Urban environments, characterized by their complex spatial and temporal heterogeneity, significantly influence local and global climate patterns. Remote sensing technologies, with their ability to provide high-resolution information about urban morphology, materials, and vegetation cover, offer invaluable tools for unraveling the intricate interactions within urban climates.

This session invites contributions that explore innovative applications of remote sensing techniques to advance our understanding of urban climate dynamics.

Topics of interest include:

• Satellite-based remote sensing: Utilization of thermal, hyperspectral, and multispectral imagery to assess urban heat island effects, surface energy balance, and air quality and other urban dynamics.
• UAV-based remote sensing: Leveraging unmanned aerial vehicles (UAVs) for fine-scale mapping of urban morphology, vegetation cover, and microclimate variations.
• Aircraft-based remote sensing: Employing research aircraft to collect detailed urban climate data, including atmospheric profiles, boundary layer dynamics, and pollutant concentrations, etc.
• Emerging remote sensing technologies: Exploring the potential of hyperspectral imaging, LiDAR, and synthetic aperture radar (SAR) for enhancing urban climate research.
• Laser point cloud analysis: Utilizing point cloud data derived from LiDAR and other sensors to map urban surfaces, characterize building morphology, and assess urban green infrastructure.

We encourage interdisciplinary research that integrates remote sensing with modeling, field observations, and socio-economic data to develop comprehensive insights into urban climate processes and inform sustainable urban planning and adaptation strategies.

In-situ measurements provide essential ground-truth data for validating and calibrating remote sensing observations and numerical models of urban climates. This session invites contributions that showcase innovative in-situ observation techniques and their applications in advancing our understanding of urban climate processes.

Topics of interest include:

• Meteorological stations: Deployment and operation of meteorological stations to measure temperature, humidity, wind speed and direction, precipitation, and other atmospheric variables.
• Eddy covariance flux towers: Utilization of eddy covariance techniques to quantify the exchange of energy, momentum, and carbon dioxide between urban surfaces and the atmosphere.
• Atmospheric profiling: Profile observations of the urban atmosphere (and boundary layer) using in-situ methods (e.g. radiosondes, tethered balloons, UAS), remote sensing on ground-based (or also spaceborne platforms), such as Doppler wind lidars, automatic lidars and ceilometers, differential absorption lidars, microwave radiometers, or infrared radiometers.
• Ground-based remote sensing: Deployment of thermal cameras, weather stations, LiDAR, and sonic anemometers and other remote sensing instruments to capture high-resolution measurements of urban climate variables.
• Air quality monitoring networks: Establishment and maintenance of air quality monitoring networks to assess pollutant concentrations and fluxes nd their spatial and temporal distributions.
• Urban green,blue, and brown infrastructure monitoring: Measurement of environmental parameters within urban green-, blue-, and brown-spacessuch as parks, gardens, ponds, lakes, areas in urban areas with fertile soil, and green roofs, to evaluate their contribution to mitigating urban heat island effects and improving air quality.
• Data integration and analysis: Development of methods for integrating in-situ data with remote sensing observations and numerical models to improve our understanding of urban climate dynamics.
• Other New/Innovative observational techniques or approaches which enhances the understanding of urban climate dynamics.

We encourage submissions that demonstrate the value of in-situ observations for addressing specific research questions, validating modeling results, and informing urban climate adaptation and mitigation strategies.

Crowdsourcing and community science initiatives offer innovative approaches to engage diverse communities in urban climate research, expanding the spatial and temporal coverage of observations and fostering broad participation in environmental monitoring. This session invites contributions that explore how crowdsourcing and community science methods can enhance urban climate research.

Topics of interest include:

• Community science projects: Designing and implementing projects to collect data on urban climate variables such as temperature, air quality, and green space.
• Crowdsourcing platforms: Leveraging platforms to mobilize widespread participation in data collection and analysis.
• Mobile applications: Developing apps for community members to report observations and contribute data.
• Data quality assurance: Ensuring accuracy and reliability in community-contributed data.
• Societal engagement and education: Engaging communities in urban climate research to raise awareness about climate change.
• Community-based research: Collaborative projects that involve communities and researchers in addressing local climate challenges.
• Ethical considerations: Ethical implications of crowdsourcing and community science, including data privacy and informed consent.

We welcome submissions demonstrating how crowdsourcing and community science contribute to urban climate research, foster community involvement, and inform decision-making.

