Urbanization represents one of the most significant anthropogenic changes to the Earth’s surface, with profound climatic effects across scales through modifying the surface biophysical properties and hence perturbing the Earth’s surface energy balance. An additional 2.5 billion people projected to reside in urban areas by year 2050, nearly doubling the world’s current urban population in just three decades. This inevitable urbanization coupled with climate change will not only expose cities and their residents to substantial risks across the world, but also presents a historic and time-sensitive opportunity to mitigate and adapt to the negative impacts of future changes and to advance global sustainable and resilient growth. Addressing this grand challenge, however, requires advanced data and tools that better represent and/or resolve urban effects and their complex two-way interactions with climate across spatiotemporal scales, both for improved scientific understanding of cities and for planning effective resilient strategies. Recent advances in AI/ML, satellite remote sensing, high-resolution urban-resolving Earth system modeling, and advanced computing have enabled development of many of these advanced tools and opened up promising opportunities. In this talk, I will present some examples, using our recent work, on how AI/ML, hybrid modeling, and advance computing could help empower urban climate research as well as the associated challenges.

How to cite: Zhao, L.: Developing advanced urban-resolving tools for urban climate research, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1125, https://doi.org/10.5194/icuc12-1125, 2025.

Accurate urban surface energy balance (SEB) predictions are essential for reliable weather forecasting, influencing near-surface variables and serving as boundary conditions for city-scale atmospheric flow. However, results from the Urban-PLUMBER intercomparison project (Lipson et al., 2024) indicate that despite advances in urban SEB modelling, conventional models often underperform compared to simple empirical benchmark models derived from flux tower observations.

This study investigates whether machine learning (ML) models, trained on flux tower observations and land surface descriptors, can outperform traditional urban SEB models. Our results show that ML approaches achieve lower errors across standard metrics than nearly all Urban-PLUMBER participant models. However, ML models face challenges, particularly in maintaining expected physical relationships such as energy budget closure. We discuss these limitations and propose solutions, including hybrid approaches that combine ML with conventional models to address the sparse global coverage of urban SEB observations.

Since the SEB underpins urban surface-atmosphere interactions, its representation will be essential in urban digital twins. The neural network approach presented is computationally efficient, utilizes standard urban land surface model input parameters, and can be readily integrated into digital twin frameworks. By improving SEB predictions, this method has the potential to enhance forecasts of urban hazards such as heatwaves and flooding.

How to cite: Bluun, L., Lipson, M., and Higgs, L.: Observations Driven Machine Learning Prediction of the Urban Surface Energy Balance, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1129, https://doi.org/10.5194/icuc12-1129, 2025.

How to cite: Lin, G.-S., McGrory, M., Hertwig, D., Thor Smith, S., Grimmond, S., and Manoli, G.: Towards dynamic heat exposure assessments using open data and agent-based modelling: the case of Lausanne and Geneva, Switzerland, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-294, https://doi.org/10.5194/icuc12-294, 2025.

Modeling residential electricity consumption is complex due to intertwined environmental and socioeconomic factors, requiring new methods to estimate patterns and provide datasets for diverse stakeholders. This project develops a Bayesian hierarchical Spatio-Temporal model (BHST) to downscale U.S. state-level residential electricity data (2000–2020) to the census tract level, capturing variations and uncertainties driven by climate, land use, building, and household characteristics within local urban scales. By integrating spatial and temporal dependencies, the BHST addresses gaps in high-resolution electricity consumption data quantifying uncertainties in estimates. The model leverages high-resolution datasets, including meteorological climate reanalysis products, land use, and gridded population data, alongside census-derived socioeconomic variables. Using the integrated nested Laplace approximation, the approach efficiently handles computational challenges associated in modeling Bayesian spatial and temporal processes. Model validation involves residual analysis (Moran’s I) and K-fold cross-validation at the state level. At the census tract level, the volume-preserving property is tested using posterior predictive checks to compare that aggregates match when downscaling to census tracts. Initial testing focuses on the Southwest U.S., presenting the theoretical formulation, development, and testing of the BHST downscaling method using data from Arizona, New Mexico, Utah, Colorado, Nevada, and California. This research contributes a robust methodological framework, a detailed analysis of socioeconomic and climatic drivers of electricity uses in the residential sector, and a valuable dataset to advance research, policy, and practice in energy efficiency and climate adaptation.

