Improved mesoscopic meteorological modelling of the urban climate for building physics applications

A meteorological mesoscale model is used to predict the local urban climate at 250 m resolution. The authors propose a hybrid machine learning approach to improve the prediction accuracy and remove simulation bias. Two case studies are presented to show the improvements of the simulation accuracy. B...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-12, Vol.2654 (1), p.12147
Main Authors: Strebel, D, Derome, D, Kubilay, A, Carmeliet, J
Format: Article
Language:English
Subjects:
Accuracy
Climate models
Cooling loads
Machine learning
Physics
Rural environments
Urban environments
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Summary:A meteorological mesoscale model is used to predict the local urban climate at 250 m resolution. The authors propose a hybrid machine learning approach to improve the prediction accuracy and remove simulation bias. Two case studies are presented to show the improvements of the simulation accuracy. Based on the hybrid model results, using cooling degree hours is proposed as an insightful time-dependent index to map local hotspots and assess the difference of cooling loads between rural and urban environments.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2654/1/012147