Refined dataset to describe the complex urban environment of Hong Kong for urban climate modelling studies at the mesoscale
Publication in refereed journal
Officially Accepted for Publication


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AbstractUrban climate models are indispensable tools for the evaluation of climatic risks faced by the growing urban population. In order to accurately simulate the urban surface energy balance at a high spatial resolution, it is important to provide models with detailed input data that can adequately describe the spatial variation of land covers, urban morphology, construction materials, and building functions within an urban area. Using Hong Kong—a city well-known for its complex, high-rise urban environment—as the testing ground, this study aims to present a geographic information system–based workflow for the construction of a refined urban database. Firstly, maps of land cover tiles, pervious and impervious surface fractions, building height, and other input parameters required by mesoscale atmospheric models are derived from multiple data sources including administrative building data, satellite images, and land use surveys. Secondly, a total of 18 representative building archetypes, with their corresponding architectural characteristics and occupant behaviour schedules, are defined. This allows for models to take into account the radiative, thermal, and dynamic interactions between buildings and the atmosphere, as well as the anthropogenic heat fluxes. Finally, locally adapted ranges of urban morphological parameters for the different local climate zones (LCZs) are derived, enabling the expansion of data coverage to neighbouring areas of Hong Kong, where detailed urban data are not readily available. Uncertainties of the refined database and limitations of the LCZ scheme are also discussed so that a similar approach may be adapted and applied to other cities in the world.
Acceptance Date08/06/2020
All Author(s) ListYu Ting Kwok, Cecile De Munck, Robert Schoetter, Chao Ren, Kevin Ka-Lun Lau
Journal nameTheoretical and Applied Climatology
Year2020
ISSN0177-798X
eISSN1434-4483
LanguagesEnglish-United Kingdom

Last updated on 2020-23-11 at 23:38