arXiv:2606.31248v1 Announce Type: cross Abstract: Kilometer-scale convection shapes precipitation extremes, tropical organization, and cloud feedbacks, but most global atmospheric models approximate these processes at 25-100 km resolution. Global storm-resolving physics models resolve convective systems explicitly, but at a cost -- roughly one MWh per simulated day on exascale supercomputers -- that limits long-duration simulation. We introduce STRATA (Storm-resolving Tile-based autoRegressive Atmosphere Transformer Architecture), the first autoregressive AI emulator for global storm-resolving
The development of STRATA suggests a maturing of AI models capable of handling complex physical simulations at high resolution, coinciding with increasing computational power.
This breakthrough could significantly improve climate modeling and weather prediction, crucial for economic planning, disaster preparedness, and understanding climate change impacts.
Global storm-resolving atmospheric simulations, previously limited by exascale supercomputing costs and time, can now potentially be performed much more efficiently and broadly using AI emulators.
- · Climate scientists
- · Insurance industry
- · Agriculture sector
- · Supercomputing centers
- · Traditional physical modeling approaches (if not integrated with AI)
More accurate and accessible long-term weather forecasting and climate projections for governments and businesses.
Improved infrastructure planning and resource allocation in areas sensitive to extreme weather events.
Potentially a deeper understanding of climate tipping points and the development of more effective mitigation and adaptation strategies at a global scale.
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Read at arXiv cs.LG