Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6301-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6301-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
Siyuan Chen
2035 Future Laboratory, PIESAT Information Technology Co., Ltd., Beijing, China
Beijing Research Institute, Nanjing University of Information Science and Technology, Beijing, China
2035 Future Laboratory, PIESAT Information Technology Co., Ltd., Beijing, China
State key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, Beijing, China
Beijing Research Institute, Nanjing University of Information Science and Technology, Beijing, China
Yiming Wang
2035 Future Laboratory, PIESAT Information Technology Co., Ltd., Beijing, China
Beijing Research Institute, Nanjing University of Information Science and Technology, Beijing, China
Zhuang Liu
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
Xiaohan Li
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
Wei Xue
Department of Computer Science and Technology, Tsinghua University, Beijing, China
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2790, https://doi.org/10.5194/egusphere-2025-2790, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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This study develops a novel physics-based weather prediction model using artificial intelligence development platforms, achieving high accuracy while maintaining strict physical conservation laws. Our algorithms are optimized for modern super computers, enabling efficient large-scale weather simulations. A key innovation is the model's inherent differentiable nature, allowing seamless integration with AI systems to enhance predictive capabilities through machine learning techniques.
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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
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
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
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The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
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
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This study uses a set of deep neural networks to learn a parameterization scheme from a superparameterized general circulation model (GCM). After being embedded in a realistically configurated GCM, the parameterization scheme performs stably in long-term climate simulations and reproduces reasonable climatology and climate variability. This success is the first for long-term stable climate simulations using machine learning parameterization under real geographical boundary conditions.
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
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In this study, the first 30 m resolution terrace map of China was developed through supervised pixel-based classification using multisource, multi-temporal data based on the Google Earth Engine platform. The classification performed well with an overall accuracy of 94 %. The terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and the terrace map will be valuable for studies on soil erosion, carbon cycle, and ecosystem service assessments.
Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu, and Aixue Hu
Geosci. Model Dev., 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021, https://doi.org/10.5194/gmd-14-2977-2021, 2021
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This paper presents the high-resolution version of the Beijing Climate Center (BCC) Climate System Model, BCC-CSM2-HR, and describes its climate simulation performance including the atmospheric temperature and wind; precipitation; and the tropical climate phenomena such as TC, MJO, QBO, and ENSO. BCC-CSM2-HR is our model version contributing to the HighResMIP. We focused on its updates and differential characteristics from its predecessor, the medium-resolution version BCC-CSM2-MR.
Yihui Zhou, Yi Zhang, Jian Li, Rucong Yu, and Zhuang Liu
Geosci. Model Dev., 13, 6325–6348, https://doi.org/10.5194/gmd-13-6325-2020, https://doi.org/10.5194/gmd-13-6325-2020, 2020
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This paper explores the configuration of a global atmospheric model (global-to-regional integrated forecast system-atmosphere; GRIST-A) with various multiresolution grids. The model performance is evaluated from dry dynamics to simple physics and full physics. The model is able to resolve the fine-scale structures in the grid-refinement region, and the adverse impact due to the mesh transition and the coarse-resolution area can be controlled well.
Xiaohan Li, Yi Zhang, Xindong Peng, and Jian Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-254, https://doi.org/10.5194/gmd-2020-254, 2020
Revised manuscript not accepted
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This study develops a single-column model (SGRIST1.0) to bridge the coupling of physical parameterizations and a new unstructured-mesh modeling system. The physical parameterization suite is first isolated and evaluated via SGRIST1.0 to reduce the uncertainty of physics during transfer, then the validated parameterization suite is coupled to the 3D dynamical framework. The transferred package shows reasonable behavior in the full physics-dynamics interaction.
Shaoqing Zhang, Haohuan Fu, Lixin Wu, Yuxuan Li, Hong Wang, Yunhui Zeng, Xiaohui Duan, Wubing Wan, Li Wang, Yuan Zhuang, Hongsong Meng, Kai Xu, Ping Xu, Lin Gan, Zhao Liu, Sihai Wu, Yuhu Chen, Haining Yu, Shupeng Shi, Lanning Wang, Shiming Xu, Wei Xue, Weiguo Liu, Qiang Guo, Jie Zhang, Guanghui Zhu, Yang Tu, Jim Edwards, Allison Baker, Jianlin Yong, Man Yuan, Yangyang Yu, Qiuying Zhang, Zedong Liu, Mingkui Li, Dongning Jia, Guangwen Yang, Zhiqiang Wei, Jingshan Pan, Ping Chang, Gokhan Danabasoglu, Stephen Yeager, Nan Rosenbloom, and Ying Guo
Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020, https://doi.org/10.5194/gmd-13-4809-2020, 2020
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Science advancement and societal needs require Earth system modelling with higher resolutions that demand tremendous computing power. We successfully scale the 10 km ocean and 25 km atmosphere high-resolution Earth system model to a new leading-edge heterogeneous supercomputer using state-of-the-art optimizing methods, promising the solution of high spatial resolution and time-varying frequency. Corresponding technical breakthroughs are of significance in modelling and HPC design communities.
