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1. Deep learning-based urban morphology for city-scale environmental modeling

   https://doi.org/10.1093/pnasnexus/pgad027  

   Patel, Pratiman ; Kalyanam, Rajesh ; He, Liu ; Aliaga, Daniel ; Niyogi, Dev ; Zhang, ed., Jiahua ( February 2023 , PNAS Nexus)

   Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.

    

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2. 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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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. 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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5. Mitigation Options to Reduce Peak Air Temperature and Air-Conditioning Demand in the Context of a Warming Climate for a Tropical Coastal City

   https://doi.org/10.1115/1.4051160  

   Pokhrel, Rabindra ; González-Cruz, Jorge E. ( May 2021 , ASME Journal of Engineering for Sustainable Buildings and Cities)

   null (Ed.)

   Abstract Air conditioning (AC) demand has recently grown to about 10% of total electricity globally, and the International Energy Agency (IEA) predicts that the cooling requirement for buildings globally increases by three-fold by 2050 without additional policy interventions. The impacts of these increases for energy demand for human comfort are more pronounced in tropical coastal areas due to the high temperatures and humidity and their limited energy resources. One of those regions is the Caribbean, where building energy demands often exceed 50% of the total electricity, and this demand is projected to increase due to a warming climate. The interconnection between the built environment and the local environment introduces the challenge to find new approaches to explore future energy demand changes and the role of mitigation measures to curb the increasing demands for vulnerable tropical coastal cities due to climate change. This study presents mid-of-century and end-of-century cooling demand projections along with demand alleviation measures for the San Juan Metropolitan Area in the Caribbean Island of Puerto Rico using a high-resolution configuration of the Weather Research and Forecasting (WRF) model coupled with Building Energy Model (BEM) forced by bias-corrected Community Earth Systems Model (CESM1) global simulations. The World Urban Database Access Portal Tool (WUDAPT) Land Class Zones (LCZs) bridge the gap required by BEM for their morphology and urban parameters. MODIS land covers land use is depicted for all-natural classes. The baseline historical period of 2008–2012 is compared with climate and energy projections in addition to energy mitigation options. Energy mitigation options explored include the integration of solar power in buildings, the use of white roofs, and high-efficiency heating, ventilation, and air conditioning (HVAC) systems. The impact of climate change is simulated to increase minimum temperatures at the same rate as maximum temperatures. However, the maximum temperatures are projected to rise by 1–1.5 °C and 2 °C for mid- and end-of-century, respectively, increasing peak AC demand by 12.5% and 25%, correspondingly. However, the explored mitigation options surpass both increases in temperature and AC demand. The AC demand reduction potential with energy mitigation options for 2050 and 2100 decreases the need by 13% and 1.5% with the historical periods. Overall, the demand reduction potential varies with LCZs showing a high reduction potential for sparsely built (32%), and low for compact low rise (21%) for the mid-of-century period compared with the same period without mitigation options. 

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