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1. Pathway using WUDAPT's Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling

   Ching, J; Aliaga, D; Mills, G; Masson, V; See, L; Neophytou, M; Middel, A; Baklanov, A; Ren, C; Ng, E; et al (June 2019, Urban climate)

   The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs) as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones (LCZ) with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC) tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC) to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling within city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells. 

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2. Urban Weather Predictions Compared to a Dense Network of Ground-Based Weather Station Data for Assessment of Urban Building Energy Consumption

   Jahani, E; Vanage, S.; Jahn, D.; Gallus, W.; Cetin, K.S. (June 2019, ASHRAE Annual Conference papers)

   Building energy consumption is highly influenced by weather conditions, thus having appropriate weather data is important for improving the accuracy of building energy models. Typically local weather station data from the nearest airport or military base is used for weather data input. However this is generally known to differ from the actual weather conditions experienced by an urban building, particularly considering most weather stations are located far from urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides a means to be able to predict more localized variations in weather conditions. However, one of the main challenges associated with the assessment of the use of this model is the lack of availability of ground based weather station data with which to compare its results. This has generally limited the ability to assess the level of agreement between WRF-UCM weather predictions and measured weather data in urban locations. In this study, a network of 40 ground based weather stations located in Austin, TX are compared to WRF/UCM-predicted weather data, to assess similarities and differences between model-predicted results and actual data. Given that the WRF-UCM method also takes into account many input parameters and assumptions, including the urban fraction which can be measured at different scales, this work also considers the relative impact of the granularity of the urban fraction data on WRF-UCM predicted weather. As a case study, a building energy model of a typical residential building is then developed and used to assess the differences in predicted building energy use and demands between the WRF-UCM weather and measured weather conditions during an extreme heatwave event in Austin, TX 

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3. Assessing a community-engaged decision framework for increased urban neighborhood resilience in a warming climate

   https://doi.org/10.35483/ACSA.AIA.Inter.21.29  

   Passe, U (November 2023, Proceedings of the American Institute of Architecture Conference on Communities)

   Successful urban systems-related climate-action-support tools enable urban stakeholders to communicate and collaborate across and beyond their respective disciplines to identify innovative, transformative solutions to increase urban infrastructure resilience and sustainability. The actions of humans within buildings and the relationship of buildings to their near-building environments (aka microclimate) constitute one understudied urban system with significant impact on urban energy use strongly impacted by a warming urban climate. This interdisciplinary research team lead by an architect at a large research university collaborates with local community partners to identify evidence-based approaches for the integration of human behavior data, building energy use characteristics, future climate scenarios, and near-building microclimate data. The team has built a prototypical model, which integrates urban trees into urban energy models based on a large-scale inventory and probabilistic occupancy data based on a neighborhood wide energy use survey. To ensure that these urban energy models are equitable, however, the needs of marginalized populations must be included- especially those most vulnerable to the consequences of a changing climate. The paper reports on two intertwined research strands. First of all, the team’s best practices for gathering data from individuals facing marginalization as well as the application of this residential occupancy data into neighborhood energy models. The second strand addresses trees in urban landscapes and their capacity to modify temperatures in the near-building environment, which is important for reducing summer heat loads on building surfaces. Preliminary results for an urban neighborhood strategies are reported. 

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4. CITY-SCALE ENERGY MODELING TO ASSESS IMPACTS OF EXTREME HEAT ON ELECTRICITY CONSUMPTION AND PRODUCTION USING WRF-UCM MODELING WITH BIAS CORRECTION

   Jahani, Elham; Vanage, Soham; Cetin, Kristen; Gallus, William (January 2019, CSCE Annual Conference)

   null (Ed.)

   The energy consumption of buildings at the city scale is highly influenced by the weather conditions where the buildings are located. Thus, having appropriate weather data is important for improving the accuracy of prediction of city-level energy consumption and demand. Typically, local weather station data from the nearest airport or military base is used as input into building energy models. However, the weather data at these locations often differs from the local weather conditions experienced by an urban building, particularly considering most ground-based weather stations are located far from many urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides means to predict more localized variations in weather conditions. However, despite advances made in climate modeling, systematic differences in ground-based observations and model results are observed in these simulations. In this study, a comparison between WRF-UCM model results and data from 40 ground-based weather station in Austin, TX is conducted to assess existing systematic differences. Model validations was conducted through an iterative process in which input parameters were adjusted to obtain to best possible fit to the measured data. To account for the remaining systemic error, a statistical approach with spatial and temporal bias correction is implemented. This method improves the quality of the WRF-UCM model results by identifying the statistic properties of the systematic error and applying several bias correction techniques. 

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5. CITY-SCALE ENERGY MODELING TO ASSESS IMPACTS OF EXTREME HEAT ON ELECTRICITY CONSUMPTION AND PRODUCTION USING WRF-UCM MODELING WITH BIAS CORRECTION

   Jahani, E; Vanage, S.; Jahn, D.; Gallus, W.; Cetin, K.S. (June 2019, Canadian Society of Civil Engineers Annual Conference)

   The energy consumption of buildings at the city scale is highly influenced by the weather conditions where the buildings are located. Thus, having appropriate weather data is important for improving the accuracy of prediction of city-level energy consumption and demand. Typically, local weather station data from the nearest airport or military base is used as input into building energy models. However, the weather data at these locations often differs from the local weather conditions experienced by an urban building, particularly considering most ground-based weather stations are located far from many urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides means to predict more localized variations in weather conditions. However, despite advances made in climate modeling, systematic differences in ground-based observations and model results are observed in these simulations. In this study, a comparison between WRF-UCM model results and data from 40 ground-based weather station in Austin, TX is conducted to assess existing systematic differences. Model validations was conducted through an iterative process in which input parameters were adjusted to obtain to best possible fit to the measured data. To account for the remaining systemic error, a statistical approach with spatial and temporal bias correction is implemented. This method improves the quality of the WRF-UCM model results by identifying the statistic properties of the systematic error and applying several bias correction techniques. 

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