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Typical Meteorological Year (TMY) datasets, widely used in building energy modeling, overlook Urban Heat Island (UHI) effects and future climate trends by relying on long-term data from rural stations such as airports. This study addresses this limitation by integrating Urban Weather Generator (UWG) simulations with CCWorldWeatherGen projections to produce microclimate-adjusted and future weather scenarios. These datasets were then incorporated into an Urban Building Energy Modeling (UBEM) framework using Urban Modeling Interface (UMI) to evaluate energy performance across a lowincome residential neighborhood in Des Moines, Iowa. Results show that UHI intensity will rise from an annual average of 0.55 °C under current conditions to 0.60 °C by 2050 and 0.63 °C by 2080, with peak intensities in summer. The UHI elevates cooling Energy Use Intensity (EUI) by 7% today, with projections indicating a sharp increase—91% by 2050 and 154% by 2080. The UHI will further amplify cooling demand by 2.3% and 6.2% in 2050 and 2080, respectively. Conversely, heating EUI will decline by 20.0% by 2050 and 40.1% by 2080, with the UHI slightly reducing heating demand. Insulation mitigates cooling loads but becomes less effective for heating demand over time. These findings highlight the need for climate-adaptive policies, building retrofits, and UHI mitigation to manage future cooling demand. 

Land surface temperature (LST) derived from satellite observations and weather modeling has been widely used for investigating Earth surface-atmosphere energy exchange and radiation budget. However, satellite-derived LST has a trade-off between spatial and temporal resolutions and missing observations caused by clouds, while there are limitations such as potential bias and expensive computation in model calibration and simulation for weather modeling. To mitigate those limitations, we proposed a WRFM framework to estimate LST at a spatial resolution of 1 km and temporal resolution of an hour by integrating the Weather Research and Forecasting (WRF) model and MODIS satellite data using the morphing technique. We tested the framework in eight counties, Iowa, USA, including urban and rural areas, to generate hourly LSTs from June 1st to August 31st, 2019, at a 1 km resolution. Upon evaluation with in-situ LST measurements, our WRFM framework has demonstrated its ability to capture hourly LSTs under both clear and cloudy conditions, with a root mean square error (RMSE) of 2.63 K and 3.75 K, respectively. Additionally, the assessment with satellite LST observations has shown that the WRFM framework can effectively reduce the bias magnitude in LST from the WRF simulation, resulting in a reduction of the average RMSE over the study area from 4.34 K (daytime) and 4.12 K (nighttime) to 2.89 K (daytime) and 2.75 K (nighttime), respectively, while still capturing the hourly patterns of LST. Overall, the WRFM is effective in integrating the complementary advantages of satellite observations and weather modeling and can generate LSTs with high spatiotemporal resolutions in areas with complex landscapes (e.g., urban). 

The U.S. Department of Energy (DOE) offers building reference prototypes for energy use modeling in commercial and residential buildings. However, these reference prototypes have traditionally been treated in isolation, neglecting the impact of neighboring objects on local microclimate. In urban energy models, where the intricate interaction of urban elements significantly shapes environmental conditions, it becomes more important to reconsider the conventional treatment of building reference prototypes. In this paper we aim to discern potential disparities in energy consumption estimations using DOE prototypes at an urban scale. The Urban Modeling Interface (UMI) was chosen as the simulation platform to incorporate the shadow effect from neighboring objects on building energy use across six scenarios with different shadow coverage by neighboring objects. We found that trees as neighboring structures can decrease cooling load by up to 29%. These results highlight the importance of considering the urban context in energy use estimation of buildings. 

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. 

Abstract Building cooling loads are driven by heat gains through enclosures. This research identifies possible ways of reducing the building cooling loads through vegetative shading. Vegetative shading reduces heat gains by blocking radiation and by evaporative air cooling. Few measured data exist, so we gathered thermal data from a vegetative wall grown in front of a Mobile Diagnostics Lab (MDL), a trailer with one conditioned room with instrumentation that collects thermal data from heat flux sensors and thermistors within its walls. In spring 2020 a variety of plants were cultivated in a greenhouse and planted in front of the south façade of the MDL, which was placed in direct sunlight to collect heat flux data. The plants acted as a barrier for solar radiation and reduced the amount of thermal energy affecting the trailer surface. Data were collected through the use of 16 heat flux sensors and development of continuous infrared (IR) images indicating surface temperature with and without plant cover. The façade surface beneath the plants was 10-30 °C cooler than exposed façade areas. In further analyses, the heat-flux data were compared to IR temperature data. 

We deploy a fully coupled thermo-fluidic finite element approach to simulating natural ventilation in a sustainably designed building with complex geometry. The 'interlock house' uses building design for climate control instead of mechanical means (such as air conditioning). Therefore, accurately modeling the natural ventilation flows is crucial to assess thermal comfort in such designs. A residual-based variational multiscale method (VMS) is employed, which is a Large Eddy Simulation (LES) type approach to turbulence modeling. Air diffusion performance index (ADPI) and predicted mean vote (PMV) are computed to investigate thermal comfort in both configurations. This work illustrates the ability of the framework to comprehensively model and predict natural ventilation under various operating scenarios.