Articles | Volume 12, issue 7
Geosci. Model Dev., 12, 2781–2795, 2019
https://doi.org/10.5194/gmd-12-2781-2019
Geosci. Model Dev., 12, 2781–2795, 2019
https://doi.org/10.5194/gmd-12-2781-2019
Development and technical paper
09 Jul 2019
Development and technical paper | 09 Jul 2019

A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration

Ting Sun and Sue Grimmond

Related authors

SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023,https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022,https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020,https://doi.org/10.5194/gmd-13-4891-2020, 2020
Short summary
Evaluation of the WRF lake module (v1.0) and its improvements at a deep reservoir
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019,https://doi.org/10.5194/gmd-12-2119-2019, 2019
Short summary
The Analytical Objective Hysteresis Model (AnOHM v1.0): methodology to determine bulk storage heat flux coefficients
Ting Sun, Zhi-Hua Wang, Walter C. Oechel, and Sue Grimmond
Geosci. Model Dev., 10, 2875–2890, https://doi.org/10.5194/gmd-10-2875-2017,https://doi.org/10.5194/gmd-10-2875-2017, 2017
Short summary

Related subject area

Hydrology
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023,https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Customized deep learning for precipitation bias correction and downscaling
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023,https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023,https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023,https://doi.org/10.5194/gmd-16-353-2023, 2023
Short summary
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023,https://doi.org/10.5194/gmd-16-233-2023, 2023
Short summary

Cited articles

Alexander, P. J., Bechtel, B., Chow, W. T. L., Fealy, R., and Mills, G.: Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation, Urban Climate, 17, 196–215, https://doi.org/10.1016/j.uclim.2016.08.003, 2016. 
Alexander, P. J., Mills, G., and Fealy, R.: Using LCZ data to run an urban energy balance model, Urban Climate, 13, 14–37, https://doi.org/0.1016/j.uclim.2015.05.001, 2015. 
Ao, X., Grimmond, C., Chang, Y., Liu, D., Tang, Y., Hu, P., Wang, Y., Zou, J., and Tan, J.: Heat, water and carbon exchanges in the tall megacity of Shanghai: challenges and results, Int. J. Climatol., 36, 4608–4624, https://doi.org/10.1002/joc.4657, 2016. 
Ao, X., Grimmond, C., Ward, H. C., Gabey, A. M., Tan, J., Yang, X.-Q., Liu, D., Zhi, X., Liu, H., and Zhang, N.: Evaluation of the Surface Urban Energy and Water balance Scheme (SUEWS) at a dense urban site in Shanghai: Sensitivity to anthropogenic heat and irrigation, J. Hydrometeorol., 19, 1983–2005, https://doi.org/10.1175/JHM-D-18-0057.1, 2018. 
Bailey, D. H., Barszcz, E., Barton, J. T., Browning, D. S., Carter, R. L., Dagum, L., Fatoohi, R. A., Frederickson, P. O., Lasinski, T. A., Schreiber, R. S., Simon, H. D., Venkatakrishnan, V., and Weeratunga, S. K.: The Nas Parallel Benchmarks, International Journal of Supercomputing Applications, 5, 63–73, https://doi.org/10.1177/109434209100500306, 1991. 
Download
Short summary
A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.