How to cite: Ramírez Aguilar, E. A. and Sailor, D.: A Bayesian Spatio-Temporal model to Downscaling State-Level Residential Electricity Data., 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-522, https://doi.org/10.5194/icuc12-522, 2025.

Understanding climate-driven risks in urban areas necessitates improved representation of urban climate processes in Earth system models (ESMs). Machine learning (ML) has emerged as a promising tool to enhance this representation by capturing complex, nonlinear relationships in urban surfaces-atmospheric processes. Although prior ML methods achieve considerable accuracy in emulating urban climate processes for ESMs, they may compromise by focusing on a single variable and not necessarily obeying the physical laws. In contrast, physics-guided ML integrated physical elements offers promise in overcoming the above limitations, while also showcasing potential for enhanced generalization. In this work, we present a multi-task physics-guided Transformer, named UCformer, to emulate nonlinear interactions between urban surfaces and atmospheric forcing, and to generalize urban climate dynamics over time. The architecture of UCformer incorporates physical and climatic knowledge, enabling it to achieve superior performance in urban climate multi-task estimations compared to baseline models. The investigation of urban surface parameters suggests that the thermal parameters have the most integrated impact on UCformer emulation skill, with radiation parameters ranking second. Additionally, an ablation study further examines the performance gains from specific components of the UCformer architecture, underscoring the significant potential of integrating physical and climatic knowledge to enhance urban climate modeling. 

How to cite: Xia, J., Ling, F., Yu, J., Li, Z., Zhang, H., Topping, D., Bai, L., and Zheng, Z.: A Physics-Guided Deep Learning Architecture for Multi-Task Modeling of Urban Climate, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-117, https://doi.org/10.5194/icuc12-117, 2025.

Urban morphology plays a pivotal role in shaping urban heat islands (UHI), especially in high-density cities, by influencing land surface temperature (LST) and air temperature. While previous studies have explored the relationships between urban morphology and temperature, they often fail to simultaneously capture the nonlinear interactions of these relationships and spatial heterogeneity arising from regional variations in urban form and environmental conditions. Furthermore, the varying influence of urban morphology on diurnal and nocturnal UHI remains insufficiently understood. This study bridges this gap by applying Geographically Weighted Machine Learning (GWML) and Graph Neural Network (GNN) models to investigate the non-stationary relationships between UHI and its influencing factors. Using high-resolution diurnal and nocturnal LST data from Landsat and ECOSTRESS, combined with 3D building morphology metrics (e.g., Sky View Factor), road network attributes, socio-demographic characteristics, and landscape indices, we systematically analyse the spatial variations in these associations. The analysis includes calculating Moran’s I to detect spatial patterns and comparing the predictive performance of GWML and GNN against Geographically Weighted Regression (GWR). SHapley Additive exPlanations (SHAP) enhance interpretability of explainable GeoAI (X-GeoAI) models, revealing localized impacts of key influencing factors. Our findings demonstrate significant spatial variations in the effects of 2/3D urban morphology on UHI across diurnal and nocturnal cycles. These insights provide a robust foundation for targeted UHI mitigation strategies and adaptive urban planning. This work highlights the potential of advanced GeoAI methods in urban climate research and offers actionable pathways for enhancing climate resilience in high-density cities.

How to cite: Wang, Y., Liu, P., and Stouffs, R.: Revealing the impact of 2D/3D urban morphology on spatial heterogeneity of diurnal and nocturnal UHI through X-GeoAI driven analytics, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-888, https://doi.org/10.5194/icuc12-888, 2025.

Urban and climate science convergence research often benefits from urban climate models. The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. Despite its advanced capabilities, CLMU presents significant challenges for users unfamiliar with numerical modeling due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, a toolkit was developed, including (1) an operating system-independent containerized application developed to streamline the execution of CLMU and (2) a Python-based tool (Pyclmuapp) used to interface the containerized CLMU and create urban surface data and atmospheric forcing data for the model. This toolkit enables users to simulate urban climate and explore climate-related variables such as urban building energy consumption, urban water balance, and human thermal stress. It also supports the simulation under future climate conditions and the exploration of urban climate responses to various surface properties, providing a foundation for evaluating urban climate adaptation strategies. Overall, this toolkit makes urban climate modeling more accessible, promoting broader applications from research to practical urban planning and policy-making. Detailed documentation for instructions can be found at https://envdes.github.io/pyclmuapp.

