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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. 

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. 

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. 

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. 

The IPCC’s Special Report on Climate Change and Cities shows how cities must adapt to climate risks. Urban planners need to create solutions that fit each city’s needs, enhancing urban adaptability and resilience in the context of increasing climate-related risks. Sustainable urban planning, increased citizen awareness, and resilient infrastructure design are crucial in mitigating the growing impacts of climate change on human settlements. Addressing these challenges requires the integration of perspectives from diverse disciplines, including the natural sciences, social sciences, and engineering fields. This article draws on insights from a collaborative effort among experts in these areas, promoting a more coordinated and interdisciplinary approach. By bridging this expertise, we aim to advance resilience practices and awareness, fostering effective urban climate solutions in Texas and beyond. 

Abstract Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities. 

Abstract The impact of climate extremes upon human settlements is expected to accelerate. There are distinct global trends for a continued rise in urban dwellers and associated infrastructure. This growth is occurring amidst the increasing risk of extreme heat, rainfall, and flooding. Therefore, it is critical that the urban development and architectural communities recognize climate impacts are expected to be experienced globally, but the cities and urban regions they help create are far more vulnerable to these extremes than nonurban regions. Designing resilient human settlements responding to climate change needs an integrated framework. The critical elements at play are climate extremes, economic growth, human mobility, and livability. Heightened public awareness of extreme weather crises and demands for a more moral climate landscape has promoted the discussion of urban climate change ethics. With the growing urgency for considering environmental justice, we need to consider a transparent, data-driven geospatial design approach that strives to balance environmental justice, climate, and economic development needs. Communities can greatly manage their vulnerabilities under climate extremes and enhance their resilience through appropriate design and planning towards long-term stability. A holistic picture of urban climate science is thus needed to be adopted by urban designers and planners as a principle to guide urban development strategy and environmental regulation in the context of a growingly interdependent world.