Dominik Strebel, D. Derome, A. Kubilay, J. Carmeliet
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Improved mesoscopic meteorological modeling of the urban climate for building physics applications
Meteorological mesoscale models with different urban parametrization are used to predict the local urban climate at 250 m resolution. The authors propose a hybrid machine learning approach to improve the mesoscale prediction accuracy using measured air temperature data from a sensor network and remove simulation bias. The simulation of the urban climate of Zurich during a hot summer is used as case study showing the improvements of the simulation accuracy. Based on the hybrid model results, a cumulative heat exposure index is proposed to map local hotspots in the city and assess the difference of cooling loads between rural and urban environments. Furthermore, intra-urban microclimatic differences of a typical mid-latitude city are explored to highlight the benefits of detailed simulations for building physics purposes.
期刊介绍:
Journal of Building Physics (J. Bldg. Phys) is an international, peer-reviewed journal that publishes a high quality research and state of the art “integrated” papers to promote scientifically thorough advancement of all the areas of non-structural performance of a building and particularly in heat, air, moisture transfer.