How to cite: Yu, J., Sun, Y., Lindley, S., Jay, C., Topping, D. O., Oleson, K. W., and Zheng, Z.: Integration and Execution of Community Land Model Urban (CLMU) in a Containerized Environment, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-20, https://doi.org/10.5194/icuc12-20, 2025.

How to cite: Niyogi, D.: Atmospheric Urban Digital Twins (AUDTs) - Definition, Examples, and Case Studies, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1132, https://doi.org/10.5194/icuc12-1132, 2025.

Urban areas face increasing challenges from climate change and require comprehensive approaches to address both mitigation and adaptation. The complexity of this integrated approach can be tackled through the adoption of digital enabling technologies in the realm of smart cities. In recent years, numerous Urban Digital Twins have been developed by cities worldwide to better support planners and designers with data, simulations, and visualizations.

This study explores the integration of climate change mitigation and adaptation within Urban Digital Twins. The research identifies factors that are often overlooked in current implementations and provides guidelines for structuring Urban Digital Twin modules. By analyzing key functionalities and requirements, as well as insights from stakeholder interviews, this work highlights the potential of Urban Digital Twins in fostering holistically climate-proof urban development.

Key findings emphasize the importance of incorporating climate-related variables across Urban Digital Twin functions to support decision-making in urban regeneration and new urban development projects. Despite challenges such as data integration, the proposed guidelines aim to inform future Urban Digital Twin development efforts, enhancing their role in planning and designing more resilient urban areas. The paper presents a case study on the Urban Digital Twin of Helsinki, outlining essential factors and their implications for urban climate-proof decision-making.

How to cite: Ricciardi, G., Callegari, G., and Leone, M. F.: Guidelines for creating a Urban Digital Twin (UDT) module for urban regeneration scenarios that include climate change mitigation (CCM) and adaptation (CCA) to support design and decision-making, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-940, https://doi.org/10.5194/icuc12-940, 2025.

In our presentation we will introduce the research project EDiT-Cities, which has started in 2025. Within EDiT-Cities high-quality climate and air quality information will be linked to an urban digital twin (UDT). Results will be used to support cities and municipalities in developing digital twins for climate-resilient urban development. Speyer, a town with about 50.000 inhabitants and a medieval city center, lying in the one of the warmest regions of Germany, serves as model city.

Numerical simulations with the urban climate model PALM-4U will be performed using Copernicus data and local environmental data as input data. The results will be included in the UDT and can be used for assessing climate adaption and clean air measures in the UDT. Heat load scenarios as well as dispersion calculations for different weather conditions will be conducted. The results also make it possible to assess climate adaptation measures not only in terms of their effects on heat load, but also regarding their impact on air quality. The project aims to strengthen the potential of digital twins in urban planning, traffic-related air pollution control, air quality and health. It will build on existing, freely accessible data and products and develop them further in line with public needs in Germany. Achieved results will be made freely available for further use.

How to cite: Schau-Noppel, H., Prestel Kupferer, I., Kniffka, A., and Eichhorn-Müller, A.: EDiT-Cities: Extending urban digital twins with Copernicus data and model simulations for developing resilient cities, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-664, https://doi.org/10.5194/icuc12-664, 2025.

The urban atmosphere plays a vital role in the complex interactions with other Earth and societal systems. Extremes in urban heat and air pollution affect the population of cities. The interactions of atmospheric variables with human interventions in the urban environment influence the health and well-being of urban dwellers and the liveability of our cities. The complexity of these interactions makes it challenging for existing infrastructures to provide robust evidence to support stakeholders who make these decisions. Thus, bringing together internationally disparate expertise and high-quality research infrastructures in a digital twin tailored to stakeholder needs may facilitate decision making in urban problems involving heat and air quality.

UrbanAIR[1] will include a cascade of atmospheric models, ranging from the global scale, linking via the mesoscale to very high-resolution simulators at the neighbourhood or street level. By starting from the perspective of the decision-maker and fostering co-creation, we will configure the models to generate scenarios that address key dilemmas and support a balanced evaluation of decision criteria. In this presentation, we present our plans for integrating the different simulation and decision-making components. We will pay specific attention to the integration of observations into the simulator and to uncertainty quantification through emerging data assimilation and machine-learning techniques.

