AERIS

Argonne Earth Systems Model for Reliable and Skillful Predictions

Presentation at the 2025 ALCF Hands On HPC Workshop

Author

Affiliation

Sam Foreman

ALCF

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AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions

arXiv:2509.13523

Figure 1

High-Level Overview of AERIS

Figure 2: Rollout of AERIS model, specific humidity at 700m.

Table 1: Overview of AERIS model and training setup

Property	Description
Domain	Global
Resolution	0.25° & 1.4°
Training Data	ERA5 (1979–2018)
Model Architecture	Swin Transformer
Speedup1	O(10k–100k)

Model Overview

• Dataset: ECMWF Reanalysis v5 (ERA5)
• Variables: Surface and pressure levels
• Usage: Medium-range weather forecasting
• Partition:
  • Train: 1979–2018
  • Val: 2019
  • Test: 2020
• Data Size: 100GB at 5.6° to 31TB at 0.25°

Table 2: Variables used in AERIS training and prediction

Variable	Description
t2m	2m Temperature
X u(v)	$u$ ($v$) wind component @ Xm
q	Specific Humidity
z	Geopotential
msl	Mean Sea Level Pressure
sst	Sea Surface Temperature
lsm	Land-sea mask

Windowed Self-Attention

• Benefits for weather modeling:
  • Shifted windows capture both local patterns and long-range context
  • Constant scale, windowed self-attention provides high-resolution forecasts
  • Designed (currently) for fixed, 2D grids
• Inspiration from SOTA LLMs:
  • RMSNorm, SwiGLU, 2D RoPE

Figure 3: Windowed Self-Attention

Model Architecture: Details

Figure 4: Model Architecture

Sequence-Window-Pipeline Parallelism SWiPe

• SWiPe is a novel parallelism strategy for Swin-based Transformers
• Hybrid 3D Parallelism strategy, combining:
  • Sequence parallelism (SP)
  • Window parallelism (WP)
  • Pipeline parallelism (PP)

Figure 5

Figure 6: SWiPe Communication Patterns

🌌 Aurora

Table 3: Aurora Specs

Racks	166
Nodes	10,624
CPUs	21,248
GPUs	63,744
NICs	84,992
HBM	8 PB
DDR5c	10 PB

Figure 7: Aurora²: Fact Sheet.

🌎 AERIS: Scaling Results

Figure 8: AERIS: Scaling Results

• 10 EFLOPs (sustained) @ 120,960 GPUs
• See (Hatanpää et al. (2025)) for additional details
• arXiv:2509.13523

References

Hatanpää, Väinö, Eugene Ku, Jason Stock, Murali Emani, Sam Foreman, Chunyong Jung, Sandeep Madireddy, et al. 2025. “AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions.” https://arxiv.org/abs/2509.13523.

Footnotes

1. Relative to PDE-based models, e.g.: GFS

2. 🏆 Aurora Supercomputer Ranks Fastest for AI