Urban environments face growing challenges from hazardous weather events and deteriorating air quality conditions, including extreme heat waves, severe storms, and elevated levels of fine particulate matter (PM2.5). These hazards pose serious risks to public health, infrastructure, and overall urban resilience. To address these challenges, we have developed a high-resolution early warning system tailored for the Chicago region, leveraging the fully coupled urbanized WRF-Chem (uWRF-Chem) model. This system provides 48-hour forecasts of key meteorological variables along with air pollutant concentrations, including PM and carbon monoxide (CO), at an unprecedented 100-meter resolution. Urban land-use representation is enhanced through a newly developed CGLC–MODIS–LCZ hybrid dataset, while the Building Effect Parameterization (BEP) scheme is employed to account for urban morphology and its impact on atmospheric processes. The system was operationally tested and evaluated from August 13 to September 13, 2024, successfully capturing a late-August heat wave, demonstrating its capability to predict extreme weather and air quality conditions in complex urban settings. These forecasts offer a valuable tool for public health advisories, emergency response, and urban planning. Additionally, we are developing dynamic vulnerability metrics to enhance early warnings, with initial results highlighting key exposure risk metrics. These metrics aim to provide a more comprehensive understanding of how different populations and urban systems are affected by extreme weather and air quality events. 

How to cite: Sharma, A., Li, P., Wu, S., and Kumar, R.: Early Warning System for the Chicago Region and Developing Dynamic Vulnerability Metrics, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-489, https://doi.org/10.5194/icuc12-489, 2025.

Climate change is intensifying extreme weather events, posing increasing risks to communities worldwide. In response, the United Nations has called for comprehensive Early Warning Systems for All (EW4All) by 2027. This presentation introduces PREPARE—a Personalized Risk and Exposure Prediction for Adaptation and Resilience system—designed to deliver tailored early warnings for climate-related hazards, enabling individuals and communities to take proactive protective measures.

The development of PREPARE builds upon insights from the Personalized Real-time Air Quality Information System for Exposure for Hong Kong (PRAISE-HK), which provides ultra-high-resolution air quality forecasts and actionable health recommendations. A core strength of PRAISE-HK is its user-centric design, which integrates feedback from vulnerable populations to enhance accessibility and impact. Similarly, PREPARE leverages high-resolution climate predictions, AI-driven risk assessments, and real-time exposure analytics to provide personalized alerts for extreme weather events such as floods, heatwaves, and storms.

Beyond advanced forecasting, PREPARE emphasizes effective risk communication and community engagement. By collaborating with local NGOs, healthcare providers, and community organizations, the system addresses the last-mile challenge in warning dissemination, ensuring that critical information reaches those most at risk in a timely and actionable manner.

This presentation highlights how PREPARE integrates cutting-edge environmental forecasting, AI, and participatory design to create a more responsive, inclusive, and effective early warning system for climate resilience.

How to cite: Lau, A. K. H.: Personalized Risk and Exposure Prediction for Adaptation and Resilience, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-452, https://doi.org/10.5194/icuc12-452, 2025.

Air pollution in Eastern and Southern Africa (E&SA) is a severe public health issue, causing over 23,000 premature deaths annually and widespread respiratory problems. To mitigate this problem, we are developing a regional air quality forecasting system system that provides a 48 hour air quality forecast over E&SA under a NASA funded SERVIR project. To help the stakeholders understand past trends in air quality,  we have also developed a regional air quality atlas based on the Copernicus Atmosphere Monitoring System. This project is being carried out in collaboration with local stakeholders. This system is being intentionally designed to address specific regional needs, priorities, and environmental challenges, and can aid informed decision-making and regional planning in response to local mandates. Our regional air quality forecasting system is based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) employed at 15 km grid spacing. Aerosol optical depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite are assimilated daily using the community Gridpoint Statistical Interpolation (GSI) framework to improve aerosol initialization. Meteorological data from the Global Forecast System (GFS), anthropogenic emissions based on CAMS annual estimates, and real-time fire emissions from the Fire Inventory from NCAR (FINN) are used in the forecast. Initial simulations, focusing on June 2022, capture major regional pollution events, including wildfires, dust storms, and local anthropogenic emissions. Challenges such as validation in data-sparse regions and high cloud coverage in tropical areas, which complicates data assimilation, will be addressed. This system has been installed for operational air quality forecasting at Meteo Rwanda and has been running operationally since Oct 2024. This talk will describe the development of this system in detail. 