The resulting tools will be integrated into the Destination Earth infrastructure[2]. By testing the tools in a variety of real-world settings, the research infrastructure of UrbanAIR will pave the way for effective climate adaptation and hazard mitigation in a more general sense, transforming urban planning and design into a proactive, tool-based, approach for a safer, healthier, and more resilient future.

[1] UrbanAIR is part of HORIZON-INFRA-2024-TECH-01-03: New digital twins for Destination Earth.
[2] https://destination-earth.eu/

How to cite: Vossepoel, F., Pickard, S., van Reeuwijk, M., Samler, M., Theeuwes, N., and Veldeman, N.: UrbanAIR: a digital twin of the urban atmosphere for decision support, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-770, https://doi.org/10.5194/icuc12-770, 2025.

Three-dimensional urban simulation is a powerful tool for informed urban and architectural planning. However, the intensive manual effort required to prepare input 3D city models has hindered its widespread adoption. To mitigate this effort, we present VoxCity, an open-source Python package that provides a one-stop solution for grid-based 3D city model generation and urban simulation for cities worldwide. VoxCity's generation module automatically downloads building heights, tree canopy heights, land cover, and terrain elevation within a specified target area, and voxelizes buildings, trees, land cover, and terrain to generate an integrated voxel city model. The simulation module enables users to conduct environmental simulations, including solar radiation and view index analyses. Users can export the generated models using several file formats compatible with external software, such as ENVI-met (INX), Blender, and Rhino (OBJ). We generated 3D city models for eight global cities, and showcased the calculation of solar irradiance, sky view index, and green view index. We demonstrated microclimate simulation and 3D rendering visualization through ENVI-met and Rhino, respectively, using the file export function. Additionally, we reviewed openly available geospatial data to create guidelines to help users choose appropriate data sources depending on their target areas and purposes. VoxCity can significantly reduce the effort and time required for 3D city model preparation and promote the utilization of urban simulations. This contributes to more informed urban and architectural design that considers environmental impacts, and in turn, fosters sustainable and livable cities. VoxCity is released openly at https://github.com/kunifujiwara/VoxCity.

How to cite: Fujiwara, K., Tsurumi, R., Kiyono, T., Fan, Z., Liang, X., Lei, B., Yap, W., Ito, K., and Biljecki, F.: VoxCity: A One-stop Package for Open Geospatial Data Integration, Grid-Based 3D City Model Generation and Urban Simulation, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-313, https://doi.org/10.5194/icuc12-313, 2025.

Climate change is increasing the number, severity, and frequency of extreme weather events, such as heatwaves, which kill thousands of people worldwide and strain healthcare systems, particularly in cities. Those responsible for managing the societal consequences of these events require easy and uniform access to the best data, models, and decision support tools in order to mitigate impacts on affected populations and enable appropriate responses. To achieve this, spatially and temporally accurate information on the occurrence, location, and duration of heatwaves is required.

A digital replica of the Earth system, a foundational component to a digital twin (DT), can provide such information by producing bespoke cutting-edge numerical simulations of upcoming heatwaves and combining several components of the value chain – from observations to modelling, to answering strategic and socioeconomic questions – in a single workflow. To better understand how heatwaves may affect a city across different neighbourhoods, however, km-resolution DT forecasts must be zoomed in at the metre scale as events unfold. This necessitates taking into account urban complexity, which can be achieved by combining operational weather forecasts with quality-controlled station observations and land-use data. By using such an approach, machine learning (ML) has shown to enhance forecasts in urban areas to much higher resolutions than standard operational forecasts. Yet, the use of ML to forecast downscaling has so far involved offline, city-specific applications with limited scalability and testing in real-world settings.

The US-EU AI for the Public Good partnership seeks to address these limitations. As part of the partnership, ML solutions are being implemented to deliver hyper-local data from the European Commission's Destination Earth (DestinE) initiative and operationally generate forecast maps of heat in terms of thermophysiological stress. This presentation will provide an overview, plans, and current status of the partnership.

How to cite: Di Napoli, C., Siemen, S., Oliveira, A., Kihn, E. A., Rao, D., Webster, J. A. B., Piesiewicz, G., and Pappenberger, F.: Urban complexity and digital twins: leveraging machine learning for hyper-local heat stress forecasting, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-365, https://doi.org/10.5194/icuc12-365, 2025.

