Earth Virtualization Engines: A Technical Perspective

被引:6
作者
Hoefler T. [1 ]
Stevens B. [2 ]
Prein A.F. [3 ]
Baehr J. [4 ]
Schulthess T. [5 ]
Stocker T.F. [6 ]
Taylor J. [7 ]
Klocke D. [2 ]
Manninen P. [8 ]
Forster P.M. [9 ]
Kolling T. [2 ]
Gruber N. [1 ]
Anzt H. [10 ]
Frauen C.
Ziemen F. [11 ]
Klower M. [12 ]
Kashinath K. [13 ]
Schar C. [14 ]
Fuhrer O. [15 ]
Lawrence B.N. [16 ]
机构
[1] Eth Zurich, Zurich
[2] Max Planck Institute for Meteorology, Hamburg
[3] National Center for Atmospheric Research, Boulder, 80301, CO
[4] Universität Hamburg, Hamburg
[5] Eth Zurich and Swiss National Supercomputing Center, Lugano
[6] University of Bern, Bern
[7] Commonwealth Scientific Industrial Research Organisation, Canberra, 2601, ACT
[8] CSC-IT Center for Science, Espoo
[9] University of Leeds, Leeds
[10] University of Tennessee, Knoxville, 37996, TN
[11] German Climate Computing Center, Hamburg
[12] Massachusetts Institute of Technology, Cambridge, 02139, MA
[13] Nvidia Corporation, Santa Clara, 95051, CA
[14] Eth Zurich, Atmospheric and Climate Science, Zurich
[15] Federal Office of Meteorology and Climatology MeteoSwiss, Zurich
[16] University of Reading, Reading
关键词
Climate change - Climate models - Earth (planet) - Earth system models - Engines - Learning systems - Virtual reality;
D O I
10.1109/MCSE.2023.3311148
中图分类号
学科分类号
摘要
Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At its core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change. © 1999-2011 IEEE.
引用
收藏
页码:50 / 59
页数:9
相关论文
共 15 条
[1]  
Prein A.F., Et al., A review on regional convectionpermitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 2, pp. 323-361, (2015)
[2]  
Lorenz E., Predictability: Does the flap of a butterfly's wing in Brazil set off a tornado in Texas?, (1972)
[3]  
Klinker E., Et al., The ECMWF operational implementation of four-dimensional variational assimilation. III: Experimental results and diagnostics with operational configuration, Quart. J. Roy. Meteorol. Soc., 126, 564, pp. 1191-1215, (2000)
[4]  
Hersbach H., Et al., The ERA5 global reanalysis, Quart. J. Roy. Meteorol. Soc., 146, 730, pp. 1999-2049, (2020)
[5]  
Forster P., Et al., The Earth's energy budget, climate feedbacks, and climate sensitivity, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 923-1054, (2021)
[6]  
Schar C., Et al., Kilometer-scale climate models: Prospects and challenges, Bull. Amer. Meteorol. Soc., 101, 5, pp. E567-E587, (2020)
[7]  
Schneider T., Et al., Climate goals and computing the future of clouds, Nature Clim. Change, 7, 1, pp. 3-5, (2017)
[8]  
Gronquist P., Et al., Deep learning for post-processing ensemble weather forecasts, Philos. Trans. Roy. Soc. A, 379, 2194, (2021)
[9]  
Schulthess T.C., Et al., Reflecting on the goal and baseline for exascale computing: A roadmap based on weather and climate simulations, Comput. Sci. Eng., 21, 1, pp. 30-41, (2019)
[10]  
Huang L., Hoefler T., Compressing multidimensional weather and climate data into neural networks, (2022)