1,2,3,1,4,3,4- 1Physics and Astronomy, Ghent University, Ghent, Belgium
- 2Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
- 3Electronics and Informatics Department (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
- 4Royal Meteorological Institute of Belgium, Brussels, Belgium
Detailed long-term climate scenarios over cities are needed for reliable future urban risk assessment and for designing climate resilient cities. For many cities, only coarse model information is however available. A small portion of mainly large cities in the global North possesses (sub-)kilometer long-term climate information, often acquired in the context of dedicated modelling case studies. Producing intra-urban climate projections for the multitude of medium to small cities is impossible using traditional downscaling techniques due to the high computational cost.
Therefore, we explored the potential of machine learning to simulate high-resolution near-surface air temperature over European cities. The European Random Forest Urban Climate Emulator (Eu-RaFUCE) framework was built and tested on hectometer-scale 2 m-air temperature of the urban climate model UrbClim for which data for 100 European cities is available (Lauwaet et al., 2024). Feature importance analysis showed that both temporal, such as surface net solar radiation, and spatial, such as land cover, inputs are important for the machine learning model to capture the urban heat island (UHI) effect. By applying Eu-RaFUCE, hourly ERA5 reanalysis data can be directly downscaled to (sub-)kilometer 2 m-temperature. Eu-RaFUCE captures the spatial and temporal UHI characteristics of multiple test cities well, resulting in high model accuracy over cities and years that were not included during the training. Out of all test cities Madrid has the largest root mean square error (RMSE), amounting 1.82 K, while the lowest RMSE of 0.85 K was found for Tallin. This proof of concept paves the way for the application of Eu-RaFUCE in downscaling 2 m-temperature projections to hectometer resolution over many cities, showing the potential of machine learning for urban climate studies.
Reference: Lauwaet et al. (2024). High resolution modelling of the urban heat island of 100 European cities. Urban Climate, 54, 101850.
How to cite: Top, S., Blancke, J., Delbare, K., Covaci, A., Caluwaerts, S., Hamdi, R., and De Cruz, L.: Hectometer-scale 2 m-temperature climate data over every city?!, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-647, https://doi.org/10.5194/icuc12-647, 2025.
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