How to cite: Kumar, R., Shams, S., Weeks, V., Bruintjes, R., Drews, C., and Lacey, F.: Development of an air quality early warning system for eastern and southern Africa, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-554, https://doi.org/10.5194/icuc12-554, 2025.

Urban air pollution is a growing public health challenge, with meteorology playing a crucial role in determining pollutant dispersion. In India, stagnant atmospheric conditions—characterized by weak winds and a shallow planetary boundary layer (PBL)—intensify pollution episodes, increasing health risks. The Ventilation Coefficient (VC), defined as the product of PBL height and mean wind speed within the PBL, serves as a key indicator of the atmosphere’s ability to disperse pollutants. This study examines the spatio-temporal variability of VC in Ahmedabad, India, using high-resolution Weather Research and Forecasting (WRF) model simulations for two contrasting meteorological periods: winter (13–19 January 2024) and summer (18–24 May 2024). By analysing these extreme regimes, the study provides insights into how seasonal and diurnal variations in VC impact the city’s air quality dynamics.

Results indicate a significant seasonal contrast in VC, with values ranging from 185 to 30,268 m²/s in winter and 322 to 44,316 m²/s in summer. The average VC during the 6.30 pm to 8.30 am (nighttime) during winter ranges between 899 – 2390 m²/s while that in summer ranges between 2678 – 4216 m²/s while that during 9.30 am – 5.30 pm (daytime) it varies between 12951 – 22782 m²/s in winter and 8,455 – 12,364 m²/s in summer. The observed variations highlight that atmospheric ventilation capacity fluctuates not only seasonally but also on hourly and daily timescales, influencing pollution dispersion patterns.

The findings underscore the potential of VC as a guiding parameter for air quality management, particularly in scheduling industrial emissions and optimizing mitigation strategies. A real-time forecast of VC could strengthen air pollution action plans by helping regulators anticipate periods of poor dispersion and implement timely interventions. By integrating VC into urban air quality policies, cities like Ahmedabad can develop more adaptive and effective approaches to reducing pollution exposure and safeguarding public health.

How to cite: Patel, V., Kandya, A., Kela, S., Thaker, D. M., Pandya, J., and  Mehta, P.: Assessing the spatio-temporal dynamics of Ventilation Coefficient for Air Quality Management: A study of Ahmedabad city, India using WRF model, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-752, https://doi.org/10.5194/icuc12-752, 2025.

Integration of weather and climate forecasting is currently the frontier of numerical modeling development in China, and dynamic downscaling allows for improving the performance and resolution of global climate models to the weather scale. Focusing on the “23.7” extreme rainstorm (July 29, 00:00 - August 2, 00:00 UTC) in the Beijing-Tianjin-Hebei region (BTH), this study assesses predictions from the China Meteorological Administration Climate Prediction System version 3 (CMA-CPSv3, 45 km resolution) and 9-km dynamic downscaling hindcasts from the Weather Research and Forecasting model (WRF-9km). Unlike traditional climate anomalies approaches, direct outputs are used for evaluation, similar to weather forecasting tests. By examining, both the CMA-CPSv3 forecasts and the WRF-9km hindcasts offer a 5-day prediction window for this rainstorm. They successfully predict the rainstorms and related atmospheric circulations from July 24th onward, aligning with observed and reanalyzed data. WRF-9km, with the higher resolution and optimized physical processes, outperforms CMA-CPSv3, particularly in precipitation spatial distribution and center intensity. The WRF-9km 7/24 hindcast exhibits the most significant enhancement compared to the corresponding CMA-CPSv3 forecast. This improvement is notably reflected in the substantial increase in spatial correlation, rising from 0.68 to 0.79, as well as a reduction in the difference of center values, decreasing from -51% to -20%. Furthermore, the WRF-9km 7/24 hindcast also improves the Critical Success Index by 0.08, the Success Rate by 0.08, and the Probability of Detection by 0.29 for heavy rainfall (over 25.0 mm/d). However, improvements in large-scale circulations with WRF-9km are limited, which may restrict advancements in predictability. In conclusion, the WRF-9km can enhance the performance and resolution of CMA-CPSv3 predictions, which can serve as one route for CMA-CPSv3 to achieve weather-climate integration.