Zhuang Liu, Yi Zhang, Xiaomeng Huang, Jian Li, Dong Wang, Mingqing Wang, and Xing Huang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-158, https://doi.org/10.5194/gmd-2020-158, 2020
Revised manuscript not accepted
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This paper describes several techniques for the parallelization and performance optimization of
an unstructured-mesh global atmospheric model. The purpose of this research is to facilitate the rapid iterative model development. These techniques are general and can be used for other parallel modeling on unstructured meshes.
Cited articles
Abdelfattah, A., Anzt, H., Boman, E. G., Carson, E., Cojean, T., Dongarra, J., Fox, A., Gates, M., Higham, N. J., Li, X. S., Loe, J., Luszczek, P., Pranesh, S., Rajamanickam, S., Ribizel, T., Smith, B. F., Swirydowicz, K., Thomas, S., Tomov, S., Tsai, Y. M., and Yang, U. M.: A survey of numerical linear algebra methods utilizing mixed-precision arithmetic, The Int. J. High Perform. C., 35, 344–369, https://doi.org/10.1177/10943420211003313, 2021.
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.
Brogi, F., Bnà, S., Boga, G., Amati, G., Esposti Ongaro, T., and Cerminara, M.: On floating point precision in computational fluid dynamics using OpenFOAM, Future Gener. Comp. Sy., 152, 1–16, https://doi.org/10.1016/j.future.2023.10.006, 2024.
Chantry, M., Thornes, T., Palmer, T., and Düben, P.: Scale-Selective Precision for Weather and Climate Forecasting, Mon. Weather Rev., 147, 645–655, https://doi.org/10.1175/MWR-D-18-0308.1, 2019.
Cotronei, A. and Slawig, T.: Single-precision arithmetic in ECHAM radiation reduces runtime and energy consumption, Geosci. Model Dev., 13, 2783–2804, https://doi.org/10.5194/gmd-13-2783-2020, 2020.
Düben, P. D. and Palmer, T. N.: Benchmark Tests for Numerical Weather Forecasts on Inexact Hardware, Mon. Weather Rev., 142, 3809–3829, https://doi.org/10.1175/MWR-D-14-00110.1, 2014.
Düben, P. D., McNamara, H., and Palmer, T. N.: The use of imprecise processing to improve accuracy in weather & climate prediction, J. Comput. Phys., 271, 2–18, https://doi.org/10.1016/j.jcp.2013.10.042, 2014.
Düben, P. D., Russell, F. P., Niu, X., Luk, W., and Palmer, T. N.: On the use of programmable hardware and reduced numerical precision in earth-system modeling, J. Adv. Model. Earth Sy., 7, 1393–1408, https://doi.org/10.1002/2015MS000494, 2015.
Fornaciari, W., Agosta, G., Cattaneo, D., Denisov, L., Galimberti, A., Magnani, G., and Zoni, D.: Hardware and Software Support for Mixed Precision Computing: a Roadmap for Embedded and HPC Systems, 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1–6, 2023.
Fu, H., Liao, J., Ding, N., Duan, X., Gan, L., Liang, Y., Wang, X., Yang, J., Zheng, Y., Liu, W., Wang, L., and Yang, G.: Redesigning CAM-SE for peta-scale climate modeling performance and ultra-high resolution on Sunway TaihuLight, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, 2017.
Fu, Z., Zhang, Y., Li, X., and Rong, X.: Intercomparison of Two Model Climates Simulated by a Unified Weather-Climate Model System (GRIST), Part I: Mean State, Clim. Dynam., https://doi.org/10.1007/s00382-024-07205-2, 2024.
