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Urban ground-based thermography

Morrison, W. (2019) Urban ground-based thermography. PhD thesis, University of Reading

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Abstract/Summary

Urban climates are driven by micro-meteorological processes associated with the complex urban form, materials, and land cover patterns. Given its close link to the surface energy balance, surface temperature observations are key to the improvement and evaluation of models. This work contributes to the application of ground-based thermography in urban settings as an observational method to further our understanding of urban climate processes. In this thesis, ground-based thermography observations are collected and interpreted in a unique way so that they are relatable to scales used by urban climate models and earth observation (EO) satellites. At two measurement sites (simplified outdoor scale model and complex central urban setting), variations in surface temperature are quantitatively linked to micro-scale features such as shadow patterns and material characteristics at unprecedented levels of detail. Previous studies with low level of detail have inferred these properties. The detected upwelling longwave radiation is corrected to surface temperature (Ts) using a novel, high-resolution three-dimensional (3D) radiative transfer (RT) approach. From multi-day observational evaluation, the atmospheric correction has 0.39 K mean absolute error. Ground-based observations are combined with a comprehensive 3D radiative transfer model, enabling detailed simulation of EO land surface temperature (TsEO). For a mainly clear-sky summer day, TsEO at night underestimates the unbiased “complete” surface temperature (Tc) by 0.5 – 1 K, is similar to Tc during morning and evening, and for other times varies significantly with view angle (up to 5.1 K). Generally, view angle variation is smaller than prior studies as they typically use simpler geometry and temperature descriptions, and lack vegetation. Here, the observational basis and high-resolution modelling in a real central urban setting serves as a benchmark for future improvements of simplified model parameterisations.

Item Type:Thesis (PhD)
Thesis Supervisor:Grimmond, S. and Kotthaus, S.
Thesis/Report Department:School of Mathematical, Physical and Computational Sciences
Identification Number/DOI:
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:89043

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