G.-J.: Measuring wall albedo: in situ and using street view images, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-488, https://doi.org/10.5194/ems2026-488, 2026.
Historic variations in building regulations and construction practices have profoundly shaped the thermal properties of urban areas, yet these are frequently overlooked or oversimplified in current urban weather and climate models. Buildings from different periods exhibit distinct material and insulation characteristics, leading to varying vulnerabilities to climatic extremes. Most urban mesoscale models, however, rely on generic classifications such as Local Climate Zones (LCZs), neglecting the spatial diversity of building thermal properties.
To address this gap, we developed a novel building representation approach for urban climate modeling using Amsterdam as a case study. By leveraging cadastral data and an extensive review of historical building regulations and practices in the Netherlands, we defined ten “HERITAGE building classes” that capture the thermal characteristics of different construction periods. GIS techniques were used to create heritage maps which were together with the derived thermal property values for each class integrated into the Weather Research and Forecasting (WRF) model. We analyze and compare the model’s performance against a model setup using the conventional LCZ approach and observational meteorological data from the Amsterdam Atmospheric Monitoring Supersite (AAMS) for the three available urban parameterization schemes during a recent heat wave.
Initial results show sensitivity of the model to the HERITAGE-class parameters and show improvement in representation for diurnal profiles of 2 m temperature and vapour pressure deficit (VPD), especially for the most complex building parameterization scheme (BEP-BEM). This demonstrates the skill of the HERITAGE approach to represent a historic building environment in greater detail and offers a modelling alternative to LCZs urban climate applications based on thermal characteristics and building age.
This approach can provide the urban climate community, modellers, city planners, and policymakers with a new perspective on spatial vulnerability to thermal stress, supporting more effective climate adaptation, heritage preservation, and future-proof urban design.
How to cite: Sandvik, B. E., Milošević, D., and Steeneveld, G.-J.: Beyond NUDAPT and LCZs: Representing historical buildings in urban climate mesoscale models, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-530, https://doi.org/10.5194/ems2026-530, 2026.
The QUALARIA Project main objective is the development of a state-of-the-art Artificial Intelligence (AI) model, able to provide accurate predictions of air pollutant concentrations, air quality indexes and health risks in suburban scale (< 100 m). The inference capacity in high spatial resolution is enabled by the incorporation of a low-cost air pollutant sensor (Bernard Air Analysers, BAAs) observational network, spread in a miscellaneous urban configuration. In this way, the project pursuits overcome the limitation of current info by traditional methodologies, which provide regional scale estimates (for example, 40 km from the dynamic model Copernicus Atmosphere Monitoring Service/European Centre for Medium-Range Weather Forecasts (CAMS/ECMWF)), restricting the effective decision making in the urban context. The final product is an online dashboard with air pollution predictions and high-performance health indicators, allowing the implementation of risk quantification and actions for mitigation in cities. The features and predicted indicators of the dashboard are designed together with national and international stakeholders from the public and private sectors. The target audience are decision makers, public policy makers, urban planners, public health, climate and environment institutions, and air quality non-governmental organizations. A pilot is being implemented for the Metropolitan Area of São Paulo (MASP), with the intention of scaling to all Brazil and other countries later. The AI model is being trained by the air pollutant observational networks and BAAs deployed in 28 MASP spots, in addition to info of surface physical features derivative from satellite imagery, air pollutant prediction from CAMS/ECMWF, stationary and mobile sources, building height, populational density, simulations from the model Weather Research and Forecasting with Chemistry, among other inputs.
How to cite: Peli, V., Calderón, M., Orfanoz, A., Perez, G., Martin, T., Lucena, A., Barbosa, E., Laimer, F., Gstir, T., Andrade, M. D. F., Freitas, E., Li, C., and Brasseur, G.: QUALARIA Project: Air Quality Prediction Artificial Intelligence System in Street-Level Scale, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-245, https://doi.org/10.5194/ems2026-245, 2026.
The well-being of citizens in urban areas is significantly affected by the air quality and thermal comfort in street canyons. The air quality is strongly dependent on local transportation emissions and street-canyon ventilation. This ventilation is affected by street-canyon characteristics, e.g., the presence of trees, and by meteorological conditions and local forcings, including the wind flow and turbulence affected by moving cars, as well as the heat produced by the cars which promotes the convective flow. The human thermal comfort, which can be expressed using thermal comfort indices such as the Universal Thermal Comfort Index (UTCI), is also affected by the speed of the wind which aids in cooling via perspiration. The microscale meteorological model PALM can simulate both the air quality and the thermal comfort at street-canyon scale, and its recently added PALM Traffic Module provides a simple, parameterised way to incorporate car-related processes. Based on traffic flow characteristics, it calculates the induced drag forces and adds them to the model dynamics as additional airflow tendencies, without the need to resolve the exact shape and spatiotemporal position of each individual car. It also considers the heat produced by the vehicles and transforms it into additional tendencies of air potential temperature. In this study, we present the influence of car-related processes on street-level pollutant concentrations under different meteorological conditions. We also show that these processes have a significant impact on air quality under stable conditions, although their influence is negligible under windy and convective conditions. We will present preliminary results of the comparison of the modelled wind tendency with field observations in a real street canyon.
How to cite: Krč, P., Geletič, J., Řezníček, H., Vlček, O., Pikousová, T., Belda, M., Fuka, V., and Resler, J.: Modelling the influence of the car-induced momentum and heat on the microscale wind dynamics in the street canyon, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-741, https://doi.org/10.5194/ems2026-741, 2026.
