Accurately predicting mean flow and turbulence in flow over realistic urban environments is a necessary but not sufficient condition to guide the design of parks and neighborhoods, and to determine how these affect the transport of heat, moisture, and particulate matter at the height where people live. Thanks to advances in computing hardware and software, it is now possible to carry out high fidelity simulations of flow in urban environments, where the airflow interacts with surface features including buildings and trees. High fidelity simulations require detailed datasets describing the urban surface, and said information is often not readily available. The goal of this project is to expand the New York City tree census database to include information of the three-dimensional geometry of urban trees as required for flow simulations. LiDAR point cloud measurements and the ONRL DAAC LAI database will be leveraged to achieve this goal.
Direct Supervisor: Marco Giometto
Hours per week: 35
Dates: 6/1/2020 - 8/31/2020
Qualifications: GIS knowledge, Python programming, Data science.
Eligibility: SEAS only