Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6301-2024
https://doi.org/10.5194/gmd-17-6301-2024
Development and technical paper
 | 
27 Aug 2024
Development and technical paper |  | 27 Aug 2024

Mixed-precision computing in the GRIST dynamical core for weather and climate modelling

Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue

Related authors

HOPE: An Arbitrary-Order Non-Oscillatory Finite-Volume Shallow Water Dynamical Core with Automatic Differentiation
Lilong Zhou and Wei Xue
EGUsphere, https://doi.org/10.5194/egusphere-2025-1889,https://doi.org/10.5194/egusphere-2025-1889, 2025
Short summary
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024,https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023,https://doi.org/10.5194/gmd-16-2975-2023, 2023
Short summary
Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes
Xin Wang, Yilun Han, Wei Xue, Guangwen Yang, and Guang J. Zhang
Geosci. Model Dev., 15, 3923–3940, https://doi.org/10.5194/gmd-15-3923-2022,https://doi.org/10.5194/gmd-15-3923-2022, 2022
Short summary
A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine
Bowen Cao, Le Yu, Victoria Naipal, Philippe Ciais, Wei Li, Yuanyuan Zhao, Wei Wei, Die Chen, Zhuang Liu, and Peng Gong
Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021,https://doi.org/10.5194/essd-13-2437-2021, 2021
Short summary

Related subject area

Atmospheric sciences
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025,https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025,https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Chempath 1.0: an open-source pathway analysis program for photochemical models
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025,https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025,https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary
Atmospheric moisture tracking with WAM2layers v3
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025,https://doi.org/10.5194/gmd-18-4335-2025, 2025
Short summary

Cited articles

Baboulin, M., Buttari, A., Dongarra, J., Kurzak, J., Langou, J., Langou, J., Luszczek, P., and Tomov, S.: Accelerating scientific computations with mixed precision algorithms, Comput. Phys. Commun., 180, 2526–2533, https://doi.org/10.1016/j.cpc.2008.11.005, 2009. 
Banderier, H., Zeman, C., Leutwyler, D., Rüdisühli, S., and Schär, C.: Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification, Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, 2024. 
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.: The digital revolution of Earth-system science, Nat. Comput. Sci., 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0, 2021. 
Benjamin, S. G., Brown, J. M., Brunet, G., Lynch, P., Saito, K., and Schlatter, T. W.: 100 Years of Progress in Forecasting and NWP Applications, Meteorol. Monogr., 59, 13.11–13.67, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0020.1, 2019. 
Download
Short summary
This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Share