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Urban air temperature varies along time and space. This contribution proposes a methodology to model these variations from empirical models. Four air temperature networks are implemented in three west France cities. The period [1.4–1.8] of a normalized night is investigated. Three empirical models are established, in order to predict temporal and spatial air temperature variation. They are gathered under the name of model group . Urban heat island (UHI) intensity is explained according to one temporal model using meteorological variables. Temperature spatial variations are explained from two models using geographical informations averaged in a 500 m radius buffer circle. The first one represents the mean UHI value for a given location, the second the variability of the UHI around its mean value. 32 model groups are calibrated from data sampled by one network. The accuracy of their estimations is tested comparing estimated to observed values from the three other networks. The most accurate model is identified and its performances are analyzed. Night-time UHI intensity variations are explained all along the year by wind speed and nebulosity values calculated after sunset. During spring and summer season UHI variations are mainly driven by Normalized Difference Vegetation Index (NDVI) value whereas building density or surface density are predominant explicative variables during colder seasons. The model is finally applied to the city of Nantes during a summer night to illustrate the interest of this work to urban planning applications. Several warm areas located outside the city center are identified according to this method. Their high UHI value is mainly caused by their low NDVI value. [ABSTRACT FROM AUTHOR]

The urban microclimate is due to complex physical interactions with the contribution of water balance, thermo-radiative exchanges and airflows. In this paper, we present and discuss modeling of heat island effects and mitigation techniques in order to give consistent results considering different time and space scales, and different fluxes (heat, water and winds) from ground to urban canopy, including buildings. The models and numerical descriptions are presented in detail and illustrated on typical examples of heat island mitigation techniques. At the neighborhood scale, alternative rainwater management techniques are studied by considering their impact on both seasonal water table depth and surface-atmosphere heat fluxes. Assessing the building thermal performance interactions with the microclimate requires adapted models that have to be refined for a better description of building envelope and systems effects. Two examples at the street and the neighborhood scale, modifying the building radiative properties or using green envelopes, show how simulation brings out the potential benefits of these techniques for the heat island mitigation and building energy performance. [ABSTRACT FROM PUBLISHER]

The urban context is often simplified or neglected in Building Energy Models (BEMs) due to the difficulties of taking accurately into account all the heat fluxes emanating from the environment. Oversimplifying the urban context can impact the accuracy of the BEM predictions. Nevertheless, several approaches can be used to allow for the impact of the urban environment on the dynamic behavior of a building, its heating and cooling demands, and thermal comfort. This state of the art review provides a critical overview of the different methods currently used to take into account the urban microclimate in building design simulations. First, both the microclimate and building models are presented, focusing on their assumptions and capabilities. Second, a few examples of coupling, performed between both modeling scales are analyzed. Last, the discussion highlights the differences obtained between simulations that take the urban context into consideration and those that simplify or neglect urban heat fluxes. The remaining scientific obstacles to a more effective consideration of the urban context impacting the BEMs are indicated. • The local data produced by a selection of urban climate models (UCMs) are presented. • An analysis of how the urban context is taken into account in building energy models (BEMs) is given. • Several chaining or coupling strategies to link UCMs and BEMs are analyzed and compared. • Using local climate data leads to a noticeable impact on the thermal behavior of buildings. • Recommendations for better consideration of the urban context in BEMs are formulated. [ABSTRACT FROM AUTHOR]