Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-2781-2019
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

WRF (v4.0)-SUEWS (v2018c) Coupled System: Development, Evaluation and Application
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-117,https://doi.org/10.5194/gmd-2023-117, 2023
Revised manuscript accepted for GMD
Short summary
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

Related subject area

Hydrology
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023,https://doi.org/10.5194/gmd-16-6479-2023, 2023
Short summary
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023,https://doi.org/10.5194/gmd-16-5847-2023, 2023
Short summary
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023,https://doi.org/10.5194/gmd-16-5685-2023, 2023
Short summary
Enhancing the representation of water management in global hydrological models
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023,https://doi.org/10.5194/gmd-16-5449-2023, 2023
Short summary
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023,https://doi.org/10.5194/gmd-16-5035-2023, 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.