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1. Impact of urban parameterization and integration of WUDAPT on the severe convection

   https://doi.org/10.1007/s43762-022-00071-w  

   Nadimpalli, Raghu; Patel, Pratiman; Mohanty, U. C.; Attri, S. D.; Niyogi, Dev (December 2022, Computational Urban Science)

   Abstract Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction. 

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2. Improving the local climate zone classification with building height, imperviousness, and machine learning for urban models

   https://doi.org/10.1007/s43762-022-00046-x  

   Fung, Kwun Yip; Yang, Zong-Liang; Niyogi, Dev (December 2022, Computational Urban Science)

   Abstract The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output’s spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the low-rise classes. The single-pass forward permutation test reveals that both auxiliary datasets dominate the classification accuracy in the RF classifier, while near-infrared and thermal infrared are the dominating features in the CNN classifier. These findings show that the conventional LCZ classification framework used in the World Urban Database and Access Portal Tools (WUDAPT) can be improved by adopting building height and imperviousness information. This framework can be easily applied to different cities to generate LCZ maps for urban models. 

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3. A New Methodology to Assess Building Integrated Roof Top Photovoltaic Installations at City Scales: The Tropical Coastal City Case

   https://doi.org/10.1115/1.4045347  

   Pokhrel, Rabindra; Walker, Andy; González, Jorge E. (February 2020, ASME Journal of Engineering for Sustainable Buildings and Cities)

   Abstract As a consequence of the warm and humid climate of tropical coastal regions, there is high energy demand year-round due to air conditioning to maintain indoor comfort levels. Past and current practices are focused on mitigating peak cooling demands by improving heat balances by using efficient building envelope technologies, passive systems, and demand side management strategies. In this study, we explore city-scale solar photovoltaic (PV) planning integrating information on climate, building parameters and energy models, and electrical system performance, with added benefits for the tropical coastal city of San Juan, Puerto Rico. Energy balance on normal roof, flush-mounted PV roof, and tilted PV roof are used to determine PV power generation, air, and roof surface temperatures. To scale up the application to the whole city, we use the urbanized version of the Weather Research and Forecast (WRF) model with the building effect parameterization (BEP) and the building energy model (BEM). The city topology is represented by the World Urban Database Access Portal Tool (WUDAPT), local climate zones (LCZs) for urban landscapes. The modeled peak roof temperature is maximum for normal roof conditions and minimum when inclined PV is installed on a roof. These trends are followed by the building air conditioning (AC) demand from urbanized WRF, maximum for normal roof and minimum for inclined roof-mounted PV. The net result is a reduced daytime Urban Heat Island (UHI) for horizontal and inclined PV roof and increased nighttime UHI for the horizontal PV roof as compared with the normal roof. The ratio between coincident AC demand and PV production for the entire metropolitan region is further analyzed reaching 20% for compact low rise and open low rise buildings due to adequate roof area but reaches almost 100% for compact high rise and compact midrise buildings class, respectively. 

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4. Detailed height mapping of trees and buildings (HiTAB) in Chicago and its implications to urban climate studies

   https://doi.org/10.1088/1748-9326/ad661a  

   Li, Peiyuan; Sharma, Ashish (August 2024, Environmental Research Letters)

   Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped theheight oftreesandbuildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments. 

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5. Reconceptualizing urban flood modeling: Toward intersectional and equitable urban flood risk planning

   https://doi.org/10.1016/j.scs.2025.106578  

   Scolio, Madeline; Amur, Achira; Olson, Emma; Smith, Virginia; Wadzuk, Bridget; Homet, Kate; Devlin, Eric; Kremer, Peleg (July 2025, Sustainable Cities and Society)

   Urban flooding, fueled by climate change and rapid urbanization, presents significant challenges for cities around the world. In the United States, this is of particular concern as we see older cities reaching their maximum development density, and newer cities developing to the edge of their boundaries. The dynamic nature of cities and the people that live in them complicate urban flood risk modeling. This paper highlights the need to reconceptualize urban flooding from a spatially and temporally intersectional perspective by analyzing the patterns of socio-economic and bio-physical data across eight US cities to illustrate how spatial flood risk is driven by place-specific factors. Here, we demonstrate the need for a holistic understanding of flood risk, which acknowledges both the deep histories and uncertain futures specific to each city to promote urban flood resilience and environmental justice. Legacies of racialized development continue to influence the spatial heterogeneity of urban flood risk. Thus, centering the ways past injustice has shaped the environment is critical to highlighting inequities in who and where is at increased risk of flooding. The varying impacts of climate change on flooding in different cities, as well as the actions city governments have taken in response to flood events, inform risk and should be included in modeling efforts. There are many challenges in incorporating new temporal dynamics into flood risk modeling, such as data availability, creating a necessity for a greater understanding of flood impact. This is required not only to fully comprehend the impacts of flooding but also to identify appropriate, necessary, and community-sensitive flood interventions as well as to optimize the impact of adaptive measures. Considering historical and future drivers of risk, intersectional flood risk models are required to promote more equitable and effective resilience efforts. This approach will allow urban flood planners and engineers to gain a deeper understanding of how to promote climate resilience while overcoming the reinforcement of discriminatory development and management patterns. 

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