Urban air pollution in densely built city poses a significant threat to public health, with pollutant concentrations frequently exceeding the thresholds indicated in WHO guidelines. Nature-Based Solutions (NBS), particularly street trees, are increasingly considered as mitigation measures, yet their effectiveness in real urban environments remains poorly understood. Most studies assess tree effects in idealised canyon geometries, while evidence of their effectiveness in reducing pollutant concentration from real, morphologically complex urban settings is scarce. This study addresses this gap by investigating the influence of real urban trees and green areas on pollutant dispersion in two historical European city centres: Bologna (Italy) and Nicosia (Cyprus). The comparison is deliberately designed to test whether urban geometry which differs substantially between the two cities plays a determining role in governing the effectiveness of tree-based NBS, an aspect that has not been systematically investigated to date.
For each city, the case study consists of a real urban neighbourhood of the historical city center. In Bologna, the study focuses on the street canyon of Via Irnerio, characterised by a dense, irregular building arrangement with a high aspect ratio. In Nicosia, a previously validated urban geometry is adopted from Antoniou et al. (2019), representing a broader Mediterranean urban layout with different canyon proportions and building configurations. Urban trees and green areas are implemented as porous media using a validated drag-force formulation, with crown characteristics derived from municipal tree data website. Traffic-related pollutant emissions are represented by a ground-level line source centred within the street canyon, consistent with established modelling practice.
Reynolds-Averaged Navier–Stokes (RANS) simulations with the standard k-ε turbulence model are conducted. The Bologna baseline is validated against water-channel experiments. The Nicosia baseline validation follows Antoniou et al. (2019), providing an independently verified starting point for both cities.
By comparing pollutant concentration distributions and canyon ventilation efficiency with and without trees across both sites, the study aims to assess whether morphological differences between cities lead to substantially different NBS outcomes. The findings are expected to provide novel, evidence-based guidance on the role of urban geometry in determining the effectiveness of street-level vegetation as an air quality mitigation strategy, with relevance for urban planners.
How to cite: Ullah, M. S., Alexandrou, G., Cintolesi, C., Mouzourides, P., Sengupta, B., Antoniou, N., Neophytou, M., and Di Sabatino, S.: Impact of Street-Level Trees on Pollutant Dispersion in Real Urban Canyons: A Comparative Study of Bologna and Nicosia, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-688, https://doi.org/10.5194/ems2026-688, 2026.
While overall the global warming with the causes and global processes connected to well-mixed CO2, and its impacts on global to continental scales are well understood with a high level of confidence, there are knowledge gaps concerning the impact of many other non-CO2 radiative forcers leading to low confidence in the conclusions. This relates mainly to specific anthropogenic and natural precursor emissions of short-lived GHGs and aerosols and their precursors. These gaps and uncertainties also exist in their subsequent effects on atmospheric chemistry and climate, through direct emissions dependent on changes in e.g., agriculture production and technologies based on scenarios for future development as well as feedbacks of global warming on emissions, e.g., permafrost thaw.
The main goal of EC HE project FOCI, is to assess impacts of key radiative forcers abd the processes of their impact on the climate system, to find and test an efficient implementation of these processes into global ESMs and into RCMs coupled with CTMs, and finally to use the tools developed to investigate mitigation and/or adaptation policies incorporated in selected scenarios of future development targeted at Europe and other regions of the world. We are developing new regionally tuned scenarios based on improved emissions to assess the effects of non-CO2 forcers.
Overall introduction to coupled RCM-CTM modelling experiment strategies and evaluation simulations will be presented in addition to the contemporary status of the project. Historical simulations results are validated against reanalyses data and the assessment of impact of chemistry involvement is shown. We will show the results for regional and local conditions in high resolution for City of Prague.
How to cite: Halenka, T., Sokhi, R., Finardi, S., Machado-Crespo, N., Huszár, P., and Karlický, J.: Project FOCI - Non-CO2 Forcers and Their Climate, Weather, Air Quality and Health Impacts: Chemistry – Climate Interactions over Scales, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-734, https://doi.org/10.5194/ems2026-734, 2026.
Atmospheric dispersion models used for regulatory purposes are commonly driven by single-point measurement timeseries. The fundamental input quantities are time of day, wind direction, speed and atmospheric stability. In this case, stability is usually expressed in terms of stability classes, as formulated by Pasquill and Gifford or Klug and Manier, rather than in terms of a continuous stability measure, such as Monin-Obukhov stability.
In the last decade, a number of high-resolution atmospheric reanalyses that provide hourly output fields, including ERA5 (31 km) and CERRA (5.5 km), have become available. It is therefore possible, at least in principle, to derive the input for pollution models from these reanalyses. Commercial environmental consulting institutes have presented attempts at this, albeit without disclosing the exact method.
And this choice is far from trivial since the number of possible procedures is large: Measurements from operational weather stations or semi-mobile weather stations ususally do not permit the direct calculation of atmospheric stability measures. Consequently, the stability is derived via various schemes that yield the aforementioned stability classes. Reanalyses, in contrast, yield both the quantities that would be measured by a weather station as well as stability measure, such as the Obukhov length. The latter can be used to either determine a stability class, or alternatively, it may be fed directly into the dispersion model.
The present study aims to compare the various methods used to derive stability classes within the two most relevant classifications, and to assess the levels of agreement, both with each other and with weather station data. The comparison was performed for locations in different regions, with varying terrain complexity. For two locations where long-term eddy-covariance measurements are performed, the Obukhov-length inferred via the stabilty classes is compared to the values measured in situ.
