AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions
Authors: name: Väinö Hatanpää, name: Eugene Ku, name: Jason Stock, name: Murali Emani, name: Sam Foreman, name: Chunyong Jung, name: Sandeep Madireddy, name: Tung Nguyen, name: Varuni Sastry, name: Ray A. O. Sinurat, name: Sam Wheeler, name: Huihuo Zheng, name: Troy Arcomano, name: Venkatram Vishwanath, name: Rao Kotamarthi

Generative machine learning offers new opportunities to better understand
complex Earth system dynamics. Recent diffusion-based methods address spectral
biases and improve ensemble calibration in weather forecasting compared to
deterministic methods, yet have so far proven difficult to scale stably at high
resolutions. We introduce AERIS, a 1.3 to 80B parameter pixel-level Swin
diffusion transformer to address this gap, and SWiPe, a generalizable technique
that composes window parallelism with sequence and pipeline parallelism to
shard window-based transformers without added communication cost or increased
global batch size. On Aurora (10,080 nodes), AERIS sustains 10.21 ExaFLOPS
(mixed precision) and a peak performance of 11.21 ExaFLOPS with 1 times 1
patch size on the 0.25{\deg} ERA5 dataset, achieving 95.5% weak scaling
efficiency, and 81.6% strong scaling efficiency. AERIS outperforms the IFS ENS
and remains stable on seasonal scales to 90 days, highlighting the potential of
billion-parameter diffusion models for weather and climate prediction.