An AI-Enhanced 1km-Resolution Seamless Global Weather and Climate Model to Achieve Year-Scale Simulation Speed using 34 Million Cores

作者：Xiaohui Duan, Yi Zhang, Kai Xu, Haohuan Fu, Bin Yang, Yiming Wang, Yilun Han, Siyuan Chen, Zhuangzhuang Zhou, Chenyu Wang, Dongqiang Huang, Huihai An, Xiting Ju, Haopeng Huang, Zhuang Liu, Wei Xue, Weiguo Liu, Bowen Yan, Jianye Hou, Maoxue Yu, Wenguang Chen, Jian Li, Zhao Jing, Hailong Liu, Lixin Wu · 年份：2025 · DOI：10.1145/3710848.3710893 · 被引用次数：6 · 研究领域：Meteorological Phenomena and Simulations、Climate variability and models、Cryospheric studies and observations

Global Storm Resolving Models (GSRMs) is crucial for understanding extreme weather events under the climate change background. In this study, we optimize Global-Regional Integrated Forecast System (GRIST), which is a unified weather-climate modeling system designed for research and operation, for the next-generation Sunway supercomputer, incorporating AI-enhanced physics suite, OpenMP-based parallelization, and mixed-precision optimizations to enhance both efficiency and performance portability, as well as the unified modeling capability. Our experiments successfully capture significant events during the "23.7" extreme rainfall over northern China influenced by super Typhoon Doksuri, at 1km resolution. Notably, our work scales to 34 million cores, enabling simulation speeds at 491 SDPD (3km) and 181 SDPD (1km).