How to cite: Drüe, C.: Stability classes for pollution dispersion modeling from reanalysis data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-429, https://doi.org/10.5194/ems2026-429, 2026.
Rooftop photovoltaic (PV) systems are increasingly shaping urban environments, serving not only as sources of renewable energy but also altering the surface properties of cities. By modifying how rooftops store and release heat, they affect roof-level temperatures and, consequently, the surrounding indoor and outdoor microclimate. However, the overall impact of rooftop PV remains uncertain, with previous studies reporting conflicting findings depending on system design, building characteristics, weather conditions, and, importantly, the modeling approaches employed. Prior studies used simplified representations (e.g., effective-albedo methods), site-specific energy-balance models not coupled to the atmosphere, or mesoscale atmospheric models, which capture surface-atmosphere interactions but cannot resolve building-scale flows crucial for momentum and thermal exchange. In our previous work, we implemented a parametrization for building-applied PV into the large-eddy simulation (LES) model PALM and validated it against a five-month measurement campaign. The PV parametrization captures key physical processes, including radiative exchange, heat transfer, ventilation within the PV–roof gap, and material-specific properties. In this study, we apply PALM in building-resolving simulations to investigate impacts of area-wide rooftop PV on outdoor thermal comfort, indoor temperatures, and building energy demand. Simulations are conducted for summer and winter conditions across all built Local Climate Zones (LCZ1 - LCZ10), ensuring comparability and broader applicability. This study provides the first systematic microscale assessment of rooftop PV impacts using building-resolving LES. By disentangling the underlying heat transfer processes, it clarifies under which urban and climatic conditions rooftop PV may either exacerbate local heat stress or contribute to its mitigation. These findings offer actionable insights for climate-sensitive urban design and support more informed integration of PV systems into sustainable and resilient cities.
How to cite: Anders, J. and Maronga, B.: Rooftop photovoltaic impacts on outdoor and indoor urban microclimate: Summer and winter simulations by local climate zones using LES, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-192, https://doi.org/10.5194/ems2026-192, 2026.
Urban severe convective rainfall, a major urban hazard modulated by microscale cloud processes, mesoscale storm dynamics, and synoptic-scale forcing, severely threatens the safety of coastal megacities and urban infrastructure. As a core megacity in the Yangtze River Delta (YRD) region, Shanghai is significantly affected by the combined effects of the East Asian monsoon, complex urban-topography interactions, and sea-land breeze circulations, which not only amplify the intensity and frequency of extreme rainfall events in the city but also across the YRD region. These events further trigger severe urban waterlogging, traffic congestion, and damage to urban infrastructure. To address these challenges and advance integrated urban hazard forecasting, we develop a Physics-Informed Cross-Scale Synergistic Framework (CSSF) for urban severe convective rainfall hazards, which integrates multi-band radar observations and ordinal regression to update Numerical Weather Prediction (NWP) model forecasts. The integrated system features three core components aligned with urban hazard monitoring and forecasting needs: (1) Multi-Band Radar Observation Fusion Module, which fuses S/X-band radar data from a high-density network with machine learning-based synthesis loss functions, capturing fine-scale convective features to provide observational constraints for model forecast updates; (2) Gated Vertical Information Propagation, an improved ConvLSTM architecture with skip connections and bidirectional vertical information flow, which links radar-derived convective features to the temporal evolution of NWP forecasts, ensuring physical consistency in update processes; (3) Ordinal Regression-Based Forecast Update Module, which employs ordinal regression to integrate radar observations into NWP model outputs, updating forecast results to better reflect real-time convective development and extreme rainfall characteristics in urban areas. The integrated system is validated using 2019–2023 multi-band radar data from Shanghai’s high-density monitoring network, ERA5 reanalysis data, and NWP forecasts. Validation results demonstrate that the CSSF outperforms traditional ConvLSTM models and operational NWP systems in capturing convective initiation, extreme rainfall intensity, and urban waterlogging-prone areas, primarily by using radar observations and ordinal regression to dynamically update model forecasts, bridging the gap between observation and NWP forecast. This study presents a holistic integrated system for urban severe convective rainfall hazards, highlighting the value of radar-ordinal regression integration for NWP forecast updates, and provides a transferable approach to enhance urban hazard resilience and support evidence-based disaster risk management for climate-vulnerable cities.
How to cite: Ma, L.: Radar-Guided Ordinal Regression for Dynamic Update of NWP Forecasts: An Integrated Machine Learning System for Urban Convective Rainfall Hazards, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-29, https://doi.org/10.5194/ems2026-29, 2026.