The urban climatology community has been structured internationally at different levels for several years with venues such as ICUC, AGU, EGU or IC2UHI which regularly bring together different members of academia addressing urban climate-related research and engineering challenges from different disciplines, countries and perspectives. Late 2018, a series of meetings of the French community were launched. Two in-person meetings occurring in Paris, France in early and late 2019, bringing together 30-40 academics from different French research groups and universities across the country.

During the COVID-19 pandemic and lockdown episodes, networking and research sharing took place in the form of a series of online webinars in 2021 offered a venue for young researchers to present their work to their peers and research community. In-person meetings took place once again starting in 2022. Building on the community’s engagement and interest, the research network began formalizing its existence and organization, currently structured into four thematic groups focused on:

• numerical modeling
• field observations
• knowledge transfer for operational applications
• teaching and public dissemination of scientific and technical information

The community currently comprises around 150 members from 50+ different research institutes, as well as the public (government agencies), private (startups and SMEs) and local government (municipalities, urban planning agencies, …) sectors. These members are involved in work spanning a wide range of disciplines from the technical, natural and human and social sciences and conducted at a variety of spatial scales ranging from urban materials to the regional scale (“nano”, “indoor”, micro, local and meso-scale climates) and focusing on the study of urban climate: from fundamental science to climate change adaptation solutions.

The proposed contribution will present the network, its membership as well as work undertaken by its thematic groups, including a literature review of radiation shields.

How to cite: Hendel, M., Kohler, M., Rodler, A., Morille, B., Merlier, L., Bernard, J., Bouzouidja, R., Hidalgo, J., Bouyer, J., Herpin, S., Gros, A., and Leconte, F.: Climate, Cities and Societies: the French Urban Climatology Network, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1007, https://doi.org/10.5194/icuc12-1007, 2025.

Local Climate Zones (LCZs) provide a standardized classification of urban morphology and climate interactions, yet traditional 2D representations lack spatial depth for comprehensive urban climate analysis. This research proposes an innovative approach that integrates urban remote sensing , AI-driven 3D modeling, and state-of-the-art Digital Twin workflows to enhance LCZ visualization and climate analysis. The methodology involves leveraging multi-spectral datasets (MODIS, Landsat, Sentinel) to derive urban morphological and climatic parameters such as building height, vegetation cover, land surface temperature (LST), and albedo. Next a Text-to-Mesh AI model is used to convert LCZ descriptions into spatially detailed 3D urban morphologies which means textual descriptions of LCZ characteristics are used to procedurally generate 3D urban morphologies, replacing traditional 2D classified patches with immersive, spatially accurate representations. At Last, these AI-generated models are embedded into a georeferenced digital twin framework to evaluate and analyze for its usability and consistency, based on scale generalization, and real-world validation of these 3D models for climate impact analysis. This open-data-driven approach enables improved climate visualization, supporting urban planners and policymakers in climate adaptation strategies. Our attempt is to showcase the enhanced interpretability and usability of 3D LCZ models in urban climate research. The integration of AI, digital twins, and open climate datasets provides a scalable, innovative tool for understanding and mitigating urban climate challenges. By linking remote sensing-derived land cover, thermal, and vegetation indices with AI-generated urban forms, this study enables an interactive data-driven approach to enhance urban climate zone representation. Potential applications include urban heat island mitigation, microclimate simulations, and climate-resilient urban design strategies.

How to cite: Halder, D., Harshit, H., and Garg, R. D.: 3D Local Climate Zone (LCZ) Modeling and Digital Twin Integration for Advanced Climate Resilience using AI, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-954, https://doi.org/10.5194/icuc12-954, 2025.

Informal settlements, where groups of asylum-seekers, refugees, or internally displaced people settle in self-identified, spontaneous locations, often lack basic services and are highly vulnerable to climate-driven risks. Approximately 1 billion people currently live in informal settlements, driving significant research attention to issues such as property rights and governance strategies. However, the lack of large-scale location and boundary data for informal settlements poses a major obstacle to conducting in-depth quantitative analyses. Existing approaches to identifying informal settlements have been constrained to individual cities or districts due to their reliance on costly imagery, field survey, and localized expertise. To address this gap, we develop a novel, global framework to identify permanent informal settlements using satellite imagery and publicly available datasets. First, building footprint and nighttime light datasets were combined to isolate human settlements with low energy availability, effectively narrowing the focus to potential settlement areas and significantly reducing computational costs. Next, leveraging high-resolution Sentinel-2 imagery, we estimated the similarity of each pixel's spectral principal components to those of high-confidence samples, allowing us to distinguish informal settlements from broader human settlement areas. Finally, nearly 10,000 informal settlement points provided by the United Nations were used to refine our results, ensuring that only officially recognized informal settlements were retained. This fast and scalable framework overcomes the data scarcity challenges typically faced in resource-poor regions, where informal settlements are most prevalent. The results of this study provide an unprecedented foundation for deeper quantitative analyses of informal settlement areas, supporting global efforts to achieve the Sustainable Development Goals and address urban vulnerabilities.

