Research

Our group’s research focuses on improving the understanding of the mass, momentum, and energy exchanges in the urban land-atmosphere interaction, especially inside the urban canopy layer (UCL). In today’s world with an unprecedented rate of urbanization, this is becoming increasingly important for tackling environmental challenges arising from cities: how do cities interact with regional/global systems as “hotspots” of technology innovation and policymaking, and what are their impacts in large?





Advanced Urban Climate Modeling

Accurately modeling the physical urban land surface processes and land-atmosphere interactions is of critical importance. Our group has developed novel parameterization schemes for a variety of urban processes and incorporated them into the urban canopy model coupled with the Weather Research and Forecasting model, including hydrological processes, building energy simulation, and building-integrated photovoltaic (BIPV) systems. We are continuously implementing important features of the complex urban environment into climate models to improve their predictability and reliability.

Representative Outputs

  1. Chen, L., Yang, J.*, & Zheng, X. (2024). Modelling the impact of building energy consumption on urban thermal environment: The bias of the inventory approach. Urban Climate, 53, 101802.
  2. Chen, L., Yang, J.*, & Li, P. (2022). Modelling the effect of BIPV window in the built environment: Uncertainty and sensitivity. Building and Environment, 208, 108605.
  3. Chen, L., Zheng, X., Yang, J.*, & Yoon, J. H. (2021). Impact of BIPV windows on building energy consumption in street canyons: Model development and validation. Energy and Buildings, 111207.
  4. Yang, J., Wang, Z. H.*, Chen, F., Miao, S., Tewari, M., Voogt, J. A., Myint, S. (2015) Enhancing hydrological modelling in the coupled Weather Research and Forecasting-Urban modelling system. Boundary-Layer Meteorology, 155, 87-109.
  5. Yang, J., Wang, Z. H.* (2014). Physical parameterization and sensitivity of urban hydrological models: Application to green roof systems. Building & Environment, 75, 250-263.

Data-driven Simulation of Urban Microclimate

Within cities, microclimate exhibits strong variability and uncertainty due to heterogeneous built environment and complex anthropogenic activities. While microclimate information has important implications for air quality, building energy consumption, and residential health in cities, many questions remain unresolved: 1) How to advance the capability of urban climate simulations with an increasing amount of urban dataset, and 2) how to design microclimate monitoring networks to maximize the information content of collected data. We utilize data-driven approaches to address these questions and shed new insights into the urban microclimate studies.

Representative Outputs

  1. Wang, H., Zhang, J., & Yang, J. (2024). Time series forecasting of pedestrian-level urban air temperature by LSTM: Guidance for practitioners. Urban Climate, 56, 102063.
  2. Wang, H., Yang, J.*, Chen, G., Ren, C., & Zhang, J. (2023). Machine learning applications on air temperature prediction in the urban canopy layer: A critical review of 2011–2022. Urban Climate, 49, 101499.
  3. Chen, X., & Yang, J.* (2022). Urban climate monitoring network design: Existing issues and a cluster-based solution. Building and Environment, 214, 108959.
  4. Yang, J.*, & Bou-Zeid, E. (2019). Designing sensor networks to resolve spatio-temporal urban temperature variations: fixed, mobile or hybrid?. Environmental Research Letters, 106946.

Heat and pollution dispersion in Cities

Degraded air quality in cities can be attributed to two main reasons: reduced ventilation due to high building density and on-road emissions from fossil fuel vehicles. The dispersion of traffic pollutants in street canyons takes place under the joint influence of the background wind and vehicle-induced air motions. Turbulent transport of heat and pollutants over heterogeneous built terrains remains a challenging research area. Our group is adopting the large-eddy simulation (LES) approach to address the challenges in this area, including traffic-induced airflow and turbulence, pollutant exposure of different groups of residents, etc.

Representative Outputs

  1. Zheng, X., & Yang, J*. (2023). Urban road network design for alleviating residential exposure to traffic pollutants: Super-block or Mini-block? Sustainable Cities and Society, 89, 104327.
  2. Zheng, X., & Yang, J.* (2022). Impact of moving traffic on pollutant transport in street canyons under perpendicular winds: A CFD analysis using large-eddy simulations. Sustainable Cities and Society, 82, 103911.
  3. Zheng, X., & Yang, J.* (2021). CFD simulations of wind flow and pollutant dispersion in a street canyon with traffic flow: Comparison between RANS and LES. Sustainable Cities and Society, 75, 103307.
  4. Li, Q., Yang, J.*, & Yang, L. (2021). Impact of urban roughness representation on regional hydrometeorology: An idealized study. Journal of Geophysical Research: Atmospheres, 126(4), e2020JD033812

Addressing Societal Relevant Sustainability Challenges

Cities, particularly urban cores, are significantly warmer than their rural surroundings, due to land use land cover changes and human activities. Our group has investigated the effectiveness and efficiency of different strategies for mitigating urban heat islands at a variety of scales, including the use of engineering materials and urban green infrastructure elements. We are investigating how the latest technology and innovation contribute to mitigate heat islands and their adverse impacts, and reshape the strategic urban planning towards smart cities. Our research work not only focus on thermal environment but also other important aspects of sustainable urban development, e.g., extreme precipitation.

Representative Outputs

  1. Zhang, W., Yang, J.*, Yang, L., & Niyogi, D. (2022). Impacts of city shape on rainfall in inland and coastal environments. Earth's Future, e2022EF002654.
  2. Chen, X., & Yang, J.* (2022). Potential benefit of electric vehicles in counteracting future urban warming: A case study of Hong Kong. Sustainable Cities and Society, 87, 104200.
  3. Yang, J.*, Hu, L.*, & Wang, C. (2019). Population dynamics modify urban residents’ exposure to extreme temperatures across the United States. Science Advances, 5, eaay3452.
  4. Yang, J.*, & Bou-Zeid, E. (2019). Scale dependence of the benefits and efficiency of urban heat island mitigation plans?. Landscape and Urban Planning, 185, 127-140.
  5. Yang, J.*, Z. H., Kaloush, K. E., Dylla, H. (2016) Effect of pavement thermal properties on mitigating urban heat islands: A multi-scale modeling case study in Phoenix. Building & Environment, 108, 110-121.