Gan, L., Fu, H., Luk, W., Yang, C., Xue, W., Huang, X., Zhang, Y., and Yang, G.: Accelerating solvers for global atmospheric equations through mixed-precision data flow engine, 2013 23rd International Conference on Field programmable Logic and Applications, 2–4 September 2013, Porto, Portugal, 1–6, 2013.
GRIST-Dev: Mixed-Precision Computing in the GRIST Dynamical Core for Weather and Climate Modeling, Zenodo [code and data], https://doi.org/10.5281/zenodo.11229770, 2024.
Gu, J., Feng, J., Hao, X., Fang, T., Zhao, C., An, H., Chen, J., Xu, M., Li, J., Han, W., Yang, C., Li, F., and Chen, D.: Establishing a non-hydrostatic global atmospheric modeling system at 3-km horizontal resolution with aerosol feedbacks on the Sunway supercomputer of China, Sci. B., 67, 1170–1181, https://doi.org/10.1016/j.scib.2022.03.009, 2022.
Harris, L. M. and Lin, S.-J.: A Two-Way Nested Global-Regional Dynamical Core on the Cubed-Sphere Grid, Mon. Weather Rev., 141, 283–306, https://doi.org/10.1175/MWR-D-11-00201.1, 2012.
Jablonowski, C. and Williamson, D. L.: A baroclinic instability test case for atmospheric model dynamical cores, Q. J. Roy. Meteor. Soc., 132, 2943–2975, https://doi.org/10.1256/qj.06.12, 2006.
Jablonowski, C. and Williamson, D.: The Pros and Cons of Diffusion, Filters and Fixers in Atmospheric General Circulation Models, in: Lauritzen, P. H., Jablonowski, C., Taylor, M. A., and Nair, R. D., Numerical Techniques for Global Atmospheric Models, Lecture Notes in Computational Science and Engineering, Springer, 80, 381–493, 2011.
Kent, J., Ullrich, P. A., and Jablonowski, C.: Dynamical core model intercomparison project: Tracer transport test cases, Q. J. Roy. Meteor. Soc., 140, 1279–1293, https://doi.org/10.1002/qj.2208, 2013.
Klemp, J. B., Skamarock, W. C., and Park, S. H.: Idealized global nonhydrostatic atmospheric test cases on a reduced-radius sphere, J. Adv. Model. Earth Sy., 7, 1155–1177, https://doi.org/10.1002/2015MS000435, 2015.
Li, J. and Zhang, Y.: Enhancing the stability of a global model by using an adaptively implicit vertical moist transport scheme, Meteorol. Atmos. Phys., 134, 55, https://doi.org/10.1007/s00703-022-00895-5, 2022.
Li, X., Peng, X., and Zhang, Y.: Investigation of the effect of the time step on the physics–dynamics interaction in CAM5 using an idealized tropical cyclone experiment, Clim. Dynam., 55, 665–680, https://doi.org/10.1007/s00382-020-05284-5, 2020.
Li, X., Zhang, Y., Peng, X., Zhou, B., Li, J., and Wang, Y.: Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling, Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023, 2023.
Maynard, C. M. and Walters, D. N.: Mixed-precision arithmetic in the ENDGame dynamical core of the Unified Model, a numerical weather prediction and climate model code, Comput. Phys. Commun., 244, 69–75, https://doi.org/10.1016/j.cpc.2019.07.002, 2019.
Nakano, M., Yashiro, H., Kodama, C., and Tomita, H.: Single Precision in the Dynamical Core of a Nonhydrostatic Global Atmospheric Model: Evaluation Using a Baroclinic Wave Test Case, Mon. Weather Rev., 146, 409–416, https://doi.org/10.1175/MWR-D-17-0257.1, 2018.
Palmer, T.: The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years, Q. J. Roy. Meteor. Soc., 145, 12–24, https://doi.org/10.1002/qj.3383, 2019.
Palmer, T. N.: The physics of numerical analysis: a climate modelling case study, Philos. T. Roy. Soc. A, 378, 20190058, https://doi.org/10.1098/rsta.2019.0058, 2020.
Reed, K. A. and Jablonowski, C.: An Analytic Vortex Initialization Technique for Idealized Tropical Cyclone Studies in AGCMs, Mon. Weather Rev., 139, 689–710, https://doi.org/10.1175/2010mwr3488.1, 2011.
