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cities (both governmental bodies and affected stakeholders) must implement wellconceived, comprehensive, and collective actions with a high potential to positively
influence urban climate and remedy the negative phenomena associated with the
urban heat islands.
In this context, the present contribution reports on the results of data analyses
and modelling efforts undertaken to investigates the extent of urban heat island
phenomena and the potential of relevant mitigation measures in the Central
European region (Mahdavi et al. 2013). Thereby, a large set of data was collected
and analysed concerning the extent of the UHI effect in multiple cities in Central
Europe. Furthermore, to develop and demonstrate approaches toward supporting the
process of design and evaluation of UHI mitigation measures, the potential of
numerical (simulation-based) urban microclimate analysis models were explored.
As numerical modelling poses certain challenges not only in view of time and
computational resources but also model validation and calibration issues, the potential of alternative (or complementary) empirically-based modelling options were
investigated. To develop such alternative models, certain features of the urban environment are hypothesized to influence UHI and the urban microclimate variance.
The related variables, which pertain to both geometric (morphological) and semantic (material-related) urban features are captured within a systematic framework.
The statistical relationships between the values of such variables and the extent
of microclimatic variance provide the basis for simple empirically-based models.
These models can be directly used to predict the impact of mitigation measures or
indirectly applied to gauge the performance of detailed numerical models of the
urban microclimate.
3.2 The Urban Heat Island in Central Europe
Metropolitan areas worldwide vary in their spatial configuration. This is typically
manifested in the diversity of the respective microclimatic conditions. The present
contribution focuses on documenting this diversity in terms of the frequency, magnitude, and time-dependent (diurnal and nocturnal) UHI intensity distribution (during a
reference week) and the long-term development of urban and rural temperatures in
seven Central-European cities, namely Budapest, Ljubljana, Modena, Padua, Prague,
Stuttgart, Vienna, and Warsaw (see Tables 3.1 and 3.2). The magnitude of the UHI
effect can be expressed in terms of Urban Heat Island intensity (UHI). This term
denotes the temperature difference (in K) between simultaneously measured urban
and rural temperatures. The aim was to identify and evaluate the extent of the UHI
effect and its variance in the broader geographical context of the participating cities.
As already mentioned, UHI intensity in observed urban areas was derived for a
reference summer week (with high air temperature and relatively low wind velocity) selected by each participating city independently. The collected information
included hourly data on air temperature, wind speed, and precipitation from two
representative weather stations (one urban and one rural).
3 Methodologies for UHI Analysis
cities (both governmental bodies and affected stakeholders) must implement wellconceived, comprehensive, and collective actions with a high potential to positively
influence urban climate and remedy the negative phenomena associated with the
urban heat islands.
In this context, the present contribution reports on the results of data analyses
and modelling efforts undertaken to investigates the extent of urban heat island
phenomena and the potential of relevant mitigation measures in the Central
European region (Mahdavi et al. 2013). Thereby, a large set of data was collected
and analysed concerning the extent of the UHI effect in multiple cities in Central
Europe. Furthermore, to develop and demonstrate approaches toward supporting the
process of design and evaluation of UHI mitigation measures, the potential of
numerical (simulation-based) urban microclimate analysis models were explored.
As numerical modelling poses certain challenges not only in view of time and
computational resources but also model validation and calibration issues, the potential of alternative (or complementary) empirically-based modelling options were
investigated. To develop such alternative models, certain features of the urban environment are hypothesized to influence UHI and the urban microclimate variance.
The related variables, which pertain to both geometric (morphological) and semantic (material-related) urban features are captured within a systematic framework.
The statistical relationships between the values of such variables and the extent
of microclimatic variance provide the basis for simple empirically-based models.
These models can be directly used to predict the impact of mitigation measures or
indirectly applied to gauge the performance of detailed numerical models of the
urban microclimate.
3.2 The Urban Heat Island in Central Europe
Metropolitan areas worldwide vary in their spatial configuration. This is typically
manifested in the diversity of the respective microclimatic conditions. The present
contribution focuses on documenting this diversity in terms of the frequency, magnitude, and time-dependent (diurnal and nocturnal) UHI intensity distribution (during a
reference week) and the long-term development of urban and rural temperatures in
seven Central-European cities, namely Budapest, Ljubljana, Modena, Padua, Prague,
Stuttgart, Vienna, and Warsaw (see Tables 3.1 and 3.2). The magnitude of the UHI
effect can be expressed in terms of Urban Heat Island intensity (UHI). This term
denotes the temperature difference (in K) between simultaneously measured urban
and rural temperatures. The aim was to identify and evaluate the extent of the UHI
effect and its variance in the broader geographical context of the participating cities.
As already mentioned, UHI intensity in observed urban areas was derived for a
reference summer week (with high air temperature and relatively low wind velocity) selected by each participating city independently. The collected information
included hourly data on air temperature, wind speed, and precipitation from two
representative weather stations (one urban and one rural).
3 Methodologies for UHI Analysis