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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. Modeling Large‐Scale Heatwave by Incorporating Enhanced Urban Representation

   https://doi.org/10.1029/2021JD035316  

   Patel, Pratiman ; Jamshidi, Sajad ; Nadimpalli, Raghu ; Aliaga, Daniel G. ; Mills, Gerald ; Chen, Fei ; Demuzere, Matthias ; Niyogi, Dev ( January 2022 , Journal of Geophysical Research: Atmospheres)

   This study evaluates the impact of land surface models (LSMs) and urban heterogeneity [using local climate zones (LCZs)] on air temperature simulated by the Weather Research and Forecasting model (WRF) during a regional extreme event. We simulated the 2017 heatwave over Europe considering four scenarios, using WRF coupled with two LSMs (i.e., Noah and Noah‐MP) with default land use/land cover (LULC) and with LCZs from the World Urban Database and Access Portal Tools (WUDAPT). The results showed that implementing the LCZs significantly improves the WRF simulations of the daily temperature regardless of the LSMs. Implementing the LCZs altered the surface energy balance partitioning in the simulations (i.e., the sensible heat flux was reduced and latent heat flux was increased) primarily due to a higher vegetation feedback in the LCZs. The changes in the surface flux translated into an increase in the simulated 2‐m relative humidity and 10‐m wind speed as well as changed air temperature within cities section and generated a temperature gradient that affected the temperatures beyond the urban regions. Despite these changes, the factor separation analysis indicated that the impact of LSM selection was more significant than the inclusion of LCZs. Interestingly, the lowest bias in temperature simulations was achieved when WRF was coupled with the Noah as the LSM and used WUDAPT as the LULC/urban representation.

    

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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. 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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