Reed, K. A. and Jablonowski, C.: Idealized tropical cyclone simulations of intermediate complexity: A test case for AGCMs, J. Adv. Model. Earth Sy., 4, M04001, https://doi.org/10.1029/2011MS000099, 2012.
Santos, F. F. D., Carro, L., Vella, F., and Rech, P.: Assessing the Impact of Compiler Optimizations on GPUs Reliability, ACM Trans. Archit. Code Optim., 21, 26, https://doi.org/10.1145/3638249, 2024.
Satoh, M., Tomita, H., Yashiro, H., Kajikawa, Y., Miyamoto, Y., Yamaura, T., Miyakawa, T., Nakano, M., Kodama, C., Noda, A. T., Nasuno, T., Yamada, Y., and Fukutomi, Y.: Outcomes and challenges of global high-resolution non-hydrostatic atmospheric simulations using the K computer, Prog. Earth Planet. Sci., 4, 13, https://doi.org/10.1186/s40645-017-0127-8, 2017.
Sergeev, D. E., Mayne, N. J., Bendall, T., Boutle, I. A., Brown, A., Kavčič, I., Kent, J., Kohary, K., Manners, J., Melvin, T., Olivier, E., Ragta, L. K., Shipway, B., Wakelin, J., Wood, N., and Zerroukat, M.: Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere, Geosci. Model Dev., 16, 5601–5626, https://doi.org/10.5194/gmd-16-5601-2023, 2023.
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S.-H., and Ringler, T. D.: A Multiscale Nonhydrostatic Atmospheric Model Using Centroidal Voronoi Tesselations and C-Grid Staggering, Mon. Weather Rev., 140, 3090–3105, https://doi.org/10.1175/MWR-D-11-00215.1, 2012.
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019.
Taylor, M., Caldwell, P. M., Bertagna, L., Clevenger, C., Donahue, A., Foucar, J., Guba, O., Hillman, B., Keen, N., Krishna, J., Norman, M., Sreepathi, S., Terai, C., White, J. B., Salinger, A. G., McCoy, R. B., Leung, L.-y. R., Bader, D. C., and Wu, D.: The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 29 August 2023, Denver, CO, USA, 2023.
Thornes, T., Düben, P., and Palmer, T.: On the use of scale-dependent precision in Earth System modelling, Q. J. Roy. Meteor. Soc., 143, 897–908, https://doi.org/10.1002/qj.2974, 2017.
Thuburn, J.: Some conservation issues for the dynamical cores of NWP and climate models, J. Comput. Phys., 227, 3715–3730, https://doi.org/10.1016/j.jcp.2006.08.016, 2008.
Tomita, H. and Satoh, M.: A new dynamical framework of nonhydrostatic global model using the icosahedral grid, Fluid Dynam. Res., 34, 357, https://doi.org/10.1016/j.fluiddyn.2004.03.003, 2004.
Ullrich, P. A., Melvin, T., Jablonowski, C., and Staniforth, A.: A proposed baroclinic wave test case for deep- and shallow-atmosphere dynamical cores, Q. J. Roy. Meteor. Soc., 140, 1590–1602, https://doi.org/10.1002/qj.2241, 2014.
Váňa, F., Düben, P., Lang, S., Palmer, T., Leutbecher, M., Salmond, D., and Carver, G.: Single Precision in Weather Forecasting Models: An Evaluation with the IFS, Mon. Weather Rev., 145, 495–502, https://doi.org/10.1175/MWR-D-16-0228.1, 2016.
Wang, Y., Li, X., Zhang, Y., Yuan, W., Zhou, Y., and Li, J.: Performance analysis of Precipitation Forecast by the baseline version of GRIST Global 0.125-degree weather model configuration, Chinese Journal of Atmospheric Sciences, https://doi.org/10.3878/j.issn.1006-9895.2309.22223, 2024 (in Chinese with English Abstract).
Wedi, N. P. and Smolarkiewicz, P. K.: A framework for testing global non-hydrostatic models, Q. J. Roy. Meteor. Soc., 135, 469–484, https://doi.org/10.1002/qj.377, 2009.
Wedi, N. P., Polichtchouk, I., Dueben, P., Anantharaj, V. G., Bauer, P., Boussetta, S., Browne, P., Deconinck, W., Gaudin, W., Hadade, I., Hatfield, S., Iffrig, O., Lopez, P., Maciel, P., Mueller, A., Saarinen, S., Sandu, I., Quintino, T., and Vitart, F.: A Baseline for Global Weather and Climate Simulations at 1 km Resolution, J. Adv. Model. Earth Sy., 12, e2020MS002192, https://doi.org/10.1029/2020MS002192, 2020.
