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Abstract Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities. 

Abstract This paper discusses the development and application of a digital twin (DT) for urban resilience, focusing on an integrated platform for real-time fire and smoke. The proposed platform, FireCom, adapts DT concepts for the unique challenges of urban fire management, which differ significantly from regional wildfire systems. Through an exploratory case study in Austin, Texas, in the United States, this research bridges the theoretical foundations of 3D DT with their practical application in fire and smoke management. By fusing diverse data sources, ranging from air quality sensors and meteorological data to 3D urban infrastructure, FireCom supports both emergency response and public awareness through a publicly accessible dashboard. Unlike platforms developed primarily for wildland fire applications, FireCom is specifically designed to account for urban complexities such as building canyon effects on smoke dispersion and the heightened exposure risks associated with dense populations. This study contributes a scalable, replicable framework for municipalities seeking data-driven tools for proactive disaster management, with implications for broader climate resilience planning in urban areas. 

Abstract Due to their importance in weather and climate assessments, there is significant interest to represent cities in numerical prediction models. However, getting high resolution multi-faceted data about a city has been a challenge. Further, even when the data were available the integration into a model is even more of a challenge due to the parametric needs, and the data volumes. Further, even if this is achieved, the cities themselves continually evolve rendering the data obsolete, thus necessitating a fast and repeatable data capture mechanism. We have shown that by using AI/graphics community advances we can create a seamless opportunity for high resolution models. Instead of assuming every physical and behavioral detail is sensed, a generative and procedural approach seeks to computationally infer a fully detailed 3D fit-for-purpose model of an urban space. We present a perspective building on recent success results of this generative approach applied to urban design and planning at different scales, for different components of the urban landscape, and related applications. The opportunities now possible with such a generative model for urban modeling open a wide range of opportunities as this becomes mainstream. 

The rapidly intensifying effects of climate change on urban settlements demand that cities move to the forefront of resilience planning. Climate extremes, from heatwaves to flooding, are increasingly testing the adaptability limits of urban systems and the vulnerability of their populations. Recognizing the unique position of cities, the IPCC’s seventh assessment cycle has prioritized urban areas in its upcoming Special Report on Climate Change and Cities. The IPCC report underscores the potential of cities to act as agents of climate adaptation and provides a framework for cities to build climate-resilient systems. Cities are positioned to pioneer practical, integrative solutions that blend climate sciences with urban planning, establishing frameworks that align economic growth, health equity, environmental sustainability, social justice, and effective governance. This opinion piece explores how cities, by positioning themselves as hubs for innovation, policy reform, and community collaboration, can transform climate vulnerabilities into opportunities for community resilience and sustainability, especially by becoming more-than-human cities, setting examples on the global stage. 

Urbanization has accelerated dramatically across the world over the past decades. Urban influence on surface temperatures is now being considered as a correction term in climatological datasets. Although prior research has investigated urban influences on precipitation for specific cities or selected thunderstorm cases, a comprehensive examination of urban precipitation anomalies on a global scale remains limited. This research is a global analysis of urban precipitation anomalies for over one thousand cities worldwide. We find that more than 60% of the global cities and their downwind regions are receiving more precipitation than the surrounding rural areas. Moreover, the magnitude of these urban wet islands has nearly doubled in the past 20 y. Urban precipitation anomalies exhibit variations across different continents and climates, with cities in Africa, for example, exhibiting the largest urban annual and extreme precipitation anomalies. Cities are more prone to substantial urban precipitation anomalies under warm and humid climates compared to cold and dry climates. Cities with larger populations, pronounced urban heat island effects, and higher aerosol loads also show noticeable precipitation enhancements. This research maps global urban rainfall hotspots, establishing a foundation for the consideration of urban rainfall corrections in climatology datasets. This advancement holds promise for projecting extreme precipitation and fostering the development of more resilient cities in the future.