1,2,3,3,2,6,7,1,8,2,2,11,- 1University College London, Department of Risk and Disaster Reduction, London, United Kingdom of Great Britain – England, Scotland, Wales (ting.sun@ucl.ac.uk)
- 2Department of Meteorology, University of Reading, Reading, UK
- 3Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
- 4Met Office, Reading, UK
- 5Arup, London, UK
- 6European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
- 7Laboratoire de M´et´eorologie Dynamique (LMD-IPSL), ´Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau CEDEX, France
- 8Department of Hydraulic Engineering, Tsinghua University, Beijing, China
- 9Shanghai Climate Centre, Shanghai, China
- 10School of GeoSciences, University of Edinburgh, Edinburgh, UK
- 11Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
- 12School of Atmospheric Sciences, Nanjing University, Nanjing, China
Since ICUC11, the Surface Urban Energy and Water balance Scheme (SUEWS) has advanced as a leading urban climate model. We have significantly improved its functionality, accessibility, and real-world impact. These advancements span several key areas that strengthen SUEWS’s role in understanding and addressing urban climate challenges:
- Online coupling with WRF model: SUEWS has been successfully coupled with WRF and tested at two UK sites, demonstrate promising performance in real-time interactions with NWP models.
- Enhanced radiation and heat storage modelling: The integration of SPARTACUS for radiative transfer enhances SUEWS's ability to model urban energy exchanges by accurately representing shortwave and longwave radiation in complex urban geometries. This is further improved by the explicit heat conduction (EHC) scheme, which better represents heat storage dynamics essential for predicting surface temperatures.
- Geographic property database: A comprehensive SUEWS property database has been developed, providing evidence-based parameter entries for different geographical contexts. This development streamlines the model configuration process and enhances accessibility for diverse urban environments through geographically relevant datasets.
- Structured data input and processing: A new pydantic- and YAML-based workflow has been implemented to streamline data handling. This approach ensures rigorous input validation while maintaining a structured, human-readable format. The workflow is also AI-compliant, facilitating integration with large language models (LLMs) for automated processing and analysis.
SUEWS has expanded its impact through public engagement and real-world applications. Notable collaborations include the Arup UHeat initiative and involvement in NYC climate action, demonstrating the model’s crucial role in addressing urban climate challenges and informing climate resilience strategies.
Future developments are focused on enhancing LLM-compliant workflows to improve automation and accessibility, refining anthropogenic heat representation for more comprehensive urban energy emission modelling, and establishing a SUEWS board to better synergise community efforts and guide collective development of the model.
How to cite: Sun, T., Grimmond, S., Lindberg, F., Backlin, O., Blunn, L., Hertwig, D., Ghufoor, N., Hogan, R., Kotthaus, S., Li, R., Li, Z., Morrison, W., Omidvar, H., Paskin, M., Rana, D., Rognone, S., Stretton, M., Ward, H., and Zhang, N.: Advancing SUEWS Since ICUC11: Key Developments and Future Prospects, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-437, https://doi.org/10.5194/icuc12-437, 2025.
Supporters & sponsors