How to cite: Yang, J., Zhai, P., Wu, T., Yan, J., Zhang, G., Pei, L., and Miao, S.: Evaluation of High-resolution Downscaling Predictions for the July 2023 Extreme Rainstorm in the Beijing-Tianjin-Hebei Region Based on CMA-CPSv3, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-337, https://doi.org/10.5194/icuc12-337, 2025.

Twenty-seven CMIP6 models were grouped into three ensembles based on the simulated performance of heatwaves in North China during present-day (1995-2014), and future changes in the duration and intensity of heatwaves were projected under SSP1-2.6, SSP2-4.5 and SSP5-8.5. The selected three ensembles showed consistent projections: both the duration and intensity of heatwaves would increase significantly, with the greatest under SSP5-8.5. Besides, the heatwave growth in 2081-2100 would double in 2041-2060, except for SSP1-2.6, where heatwaves would be similar in both periods. For the spatial distribution, the duration (intensity) would increase more in southern (western) parts of North China. Combining heatwaves with population and GDP, future heat exposures would concentrate on urban areas and the tertiary industry. For example, the 2041-2060 population exposure would reach 3.2-5.6 times the current level, with contributions from the urban population ranging from 55-60%. The GDP exposure would hit tens to hundreds of times the current level, with the tertiary sector replacing the secondary sector as the leading industry in North China, producing the major contribution and facing significant heat-related risks. Overall, there will be significant heat-related impacts under SSP5-8.5, about 1.5-3.0 fold of those under SSP1-2.6 and SSP2-4.5. The urban and tertiary sectors would suffer greater risks relative to the rural and other industries. Our results reinforced the need to minimize global emissions and develop strategic plans to mitigate heat impacts under high-emission scenarios, especially for urban areas and the tertiary industry, requiring great attention to climate adaptation.

How to cite: Zhang, G.: Increasing heatwave with associated population and GDP exposure in North China, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-293, https://doi.org/10.5194/icuc12-293, 2025.

Heatwaves pose an escalating threat to urban populations, particularly in rapidly expanding cities like Ahmedabad having population nearing to 10 million, where rising temperatures and urban heat island effects intensify heat stress. In May 2010, an unprecedented heatwave led to approximately 1,344 fatalities within a matter of days, underscoring the lethal consequences of extreme heat events. As climate change continues to drive up global temperatures, evaluating the recurrence, severity, and spatial variability of such extreme events is crucial for urban resilience and disaster preparedness.

This study assesses the current heatwave hazard in Ahmedabad by comparing the ambient temperature and heat index of May 2010 with those of May 2024. We employed the Weather Research and Forecasting (WRF) model at a high spatial resolution of 1 km × 1 km to simulate hourly ambient temperatures for both time periods. We validated the simulated temperature and relative humidity for May 2024 against field observations from three monitoring locations. The model demonstrated excellent agreement with observational data, yielding a Pearson correlation coefficient (r) of 0.96 for T-2 (ambient temperature at 2 m height) and 0.85 for relative humidity (RH), confirming the high reliability of our simulation approach.

Using NOAA guidelines, we calculated the heat index at an hourly scale for each grid point across Ahmedabad, integrating both T-2 and RH to assess the intensity of heat stress. By mapping the spatial distribution and occurrence frequency of heat index ranges, we examined whether the thermal conditions of May 2024 exceeded the deadly thresholds recorded in May 2010 or exhibited signs of mitigation. Our comparative analysis highlights critical trends in the progression of heat stress and exposure patterns within the city.