Urban water bodies are increasingly recognized as nature-based solutions for heat mitigation, yet the relative contributions of turbulent exchange and advective transport to canal-induced cooling remain poorly constrained. This study employs PALM4U large-eddy simulation (LES) with realistic urban geometry to systematically investigate canal cooling mechanisms in Amsterdam, Netherlands, a dense historic city whose semi-circular canal ring provides a unique natural laboratory for examining varying wind-canal orientations within a single urban setting. Model evaluation against eddy covariance flux tower and urban weather station observations showed agreement for sensible heat flux (Index of Agreement = 0.94), net radiation (IoA = 1.00), and near-surface air temperature (IoA = 0.72), confirming LES as a reliable tool for quantifying urban surface-atmosphere energy exchange. Through controlled numerical experiments comparing simulations with and without canals, we decomposed the total cooling effect into its turbulent and advective components. The results reveal that wind-canal orientation is a fundamental determinant of cooling effectiveness. When wind flows perpendicular to canals, mechanically-driven canyon vortices actively redistribute cool air from the water surface into surrounding areas, with horizontal advection accounting for 60–80% of the total cooling effect over adjacent pavement and above the urban canopy. This redistribution extends cooling asymmetrically, propagating vertically up to 2–3 building heights and horizontally into adjacent street canyons lacking water features. When wind flows parallel to canals, the absence of a cross-canyon vortex suppresses lateral and vertical redistribution, confining cooling near the water surface where it is dominated by local sensible heat flux reduction. In this configuration, advection contributes a smaller and spatially inconsistent share of the cooling budget, limiting thermal benefits to the immediate vicinity of the canal. Crucially, wind-canal orientation does not strongly alter the overall intensity of cooling but critically determines its spatial distribution and heterogeneity. Temperature differences of up to 1.5°C arise within individual street canyons depending on position relative to the canal and prevailing wind, with direct implications for the thermal environment at the pedestrian level. These findings challenge simplified representations of water body effects in urban climate models and provide actionable guidance for blue infrastructure planning: canal orientation relative to prevailing summer winds is a key parameter governing the spatial reach and equity of cooling benefits across the urban fabric.
How to cite: Chen, X., Gadde, S., Steeneveld, G.-J., and Uijlenhoet, R.: Beyond the Water Surface: Advective and Turbulent Pathways of Canal-Induced Cooling in a Dense Historic City, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-53, https://doi.org/10.5194/ems2026-53, 2026.
Urban climate simulations increasingly rely on Local Climate Zone (LCZ) classifications to represent urban morphology and land–atmosphere interactions in numerical weather prediction models such as the Weather Research and Forecasting (WRF) model. Most studies assume that using LCZ maps with higher classification accuracy leads to the most realistic urban climate simulations; however, the sensitivity of modeled intra-urban temperatures to the accuracy of LCZ classification maps remains insufficiently explored. In this study, we investigate how the classification accuracy of manually generated LCZ maps, produced using the LCZ Generator and evaluated through cross-validation accuracy metrics, influences simulated urban temperature fields during a heatwave event in Amsterdam, the Netherlands. We performed ten WRF simulations for a heatwave period in early September 2022, including one default land-use simulation and nine simulations using LCZ maps with classification accuracies from 0.20 to 0.84. Modeled 2-m air temperature outputs were evaluated against observations from the Amsterdam Atmospheric Monitoring Supersite urban meteorological network.
Results show that LCZ map selection substantially affects simulated urban air temperature, with modeled temperatures consistently underestimating observations by 1.2 to 2.4 °C across the urban network, depending on the LCZ map used. The highest-accuracy LCZ map (0.84) produced the best overall temperature performance (1.2 °C mean absolute bias), followed by the default land-use map. However, several low- to medium-accuracy LCZ maps (e.g., accuracies from 0.31 to 0.48; mean absolute bias ≈ 1.9 °C) performed similarly to or even better than some higher-accuracy maps (e.g., accuracies from 0.75 to 0.81; mean absolute bias ≈ 2.2 °C), indicating that LCZ classification accuracy alone does not fully determine model performance. These findings demonstrate that LCZ map selection can substantially influence modeled intra-urban heat distribution, but increasing LCZ map classification accuracy does not necessarily translate into proportionally improved urban climate simulations. The results highlight the importance of evaluating LCZ datasets within modeling frameworks rather than relying solely on classification accuracy metrics, with implications for urban climate modeling, heat risk assessment, and prioritization of urban mapping efforts.
Acknowledgements. The authors acknowledge support from the 4TU-program HERITAGE (HEat Robustness In relation To AGEing cities), funded by the High Tech for a Sustainable Future (HTSF) program of 4TU, the federation of the four technical universities in the Netherlands.
How to cite: Milošević, D., Gadde, S., and Steeneveld, G.-J.: Does LCZ map accuracy matter? Sensitivity of WRF urban climate simulations during a heatwave in Amsterdam, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-500, https://doi.org/10.5194/ems2026-500, 2026.
The Urban Heat Island (UHI) effect describes the phenomenon whereby, during the nighttime, cities experience higher temperatures than the rural surroundings. This phenomenon amplifies indoor and outdoor heat stress for city residents during heat waves, which are becoming more frequent and intense as a result of climate change. Several factors determine the indoor temperature experienced by the occupants of buildings, including the building’s solar exposure, the materials used in its construction, and the storey on which it is located. For instance, during heat waves, people living on the upper storeys of buildings suffer greater thermal discomfort, which can lead to an increase in morbidity. To evaluate indoor comfort, the urban processes at the city scale (e.g. UHI) and the building‑level processes must be simulated simultaneously. This is achieved by coupling an Urban Canopy Model (UCM) with a Building Energy Model (BEM). Consequently, this work focuses on improving the BEM included into the Town Energy Balance (TEB) urban climate model, with the goal of better assessing indoor heat stress and city‑wide heating and cooling energy consumption. A significant improvement is the implementation of a multi‑storey energy balance model that represents indoor air temperature, specific humidity, and mean radiant temperature across the building storeys. All processes that were once modeled as averages over the entire building are now modeled storey‑by‑storey. For instance, shutter operation and window opening are simulated separately for each storey.
The improved TEB‑BEM is validated by comparing its results with newly conducted measurements from the VERTIC (Vertical Evaluation of Residential Indoor Comfort) campaign. The measurement campaign is conducted in a single, unoccupied building (a former student residence) that features two sets, West and East-facing, of four vertically stacked bedrooms. Continuous recordings of air temperature, relative humidity, mean radiant temperature, and conductive heat fluxes through the envelope are taken for each room throughout a full year in Toulouse. The meteorological forcing data required for TEB simulations is also collected. The results from the VERTIC campaign, launched in October 2025 and still in progress, will be presented with a particular emphasis on the differences between temperature and thermal comfort on different storeys (e.g. ground storey compared to top storey). The evaluation results for the improved TEB-BEM will also be presented.