Yang, C., Xue, W., Fu, H., You, H., Wang, X., Ao, Y., Liu, F., Gan, L., Xu, P., Wang, L., Yang, G., and Zheng, W.: 10M-Core Scalable Fully-Implicit Solver for Nonhydrostatic Atmospheric Dynamics, SC '16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 13–18 November 2016, Utah, Salt Lake City, 57–68, 2016.
Yin, F., Song, J., Wu, J., and Zhang, W.: An implementation of single-precision fast spherical harmonic transform in Yin–He global spectral model, Q. J. Roy. Meteor. Soc., 147, 2323–2334, https://doi.org/10.1002/qj.4026, 2021.
Yu, R., Zhang, Y., Wang, J., Li, J., Chen, H., Gong, J., and Chen, J.: Recent Progress in Numerical Atmospheric Modeling in China, Adv. Atmos. Sci., 36, 938–960, https://doi.org/10.1007/s00376-019-8203-1, 2019.
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015.
Zarzycki, C. M., Jablonowski, C., Kent, J., Lauritzen, P. H., Nair, R., Reed, K. A., Ullrich, P. A., Hall, D. M., Taylor, M. A., Dazlich, D., Heikes, R., Konor, C., Randall, D., Chen, X., Harris, L., Giorgetta, M., Reinert, D., Kühnlein, C., Walko, R., Lee, V., Qaddouri, A., Tanguay, M., Miura, H., Ohno, T., Yoshida, R., Park, S.-H., Klemp, J. B., and Skamarock, W. C.: DCMIP2016: the splitting supercell test case, Geosci. Model Dev., 12, 879–892, https://doi.org/10.5194/gmd-12-879-2019, 2019.
Zhang, Y.: Extending High-Order Flux Operators on Spherical Icosahedral Grids and Their Applications in the Framework of a Shallow Water Model, J. Adv. Model. Earth Sy., 10, 145–164, https://doi.org/10.1002/2017MS001088, 2018.
Zhang, Y. and Chen, H.: Comparing CAM5 and Superparameterized CAM5 Simulations of Summer Precipitation Characteristics over Continental East Asia: Mean State, Frequency–Intensity Relationship, Diurnal Cycle, and Influencing Factors, J. Climate, 29, 1067–1089, https://doi.org/10.1175/JCLI-D-15-0342.1, 2016.
Zhang, Y., Li, J., Yu, R., Zhang, S., Liu, Z., Huang, J., and Zhou, Y.: A Layer-Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration, J. Adv. Model. Earth Sy., 11, 1685–1714, https://doi.org/10.1029/2018MS001539, 2019.
Zhang, Y., Li, J., Yu, R., Liu, Z., Zhou, Y., Li, X., and Huang, X.: A Multiscale Dynamical Model in a Dry-Mass Coordinate for Weather and Climate Modeling: Moist Dynamics and Its Coupling to Physics, Mon. Weather Rev., 148, 2671–2699, https://doi.org/10.1175/MWR-D-19-0305.1, 2020.
Zhang, Y., Yu, R., Li, J., Li, X., Rong, X., Peng, X., and Zhou, Y.: AMIP Simulations of a Global Model for Unified Weather-Climate Forecast: Understanding Precipitation Characteristics and Sensitivity Over East Asia, J. Adv. Model. Earth Sy., 13, e2021MS002592, https://doi.org/10.1029/2021MS002592, 2021.
Zhang, Y., Li, X., Liu, Z., Rong, X., Li, J., Zhou, Y., and Chen, S.: Resolution Sensitivity of the GRIST Nonhydrostatic Model From 120 to 5 km (3.75 km) During the DYAMOND Winter, Earth Space Sci., 9, e2022EA002401, https://doi.org/10.1029/2022EA002401, 2022.
Zhang, Y., Li, J., Zhang, H., Li, X., Dong, L., Rong, X., Zhao, C., Peng, X., and Wang, Y.: History and Status of Atmospheric Dynamical Core Model Development in China, in: Numerical Weather Prediction: East Asian Perspectives, edited by: Park, S. K., Springer International Publishing, Cham, 3–36, 2023.
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.
This study explores strategies and techniques for implementing mixed-precision code optimization...