As heatwaves become more frequent and intense due to global climate change, such assessments are indispensable to enhance a city’s resilience against future heatwave crises.

How to cite: Kandya, A., Patel, V., Kela, S., Shama, A., and Chen, F.: Heatwave Hazard in Ahmedabad, India: How Close Are We to Another May 2010 Crisis?, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1008, https://doi.org/10.5194/icuc12-1008, 2025.

Temporally compound heatwaves (CHWs), two consecutive heatwaves (HWs) with an intermittent cool break between them, are projected to occur more frequently under a warming globe. However, their spatiotemporal characteristics and interaction with urban heat island (UHI) are unexplored at the continental scale. Using observational data from over 2000 ground-based stations over China, we find that CHWs constitute an increasing portion of HW hazard from 1961 to 2017. The increasing trend is especially evident when using the daily minimum temperature to define hot days, suggesting an aggravated thermal environment at night. The urban-rural contrast of CHW trends illustrates that urbanization contributes substantially to the increased frequency of CHWs in cities, especially in southern China. Results show that mean UHI intensity (UHII) tends to weaken under HW and CHW conditions, which correlates with the increased pressure and reduced precipitation. During CHW events, UHII reduces during cool break due to enhanced evaporative cooling in urban areas under precipitation. The interaction between UHI and HW is subject to change with background climate, which is positive for dry regions and negative for wet regions. This study provides insights into CHW evolution over mainland China and demonstrates the need for heat mitigation strategies under climate change.

How to cite: Zhang, L.: Temporally Compound Heatwave and Its Interaction with Urban Heat Island over Mainland China, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-54, https://doi.org/10.5194/icuc12-54, 2025.

Effective warning systems have consistently demonstrated their capacity to save lives and are among the most cost-efficient methods of climate adaptation. They enable early actions by allowing communities to prepare for and respond to various hazards, thereby significantly reducing disaster-related deaths and economic losses. UN Secretary-General António Guterres launched the “Early Warnings for All” initiative in 2022. This initiative calls on the international community to prioritize the development and implementation of early warning systems as a cornerstone of climate resilience strategies. The goal is ambitious yet essential: to ensure that every individual on the planet is protected by early warning systems within the next five years. World Meteorological Organization is tasked to co-lead this UN effort.

Urban population, now more than half of the world’s population, are increasingly exposed to climate-related hazards such as thermal stress, air pollution, heavy rainfall, flooding, storm surges, and compound hazards. Urban areas are especially vulnerable due to their high population densities, complex infrastructures, and socio-economic factors. In 2024, the WMO World Weather Research Program (WWRP) launched the Urban Prediction Project. This project is designed to improve urban-scale prediction and warning systems, thereby reducing exposure to weather-related risks in cities. This initiative represents a critical step toward building more resilient and sustainable urban environments. In this presentation, we will discuss the scientific and societal challenges of developing ultra-high-resolution urban hazard prediction and warning systems, with an emphasis on interdisciplinary approach to integrating physical and social sciences and AI technology to enhance urban multi-hazard prediction and warning systems.

How to cite: Chen, F.: Integrated Research in Advancing Urban Multi-Hazard Prediction and Warning Systems, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-876, https://doi.org/10.5194/icuc12-876, 2025.

Adhering to the long-term goal of improving the accuracy of 0-24 hour fine-scale numerical weather forecasting for urban areas, we developed the Rapid-update Multi-scale Analysis and Prediction System (RMAPS) that coupled physical and chemical processes such as urban land surface and aerosols, achieving seamless analysis and prediction within 0-24 hours, 10-minute updates, and 10-meter grid spacing objective analysis and prediction. RMAPS model system includes urban short-term numerical weather forecasting (ST), environmental meteorological forecasting, integrated forecasting, large eddy simulation, and urban micro-scale forecasting (URBAN), etc. RMAPS-ST with horizontal grid spacing of 333-meter are developed, including data assimilation of dense observations and multi-layer urban canopy model. RMAPS-URBAN with horizontal grid spacing of 10-meter includes wind field rapid diagnosis model, hydro-thermal processes, human comfort model, and pollution diffusion model. Rainstorm disaster risk assessment and early warning model has been developed to realize real-time risk forecasting. A comprehensive index for meteorological and environmental impacts on health is proposed. RMAPS model outputs are used to drive urban multi-hazard prediction and warning, including heatwave, waterlogging, air quality, health, etc. RMAPS model system has provided very good meteorological support for urban management and many national major events such as Beijing 2022 Winter Olympics. Recent developments and open challenges will be discussed.