Ultimately, this research will increase our understanding of how urban climate affects occupants’ thermal comfort and building energy demand while delivering decision‑support tools that enable cities to design effective adaptation strategies for climate change challenges.
How to cite: Darciaux, P., Schoetter, R., Masson, V., Bonhomme, M., and Ginestet, S.: Multi-Storey modeling of residents’ exposure to heat in the urban canopy model TEB, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-91, https://doi.org/10.5194/ems2026-91, 2026.
Adequate bedroom ventilation is an important determinant of nighttime indoor air quality, yet ventilation performance in the existing housing stock remains difficult to quantify in practice. In this observational study, we analyze three years (2023–2025) of continuous bedroom CO₂ measurements from 93 Amsterdam residences (Peerlings et al., 2024) to assess possible nighttime under-ventilation in bedrooms.
Because humans exhale CO₂ continuously, and indoor CO₂ is primarily removed by ventilation to the outdoors, CO₂ is often treated as a naturally occurring “tracer of convenience” for evaluating ventilation. We evaluate recorded CO₂ concentrations, in parts per million (ppm), against a small set of literature-based ventilation benchmarks. These benchmarks are usually stated as outdoor-air supply rates for a room or occupant. Here, we interpret them as equivalent local purging-flow thresholds (L/s), i.e. the effective flow rate at which contaminants at the sensor location are removed to the outdoors rather than recirculating back. Under plausible home-specific nighttime CO₂ generation rates (L CO₂/s), these flow rates correspond to excess CO₂ thresholds. We postulate that parts of the night may exhibit approximately constant flow conditions and source strengths. Such periods may arise under stable door and window states, slowly varying outdoor conditions, and a fixed number of sleeping occupants. We then distinguish between short-lived exceedance bursts and sustained CO₂ elevation, and report exceedance only in the latter case.
In larger households, CO₂ emitted in other rooms may contribute to the recorded bedroom concentration, and the strength of that contribution can vary from night to night as indoor transport conditions change. If source attribution cannot be resolved, ventilation cannot be uniquely inferred from single-sensor CO₂ data. We therefore restrict the main analysis to homes with household size ≤ 2 people, excluding larger households where we are unsure about how much of the recorded CO₂ originated from occupants in other rooms. Using this approach, we quantify the fraction of nights showing sustained exceedance of thresholds corresponding to a range of literature-based minimum ventilation rates.
Exceedances of these thresholds indicate possible periods of insufficient local pollutant removal capacity and provide a basis for assessing nighttime bedroom ventilation performance across homes and seasons in Amsterdam. Preliminary analyses suggest that some homes exhibit recurring sustained exceedance of lower benchmark values. Finally, we demonstrate how uncertainty in source strength, outdoor reference concentrations, and sensor baselines affects the robustness of this classification, and highlight the limits of interpretating single-sensor CO₂ data as direct night-specific ventilation estimates.
How to cite: Lenssen, J., Peerlings, E., Dekkers, F., and Steeneveld, G.-J.: Analysing nighttime ventilation rates in bedrooms of Amsterdam residences , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-235, https://doi.org/10.5194/ems2026-235, 2026.
Urban areas can influence local precipitation patterns and amounts. Although earlier studies have focused on the magnitude of precipitation modification by cities, this study develops a method to identify the source areas of precipitation rather than the location where the precipitation falls. We illustrate this for the western part of the Netherlands for 2017–2023. Precipitation anomalies are located using radar-derived precipitation data and traced back through the Lucas-Kanade Optical Flow technique to their source areas by following the motion of the precipitation for each rainfall event. Using machine-learning techniques, the influence of hypothesized causes—urban heat, surface roughness, air pollution, and their interactions—on precipitation anomalies is evaluated. We find that urban and industrial areas generally enhance downwind precipitation, while large rural areas weaken it. Nearly 30% of the study area frequently generates enhanced precipitation downwind, with an average increase of 16.1% of the total precipitation. The strongest signal for the initiation of precipitation modification is present near the industrial areas of IJmuiden and the Rotterdam Harbor, and around the relatively large urban areas of The Hague and Utrecht. Large sparsely built areas like the Green Heart generally reduce precipitation downwind. This confirms the hypothesis that urban and industrial areas can enhance precipitation, while rural areas tend to weaken it. The identified source areas of precipitation modification are consistent across different seasons. We also find that surface roughness and concentrations (SO2 is used as a proxy for air pollution) show a positive correlation with precipitation anomalies. Air pollution appears the most influential predictor of precipitation anomalies in this study, followed by the interaction between air pollution and urban heat, and then surface roughness. Compound effects show a lower contribution than individual variables. This methodology provides a novel perspective on precipitation modification, offering a foundation to refine assumptions about source locations and improve understanding of the mechanisms driving the effect.
More:
van derGraaff, J. & Steeneveld, G.-J. (2026) A new method to identify and explain sources of precipitation modification, illustrated for the western Netherlands. Quarterly Journal of the Royal Meteorological Society, e70173. Available from: https://doi.org/10.1002/qj.70173
How to cite: van der Graaff, J. and Steeneveld, G.-J.: A new method to identify and explain sources of precipitation modification, illustrated for the western Netherlands, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-246, https://doi.org/10.5194/ems2026-246, 2026.
