SUMMARY

As urban areas expand, local vegetation is being replaced with man-made materials, causing increasingly adverse impacts on the surface-atmosphere energy balance. The main challenges of measuring and modeling urban thermal environments are the scale and resolution at which to perform such tasks. Current modeling of urban thermal environments is typically limited to either mesoscale (1 km to 2,000 km), or microscale (< 1 km) phenomena. This results in a gap of knowledge in simulating coupled mesoscale and microscale features upstream and downstream from urbanized regions. Additionally, measurement of urban thermal environments is historically done either remotely or via weather stations. The next generation of measurement utilizes advances in computing and communications to create distributed meteorological sensor networks. In the present work, the two main challenges of measuring and modeling urban thermal environments are addressed. First, an open-source framework for one-way upstream and downstream coupled multiscale urban thermal environment simulations will be presented that would be capable of providing valuable insights about the flow behavior and energy transport between the mesoscale and microscale. Downstream mesoscale to microscale boundary conditions are coupled together in a multiscale simulation using the Weather Research and forecasting Model, a mesoscale weather forecasting software and PALM, a CFD-style software designed for microscale atmospheric flows. Second, temperature distributions obtained from one way upstream multiscale simulations are experimentally validated against distributed sensor measurements at the Georgia Institute of Technology campus. Finally, a distributed meteorological sensor networks approach is implemented using low-cost environmental sensors inside outdoor digital displays that is scalable to any urban environment.

Meeting Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_M2ZiZjc4MjMtZWFhMy00OGQ4LWJlYzMtYjM2NTFkYzQ5ZDhl%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22024a59dc-7a7f-4a6d-9e5c-4405ca3dec60%22%7d