How to cite: Miao, S. and the coauthors: Development of RMAPS model system for urban multi-hazard prediction and warning, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-445, https://doi.org/10.5194/icuc12-445, 2025.

The Paris 2024 Olympics Research Demonstration Project, endorsed by the World Weather Research Programme of WMO, aimed to make progress on future fine-scale weather forecasting systems in cities. The project brought together more than 20 national meteorological centres and laboratories from 10 countries.

During the Olympic and Paralympic summer, simulations from 7 hectometric numerical models were conducted daily by the partners. Comparisons were made with weather stations inside and outside the city. The results demonstrated the capacity of the models to reproduce urban effects, but also revealed large variability among models, particularly regarding the extent of the urban heat plume at night. This opens up new scientific questions, which are explored in depth using the crowdsourced netatmo observations, in order to assess the role of the various physical processes in play, such as the competition between hot air advection and local cooling in the suburbs.

A decision-making procedure was also established regarding whether or not to hold the Paris 2024 “Marathon for All” in hot weather situations. Throughout the summer of 2022, 100m MesoNH model simulations were conducted over Paris and its inner suburbs, extending to Versailles. Analysis of these simulations by expert forecasters from Météo-France Sports led to proposed scenarios assessing the heat stress conditions runners would face along the marathon route, based on their running speeds. The Paris 2024 marathon organisers were able to take this meteorological information into account when planning the event. A 100m MesoNH simulation was used specifically on the day of the Marathon for All to refine the forecasts of race conditions, and to adapt the safety and assistance arrangements for runners as best possible. This study shows the value of 100-m resolution models for targeted forecasting applications.

How to cite: Masson, V., Wurtz, J., Abeillé, P., Dumas, G., Lemonsu, A., Nagel, T., De Munck, C., Garrouste, O., Blunn, L., Hanley, K., Suri, D., Lean, H., Shin, H., Franklin, C., Kumar, V., Schulz, J.-P., Ulbrich, S., Lauer, A., Leroyer, S., and De Coning, E.: Paris 2024 Olympics Project results on urban heat modelling: intercomparison and application to the marathon event, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-344, https://doi.org/10.5194/icuc12-344, 2025.

Addressing the myriad of short to long-term impacts of climate change is a complex task requiring the integration of diverse geographical and social contexts. Intersectional approaches have proven effective in uncovering the nuanced experiences of diverse social groups (e.g., individuals of different religions, abilities, genders, and races), informing targeted interventions that meet communities' needs, and building collective, empirically grounded responses. Here, we investigated the barriers to disseminating and communicating early warning messages at the local level, focusing on communities in coastal and urban areas. Our research covered two traditional communities in Brazil’s southeastern coastal zone, Ubatumirim and Campinho, and an informal settlement, Jardim Colombo, part of the Paraisópolis Complex in São Paulo. By integrating demographic data, georeferenced disaster information, survey data, and qualitative data from focus groups, workshops, and interviews, we identify critical gaps in risk communication across these three case studies. We find that: a) Information about potentially hazardous events is often delayed, with updates reaching the public only after the event has occurred. b) Communication networks rely on technologies that frequently fail during emergencies, rendering them unreliable. c) A lack of distinction between official warnings and misinformation creates confusion and erodes trust. Our results also highlight the compounded challenges faced by school-age children and youth, individuals with disabilities, old persons, and those in geographically isolated areas. Additionally, the study acknowledges the increased burden on women, who often assume caregiving responsibilities for these vulnerable groups while at the same time navigating their own risks. Our findings call for a paradigm shift in early warning systems – towards more comprehensive and inclusive risk communication strategies capable of reaching risk populations before a hazard strikes. 

