Dissertation/Thesis Abstract

Using satellite remote sensing, field observations and WRF/single-layer urban canopy model simulation to analyze the Oklahoma City UHI effect
by Zhang, Hengyue, M.S., San Jose State University, 2015, 68; 1594250
Abstract (Summary)

The Urban Heat Island (UHI) was investigated using satellite data, ground observations, and simulations with an Urban Canopy Parameterization in a numerical weather prediction model. Satellite-observed surface skin temperatures at Xi'an City and Oklahoma City (OKC) were analyzed to compare the UHI intensity for the two inland cities. A larger population density and larger building density in Xi'an City creates a stronger skin-level UHI effect. However, ground observed 2-m surface air temperature (Tair) data showed an urban cooling island (UCI) effect that occurred over an urban region in OKC during the daytime of July 19, 2003.

The sensitivity and accuracy of an Urban Canopy Model were evaluated by comparing simulation results between the urban and rural areas of OKC. The model reproduced skin temperature differences between the rural and urban area and reproduced a UCI effect in OKC. Furthermore, the Weather Research and Forecasting (WRF)/Noah/Single-Layer Urban Canopy Model (SLUCM) simulations were also compared with ground observations, including wind speeds, wind directions, and energy fluxes. Although the WRF/SLCUM model failed to simulate these variables accurately, it reproduced the diurnal variations of surface temperatures, wind speeds, wind directions and energy fluxes reasonably well.

Indexing (document details)
Advisor: Clements, Craig B.
Commitee: Jin, Menglin S., Leach, Martin J.
School: San Jose State University
Department: Meteorology
School Location: United States -- California
Source: MAI 54/06M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Climate Change, Meteorology
Keywords: Field observation, Oklahoma city, Satellite remote sensing, Urban cooling island, Urban heat island, WRF/SLUCM simulation, Weather research and forecasting
Publication Number: 1594250
ISBN: 9781321911879
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