Microscale urban climate modelling focuses on understanding atmospheric processes within small urban features, such as individual buildings, streets, and parks. These models, typically using computational fluid dynamics (CFD) or large-eddy simulations (LES), are critical for simulating fine-scale turbulence, heat transfer, and pollutant dispersion at resolutions down to meters. Despite significant advancements, accurately capturing the complex interactions between urban geometries, vegetation, water, and atmospheric flow remains a challenge, especially under varying meteorological conditions. Research gaps include refining turbulence modelling, integrating more detailed urban vegetation interactions, and improving the coupling between microscale and larger-scale models.

We encourage submissions on novel modelling techniques, high-resolution simulations, and experimental studies, particularly those addressing urban heat islands, urban hydrology, thermal comfort, and air quality, among other topics. Studies exploring data-driven improvements and model validation through field campaigns are also welcome. Examples of studies to be submitted to the session can be related to the advancements in CFD and LES techniques for urban environments, microscale urban heat and thermal comfort modelling, modelling of pollutant dispersion at street level, validation and integration of urban microclimates with larger scales, etc.

City scale urban climate models focus on simulating atmospheric processes across neighbourhoods up to entire cities, with spatial resolutions typically between hundreds of meters and a few kilometres. These models are crucial for studying phenomena such as the urban heat island effect and air pollution. However, challenges remain in improving model representation of urban heterogeneity, energy fluxes, and land-atmosphere interactions. Recent advances include better integration of satellite data and high-resolution urban datasets. However, research gaps persist in accurately modelling urban-rural interactions, energy balance components, and the impact of new urban infrastructure.

We invite research on improving model accuracy, incorporating real-time observations, and exploring climate adaptation strategies in urban settings. Example of topics include modelling urban heat island effects at city scale, improvements in energy flux representation in urban models, incorporating nature-based solutions into city scale models, validation using multi-source datasets (e.g., satellite, ground measurements), etc.

Mesoscale models and regional climate models bridge the gap between local urban dynamics and larger atmospheric processes, covering entire metropolitan regions and their surroundings. These models, such as the Weather Research and Forecasting (WRF) model coupled with urban modules or (other) regional climate models, are essential for understanding the interactions between urban areas, regional weather patterns, and climate change scenarios, including the urban heat island’s regional impacts and mesoscale flows. Despite progress, challenges remain in linking fine urban details with mesoscale dynamics, especially for extreme weather events and future climate projections. Key research gaps include better coupling of urban features with mesoscale processes and regional climate models, improved parameterization of urban effects, and understanding feedbacks between cities and regional climates in both present-day and future climate change scenarios.

We encourage submissions on novel modelling approaches, integration of urban data, and extreme weather event studies, with a particular interest in how urban areas influence regional climate under various climate change scenarios. Topics of interest can be urban impacts on regional weather systems, enhanced coupling between urban and mesoscale/regional climate models, simulation of extreme weather events (e.g., heatwaves, storms) in urban region, long-term urban climate projections at regional scale, etc.

AI and machine learning (ML) have emerged as powerful tools for urban climate modelling, enabling more efficient model calibration, improved predictive capabilities, and integration of vast, complex datasets. Recent advances in ML algorithms have provided novel methods for simulating urban climate processes, such as energy demand, heat island effects, and air quality. However, challenges include the interpretability of AI-driven models and integrating ML approaches with physical-based models.

We encourage submissions that explore innovative applications of AI/ML in urban climate prediction, hybrid modelling approaches, and data assimilation using machine learning. Studies that leverage big data or focus on improving forecast accuracy and model interpretability are particularly welcome. Topics of interest can be AI/ML applications in urban heat, air quality, and energy demand modelling, hybrid models combining ML with physical models, real-time data assimilation using machine learning, AI-driven optimization of urban climate adaptation strategies, etc.

Data assimilation integrates real-time observational data into climate models, improving forecast accuracy and responsiveness. In urban climate modelling, this approach is vital for correcting biases, refining predictions, and capturing fast-evolving processes like heat waves or localized storms. Despite advances, there are still gaps in effectively assimilating data from heterogeneous urban sources, including satellite, drone, and sensor networks, while also ensuring validation and consistency across models.

We encourage contributions that demonstrate novel data assimilation techniques, especially those improving the accuracy of urban heat and flood predictions, integrating diverse datasets, and real-time forecasting. Submissions that focus on assimilation in high-resolution urban models and enhancing extreme weather forecasting in cities are particularly encouraged. Topics of interest are novel data assimilation methods for urban models, integration of multi-source urban datasets (e.g., sensor networks, satellite, drones), real-time forecasting of urban heat and flood events, data assimilation for improving extreme weather event response, etc.