Author:
Strebel D,Derome D,Kubilay A,Carmeliet J
Abstract
Abstract
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.
Subject
Computer Science Applications,History,Education
Reference13 articles.
1. Advancement in Urban Climate Modelling at Local Scale: Urban Heat Island Mitigation and Building Cooling Demand;Kubilay;Atmosphere,2020
2. Impact of urban heat island on cooling energy demand for residential building in Montreal using meteorological simulations and weather station observations;Boudali Errebai;Energy and Buildings,2022
3. A Description of the Advanced Research WRF Model Version 4;Skamarock,2019
4. A Rational Subdivision of Scales for Atmospheric Processes Bulletin of the;Orlanski;American Meteorological Society,1975