How to cite: Jiang, S., Zhao, L., and Li, X.: Global Mapping of Informal Settlements using Satellite Imagery and Open Datasets, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-353, https://doi.org/10.5194/icuc12-353, 2025.

Thermal evaluation of buildings is essential to improving energy efficiency and mitigating the environmental impact of the construction sector. However, conventional methodologies face significant limitations, as they cannot integrate thermal and RGB data into a single 3D model, hindering the accurate identification of anomalies such as thermal bridges, heat losses, or areas with poor insulation. These challenges are further complicated by difficulties in aligning thermal and RGB images due to differences in optical properties and sensor resolutions, as well as the complexity of processing large datasets. This work proposes an innovative methodology to generate integrated 3D models combining thermal and RGB data, based on UAS-captured imagery using thermal cameras and RGB sensors, enabling the creation of cohesive 3D models that incorporate both the building’s geometry and its thermal characteristics. Using these models, statistical analysis of temperature distribution is conducted to identify thermal anomalies. Significant deviations are categorized into two main types: linear anomalies (e.g., thermal bridges) and surface anomalies (e.g., areas of heat loss), using techniques like Principal Component Analysis (PCA). This approach enables the semi-automatic detection of critical areas, optimizing the evaluation of thermal performance. The proposed methodology has been validated in a real-world case study, successfully detecting thermal anomalies with high precision and classifying their energy impact. The results were incorporated into digital twins that represent not only the geometry of the building but also its thermal behaviour, providing a powerful tool for energy audits and rehabilitation strategies. This approach represents a significant step forward in thermal inspection, promoting innovative solutions to optimize building energy performance and fostering more sustainable and resilient urban environments.

How to cite: Burgos Bayo, R., Santamaría Maestro, R., Sánchez Aparicio, L. J., Sánchez-Guevara Sánchez, C., and Arranz Arranz, B.: 3D Models for Thermal Anomaly Detection in Building Envelopes, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-373, https://doi.org/10.5194/icuc12-373, 2025.

Various operations and activities in the city municipal departments are weather and climate data sensitive. However, there is limited understanding and documentation of how and what datasets and products are required or will be useful for the various operations on the daily, weekly, monthly, seasonal, annual, and longer timescales. As part of the UT-City Climate CoLab in Austin, Texas, there has been a concerted effort to tie in city decisions and weather and climate data needs. This is becoming important as communities are experiencing more weather-extremes and understand the need for reliable, tailored climate information for their specific departmental needs. A signature project undertaken by the CoLab relates to creation of a Climate Data Decision Calendar (C2D2) that is creating a formal framework for the climate data and decision pairing. This presentation will share the background and activities underway related to the decision calendar. An example of work underway with the Austin Fire Department Wildfire Division in developing one such Climate Decision Calendar across the city of Austin, central Texas region will be presented. The operations are intense during the fire season, but are also actively underway year round and require a wide range of data at different temporal interval. The process undertaken and the example of the calendar under development will be presented.  Additional interactions with other departments such as Austin Parks, Austin Public Works, Austin Water, and Austin Energy are also underway and will be discussed. Idea is that for each of these departments and activities a unique Climate Decision Calendar will be developed based off their specific needs and operations, and this information collectively will assist in helping broaden the understanding of climate data needs, customizing data products, and helping local decision making and planning.

How to cite: Dallmann, A., Davis, B., Kim, M., Leclercq, A., Coudert, M., Baumer, Z., Flores-Gonzalez, M., Bixler, P., Jiao, J., Neupane, B., and Niyogi, D.: City Climate Data Decision Calendar (C2D2): Framework and Initial Results for Austin, Texas, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1137, https://doi.org/10.5194/icuc12-1137, 2025.