The energy exchanges at the Earth’s surface are responsible for many of the processes that govern weather, climate, human health, and energy use. This exchange, commonly known as the surface energy balance (SEB), determines the near-surface thermodynamic state by partitioning the available energy into surface fluxes. The net all-wave radiation is often the primary energy source, while the heat storage and sensible and latent heat fluxes account for the majority of energy distributed elsewhere. While the SEB of various natural environments (trees, crops, soils) has been well-observed and modeled, the urban surface energy balance remains elusive. This is due to the heterogeneity of urban land cover, where the surface cover is dominated by impervious materials (buildings, roads, and pavements) interspersed with vegetation and bare soil. The impervious materials differ in their hygro-thermal properties based on their inherent capacity to conduct and retain heat and moisture. Traditional observation techniques are unable to capture all the relevant processes in cities, and as a result, the urban surface energy budget remains mostly unknown. In this seminar, novel techniques that combine traditional boundary layer turbulence measurements and advanced remote sensing methods are presented as solutions to advance our understanding of urban surface energy balance.
Here, new methodologies are developed that apply remote sensing-based algorithms to urban environments. The first topic uses satellite measurements to derive near-surface air temperature for urban areas- this has yielded a publication (DOI: 10.1016/j.rse.2019.111495). Next, a satellite-based algorithm that approximates the net all-wave radiation is presented, using machine learning and land cover information. Lastly, two novel methods for predicting the heat stored in cities are introduced (one of which resulted in a publication with DOI:10.1016/j.rse.2020.112125). Overall, this dissertation presents new knowledge and develops novel algorithms that expand and advance our understanding of urban thermodynamics, which impacts how we observe and model agricultural processes, human vulnerability to weather and climate, better predict energy use, and improve the sustainability of our cities.
|Commitee:||Gonzalez, Jorge, Andreopoulos, Yiannis, Fan, Jing, Yu, Peng|
|School:||The City College of New York|
|School Location:||United States -- New York|
|Source:||DAI-B 82/7(E), Dissertation Abstracts International|
|Subjects:||Mechanical engineering, Atmospheric sciences, Remote sensing, Artificial intelligence|
|Keywords:||Energy balance, Heat transfer, Machine learning, Remote sensing, Satellite, Urban|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be