The frequency and severity of heatwaves are on the rise globally, with several European cities in particular experiencing the most extreme phenomena in recent history. Over the past few years people have become increasingly aware regarding the implications of thermal stress on dwellings and living conditions. However, the links between outdoor weather drivers and indoor stressors are still under investigation. This study aims to shed light on those links, using the "living lab" of the Green Village at TU Delft as a reference site.
The MSc thesis-based research will incorporate field measurements and modelling output from both building physics and atmospheric science. Outdoor meteorological parameters such as air temperature, wind speed, humidity and solar fluxes will be used for the derivation of biometeorological indices from the Rayman modelling platform. The indices of interest are the Physiological Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI), which will be used as boundary conditions for the study of the indoor environment. Indoor monitoring will include data for operative temperature, relative humidity and light irradiation. Furthermore, CO2 concentrations will be used as proxies for the study of occupant-driven ventilation. EnergyPlus simulations will quantify heat transfer, thermal storage and radiative exchanges in the building envelope. Results from the monitoring and the modelling campaigns will be complemented with the addition of human-centred research in the form of questionnaires and/or semi-structured interviews, in order to address thermal exposure from a biometeorological perspective.
All in all, this multi-layered research will attempt to quantify the impact of outdoor heat on indoor spaces while taking human perception into account. Enhanced understanding of these dynamics will facilitate future designs for houses in the face of climate change and extreme urban heat.
Keywords: Human biometeorology, Heat stress indices (PET, UTCI), Indoor thermal comfort, Urban climate adaptation, Indoor–outdoor thermal coupling
How to cite: Papanikolaou, N. and Droste, A.: Coupling Outdoor Heat Stress and Indoor Thermal Exposure in Dwellings: A Human Biometeorology Approach, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-219, https://doi.org/10.5194/ems2026-219, 2026.
Madrid is located in a topographically complex environment influenced by the Guadarrama mountain range, where thermally-driven flows (TDFs), such as mountain–valley breezes, interact with the urban heat island (UHI) and modulate local meteorological conditions. Despite the well-documented warming associated with increasing urbanisation, the coupling between TDFs and the UHI remains insufficiently characterised.
Long-term observational datasets (1961–2023) from urban and rural meteorological stations reveal significant warming trends, more pronounced in summer than in winter, together with marked differences between maximum and minimum temperatures. The UHI intensity, defined from minimum temperature differences, shows mean values close to 2 °C (depending on the rural reference station), and exhibits a strong dependence on synoptic conditions, reaching up to ~3.3 °C under stable situations, which are, indeed, ideal conditions for TDF development.
The variability of TDFs is analyzed using both long-term observations and specific field campaigns, focusing on the diurnal and seasonal cycles as well as their frequency, intensity and directional patterns. These circulations play a key role in urban ventilation, air quality, and boundary layer structure, with significant implications for thermal comfort and population vulnerability.
The interaction between TDFs and the UHI is further examined through mesoscale simulations using the Weather Research and Forecasting (WRF) model with the BEP-BEM urban canopy scheme (Carbone et al., 2024; Salamanca et al., 2010; Martilli et al., 2002). The results highlight a two-way coupling: urban-induced thermal anomalies modify local circulations, while TDFs influence the intensity and spatial distribution of the UHI.
These findings provide new insights and an integrated view of the interactions between urbanization, topography, and atmospheric dynamics in complex environments, contributing to the improvement of urban representation in numerical models and supporting the development of climate adaptation strategies.
How to cite: Carbone, J., Ortiz-Corral, P., Román-Cascón, C., Sanchez, B., Martilli, A., Santiago, J. L., Cicuéndez, V., Inclán, R. M., Royé, D., Sastre, M., and Yagüe, C.: Thermally driven flows and urban heat island interactions in Madrid: a multi-scale analysis., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-522, https://doi.org/10.5194/ems2026-522, 2026.
The nocturnal nitrogen cycle strongly influences wintertime air quality by governing the formation of particulate nitrate and nitryl chloride (ClNO2), but it remains difficult to simulate accurately in chemical transport models because of uncertainties in heterogeneous N2O5 chemistry and chloride availability. This study investigates how these two factors jointly affect model representation of nighttime nitrogen chemistry and whether their combined revision can improve simulations of particulate nitrate and ClNO2. Using field observations and CMAQ simulations, we revised two key components of the nocturnal nitrogen cycle. First, observation-constrained parameterizations were applied to update the N2O5 uptake coefficient and ClNO2 yield. Second, chloride source representation was improved by incorporating additional anthropogenic and natural chlorine emissions. The revised framework was then used to examine the individual and combined effects of chemical and emission updates on the nighttime conversion of NOx to particulate nitrate and ClNO2. The results show that revising either heterogeneous chemistry or chloride sources alone provided only partial improvement, whereas the most substantial improvement was obtained when both were updated together. In particular, correcting the N2O5 uptake coefficient and ClNO2 yield reduced biases in nocturnal heterogeneous processing, while improved chloride emissions provided a more realistic reservoir for ClNO2 formation. Together, these changes led to a more consistent representation of the nocturnal nitrogen cycle and improved simulations of both particulate nitrate and ClNO2. These findings highlight that accurate prediction of wintertime particulate nitrate requires the coupled treatment of heterogeneous N2O5 chemistry and chloride source availability.
Acknowledgment: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2025-16070879).
How to cite: Ma, Y., Kim, M. J., Lee, T., Song, J., Choi, D.-R., Hong, S.-C., Lee, J.-B., and Lee, Y.: Joint Effects of N2O5 Heterogeneous Chemistry and Chloride Sources on the Nocturnal Nitrogen Cycle, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-452, https://doi.org/10.5194/ems2026-452, 2026.
