Climate and
Atmospheric Modelling
Contact:
Dr Xiaoming Cai
School of Geography, Earth & Environmental Sciences (GEES)
The University of Birmingham
Edgbaston, Birmingham
B15 2TT, United Kingdom
Email:
informatics-crn-enquiries[at]cs.bham.ac.uk
Website: http://www.gees.bham.ac.uk/research/climate/
The increasing trend of urbanisation has the potential to pose a serious
threat to both the quality of human life and the natural environment on
a global scale. This is particularly true in urban areas where predicted
trends in global warming combined with increasing urban heat island effects
may result in significant changes to local climate within the ‘urban canopy’,
in which people live. However, dynamical and thermal processes inside
the urban canopy are extremely complicated and therefore such an impact
on local climate is largely unknown. In addition, pedestrians, cyclists,
drivers and residents in the urban canopy are likely to be exposed to
pollutant concentrations exceeding current air quality standards in urban
areas where high air pollution levels (NO2, PM10, benzene) have been observed.
Dispersion models commonly used to assess air quality within street canyons
for regulatory purposes only calculate background concentrations (i.e.,
above the urban canopy), which are normally much lower than those observed
inside the urban canopy.
Research at Birmingham seeks
to determine numerically meteorological conditions on the scales ranging
from metres to hundreds of kilometres and their influence on dispersion
of atmospheric pollutants. A particular strength has been developed at
Birmingham based on a comprehensive numerical tool called ‘large-eddy
simulation’, which attempts to resolve those energetic turbulent eddies
and therefore to provide a fairly reliable numerical solution to turbulence,
one of the most challenging scientific topics. An example of such studies
is to provide reliable climatic conditions or to identify pollutant dispersion
scenarios within the urban canopy. Implications of the study include developing
and validating empirical algorithms that are used in a climate model or
an urban air quality model.
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