How to cite: Pereira, R. D., Ribeiro, F. N. D., Iwama, A. Y., Londe, L. R., Sato, D. P., de Brito, M. M., Holloway, P., and Tavares P., C.: Strengthening inclusive disaster preparedness in Brazil: an intersectional view of risk communication in informal settlements and traditional communities, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-783, https://doi.org/10.5194/icuc12-783, 2025.

Bihar, a densely populated province in the eastern part of India, is highly vulnerable to various hydro-meteorological hazards with the highest intensity. The region has been subjected to Flash Floods, Droughts, thunderstorms, Lightning, heat waves, cold waves etc during successive years causing immense damage to life and property.

The recurrence of these disasters is diminishing the gain in development and also disrupting the development momentum as scarce resources are diverted towards relief and reconstruction activity. Thus, a robust early warning system is crucial for disaster risk reduction, community resilience and sustainable development.

The Bihar Mausam Sewa Kendra (Bihar Weather Service Centre) has operationalised an Integrated Multi-Hazard Early Warning System (IMHEWS) aligned to the Hazrd Calender developed using the historical data (last 30 years) on extreme weather conditions in the region. The IMHEWS comprises a dense network of field sensors, a robust weather database, a state-of-the-art weather forecasting system, and an ICT-enabled information dissemination system with last-mile connectivity.

A key highpoints IMHEWS are:

• Provide area and event-specific early warning with sufficient lead-time.
• The early warnings and advisories are provided in the idiom and frequency that a common man can comprehend and use.
• Interactive helpdesk, allows end users to obtain information and advisory tailored to their area of interest and related to their activity.
• End-users can seek additional information, and clarification, if necessary.

The BMSK IMHEWS assists users in making informed decisions towards transportation, agriculture, flood preparedness, construction, and other routine sector-specific activities.

How to cite: Prabhu, C.: An integrated Multi-Hazard Early Warning System for achieving Sustainable Development in Bihar, India, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1086, https://doi.org/10.5194/icuc12-1086, 2025.

Urban Heat Islands (UHI) are a significant hazard in cities, caused by low albedo surfaces, limited green space, narrow streets, and tall buildings that trap heat. This effect intensifies heatwaves and poses health risks, especially for vulnerable populations. Various measures, including increasing green space, have been proposed to mitigate UHI impacts. However, space constraints and poor implementation have limited the effectiveness of these solutions.

A study is being conducted to determine the critical factors to be considered for gaining the optimal benefit of green space and mitigating the impact of UHI. The optimal park configuration for UHI mitigation—whether several small parks distributed across the city or a single large space developed strategically provides more cooling. Further, the temperature gradients as a function of distance from the park core are examined along with the interplay between vegetation density, vegetation canopy, surface albedo, and evapotranspiration rates, etc., through WRF simulations integrated with the Hybrid 100m Land Cover Dataset (CGLC-MODIS-LCZ) and the LANDSAT-8 data analysis.

Two cities with distinct climates and urban morphology are considered for evaluating the consistency in model performance, validating the output with ground-based observations, and assessing the adaptive capacity, transferability, and scalability of the methodology. Patna (eastern India), with a humid subtropical climate, and Bengaluru (southern India), with a tropical savanna climate, are considered for the study.

The analysis shows that Patna City, Didarganj, Bariya Bus Station, Patna Airport, Danapur Industrial area, and the New Secretariat are identified as UHIs characterized by limited greenery and low surface reflectivity. These UHIs exhibit temperatures up to 3.5°C higher when compared to areas with more green cover in the city. WRF simulations show that the strategic introduction of green cover could lower temperatures up to 5°C in these hotspots.

How to cite: Mishra, A., Prabhu, C., Avinash, S., Neethu, S., Bharath, A., and Kumar, R.: Thermal Remote Sensing and WRF simulations-strategy for mitigating the Urban Heat Island effect in Patna and Bangalore, India, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-1090, https://doi.org/10.5194/icuc12-1090, 2025.