Aerosol dry deposition is an important sink of atmospheric particles, but its representation in chemical transport models remains uncertain. In particular, conventional parameterizations often neglect collection efficiency associated with microscale surface characteristics, which can strongly affect particle capture by surfaces. This study investigates how this process influences aerosol dry deposition and PM2.5 over East Asia. Tower-based measurements of aerosol dry deposition velocity were used to evaluate model performance. Simulations without the enhanced collection efficiency substantially underestimated aerosol dry deposition velocity, by more than one order of magnitude relative to observations. This is consistent with previous studies reporting that the model underestimated observed deposition velocities for particles in the 0.2–2.0 μm size range by one to two orders of magnitude over forested regions. To address this discrepancy, collection efficiency associated with microscale surface characteristics was incorporated into the aerosol dry deposition framework, and its effects on regional PM2.5 simulations over East Asia were evaluated across different land-use types and seasonal conditions. The updated representation increased aerosol dry deposition velocity and enhanced particle removal at the surface, leading to lower PM2.5 concentrations over parts of East Asia. These results demonstrate that collection efficiency associated with microscale surface characteristics plays a critical role in aerosol dry deposition and can substantially affect regional PM2.5 simulations. These findings highlight the need to refine aerosol deposition parameterizations to improve air quality predictions and reduce uncertainty in assessments.
Acknowledgment: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2025-16070879).
How to cite: Lee, Y., Kim, M. J., Hong, J., Lee, J.-B., Lee, Y. H., and Hong, S.: Impact of Collection Efficiency Associated with Microscale Surface Characteristics on Aerosol Dry Deposition and PM2.5 over East Asia, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-463, https://doi.org/10.5194/ems2026-463, 2026.
Urban water bodies, which are generally shallow and small, are increasingly recognized as a nature-based solution to facing extreme hydroclimate events, yet their thermal behaviour remains poorly captured by existing lake models. Most operational surface water modules in climate models were designed for deeper, open-water systems and do not accurately represent the depth-limited physical processes governing shallow urban lakes, leading to systematic errors in simulated temperature stratification, surface energy fluxes, and, crucially, nocturnal heat release. This simulation gap directly undermines the reliability of thermal impact assessments and the design of urban water-based climate adaptation strategies.
This study evaluates and adapts the WRF-Lake model to correctly represent the thermal dynamics of shallow urban water bodies. To enable systematic sensitivity analysis and model adaptation, WRF-Lake was reimplemented as a standalone Python model, decoupled from the WRF atmospheric framework. Validation against continuous temperature profile measurements from a 0.7 m deep pond in Delft, the Netherlands, reveals that while the default model reproduces depth-averaged temperatures with an RMSE of 1.4°C, it systematically fails to capture vertical temperature stratification observed during low-wind, high-insolation conditions. To identify the physical processes most critical at shallow depths, a sensitivity analysis was conducted on four depth-dependent physical schemes: absorbed solar radiation, near-surface absorption, light extinction, and wind-driven mixing.
The adapted model, incorporating modified parameterizations of the depth-sensitive processes identified in the sensitivity analysis, was evaluated against the same validation dataset. Compared to the default model, the adapted model reduces the layer-averaged RMSE from 1.4°C to 0.7°C, a 50% improvement, and substantially better reproduces the diurnal evolution of temperature and vertical stratification that the default model consistently fails to capture at this scale. These results demonstrate that physically consistent, depth-targeted adaptations to an operational lake model can reliably represent the thermal dynamics of shallow urban water bodies, filling a critical gap in the simulation of small-scale urban water systems and providing a foundation for their integration into broader urban climate modelling frameworks.
How to cite: Breheret, D., Chen, X., Droste, A., and Bloemendal, M.: Adapting WRF-Lake for Shallow Urban Water Bodies: Improved Simulation of Thermal Dynamics and Vertical Stratification at Reduced Depth, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-273, https://doi.org/10.5194/ems2026-273, 2026.
Improving our understanding of the behaviour of near-surface atmospheric properties will help us prepare for changing climatic conditions. Especially in cities, where human health risk is concentrated, and where surfaces present a radical departure from natural conditions. Research shows that air temperature is relatively high in cities as a consequence of the urban heat island effect, increasing the risk of heat stress. Humidity too affects human thermal perception and is altered by the urban environment. The urban effect on near-surface atmospheric humidity, however, is not nearly as well studied as the urban effect on air temperature.
Studies of the urban effect on humidity primarily compare measurements in proximate but contrasting urban and rural locations, aiming to establish the existence and behaviour of an urban moisture excess/deficit (also known as the urban moisture island effect). This has yet to be done with crowdsourced data. Unlike traditional stationary data collection methods, crowdsourced data is both temporally and spatially dense. Using quality controlled data from privately operated weather stations may therefore allow for a better representation of the heterogeneous urban environment than previously possible for studies of the urban effect on humidity.
From a network of personal weather stations managed by the DelftMeet citizen science project, we used 35 stations in an area of 17.5 km2 in Delft, the Netherlands. Each weather station measured inter alia air temperature, relative humidity, and air pressure approximately every 5 minutes. These measurements were aggregated to 15 minute averages and used to calculate vapour pressure values for assessing the urban moisture excess/deficit. The measurement campaign ran for a period of 24 months from 01/10/2023 to 30/09/2025. Excluding missing values this left approximately 2,379,282 vapour pressure values with which to conduct comparisons with rural reference data.
Although the analysis is yet to be finalised, initial results show a slight moisture deficit in Delft compared to its surroundings. This deficit does seem to persist throughout the day and throughout the year, however. Such a stable but weak moisture deficit would certainly be uncommon.
How to cite: Severiens, E., Droste, A., De Vries, S., Van Esch, M., and Uijlenhoet, R.: Exploring the urban effect on humidity using crowdsourced data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-571, https://doi.org/10.5194/ems2026-571, 2026.
Nowadays, one of the major challenges posed by the combined effects of urban heat island (UHI) phenomenon, extensive urbanization, and climate change is represented by the increased energy demand for household cooling and heating. Our analysis focuses on 4 middle sized cities from north-eastern Romania (Iași, Bacău, Botoșani and Suceava) facing both high energy demand for winter heating, but also an increased demand for summer cooling, driven by the more frequent heatwaves occurred in the recent climate change context. The trend analysis made on long-term RoCliB dataset underlines that the cooling demand increases over the analyzed cities after 2005 (+ 50-100 CDD per decade), while the heating demand has shown a more gradual decline after 1990 (- 100-200 HDD per decade).
The study is primerly based on a three-year timespan (2023-2025) of hourly air temperature measurements collected at paired observation sites located in the central areas of the analyzed cities and their peripheries, capturing the peak intensity of the UHI effect. Additionally, the hourly data is supplemented with data from mobile measurements to refine the UHI intensity estimates. The analyzed cities exhibit a UHI intensity of about 1°C at the annual mean level, reaching its maximum intensity during the summer nights (2-3°C).
Based on this dataset, heating and cooling degrees are firstly computed for the paired stations to outline the difference imposed by the UHI. Generally, the cost for winter heating (>25000 HDD) compared to summer cooling (5000 CDD) for a household is 5 times higher, while the need for heating/cooling manifests during 250/120 days, with a difference of 5 days for both between cities center and their periphery, imposed by the UHI. As well, from our assessment at the individual household level, the higher energy costs for cooling in the UHI core during the warm season are offset by lower heating costs in the cold season.
However, the higher density of households in the central area of the cities tends to shift the overall energy demand toward increased summer cooling in the future. The shift in energy demand imposed by the UHI toward less winter heating and more summer cooling is afterthat extrapolated for the next decades using future climate models projections accessed from RoCliB dataset over Romania. The results can help stakeholders strengthen climate resilience in building performance and energy demand.
Acknowledgement. This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS - UEFISCDI, project number PN-IV-P1-PCE-2023-0897, within PNCDI IV.
How to cite: Sfîcă, L., Amihăesei, V.-A., Ichim, P., and Hrițac, R.: Winter gains versus summer losses - the impact of urban heat island on energy consumption in middle sized cities of north-eastern Romania under climate change, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-595, https://doi.org/10.5194/ems2026-595, 2026.
Approximately 57% of the global population lives in urban areas, and this proportion is expected to increase in the coming decades. Consequently, cities are where a large and growing share of the population experiences the impacts of climate change. Urban environments can also intensify local warming because of their morphology and physical characteristics, including building density, anthropogenic heat emissions, impervious surfaces, and the thermal inertia of urban materials. Urban green spaces, such as parks, can partly offset these effects through shading, improving human thermal comfort. However, the thermal effect of urban parks is commonly assessed using satellite-derived surface temperature, which provides only instantaneous observations and does not capture its temporal evolution.
In this study, we propose an alternative method to evaluate the thermal effect of urban parks using large-eddy simulations (LES) with the PALM model system. The analysis focuses on Villa Ada, a 160 ha urban park in Rome, during a heatwave event in October 2023. Two nested simulation domains, with isotropic grid resolutions of 20 m and 10 m, are forced by boundary conditions from the MOLOCH mesoscale model. The simulations are used to investigate the diurnal variability of surface temperature and 2 m air temperature. The park thermal effect is quantified using a buffer-based approach with concentric rings extending up to 600 m from the park boundary, enabling the analysis of horizontal temperature gradients and their temporal evolution throughout the day. The framework also enables comparison among three land-cover scenarios: the current park configuration (baseline), a fully tree-covered configuration, and a short-grass configuration.
Results reveal a marked diurnal cycle in the park thermal effect, with lower magnitudes in the morning, a distinct maximum in the early afternoon, and a gradual decrease toward the evening. The dense-tree scenario shows the strongest cooling effect, with values reaching up to 8.5 K for surface temperature and 6.1 K for 2 m air temperature, whereas the baseline scenario also produces cooling, though of slightly lower intensity. By contrast, the short-grass scenario leads to higher surface temperatures across all buffers, indicating a warming effect.
The spatial extent of the cooling signal remains relatively stable in the baseline and dense-tree scenarios, with values typically between 330 m and 380 m. In contrast, the short-grass scenario does not show a comparable cooling signal. The results also reveal a bimodal distribution of surface temperature in the park surroundings, reflecting the contrast between vegetated and built surfaces. These findings highlight that the park cooling effect is a dynamic process, strongly controlled by diurnal evolution and surface heterogeneity, and cannot be fully assessed by instantaneous satellite observations.
How to cite: de V. Brondani, D., Christian Landi, T., Drofa, O., Imbrenda, V., Gaeta, A., Coluzzi, R., Decesari, S., Cava, D., Giostra, U., and Mortarini, L.: Diurnal evolution and spatial extent of urban park cooling from large-eddy simulations in Villa Ada (Rome), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-711, https://doi.org/10.5194/ems2026-711, 2026.
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