arXiv:2606.28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost. However, existing data-driven approaches are limited in their ability to model coupled Earth system dynamics, which is required for extending predictability beyond the ~2-week horizon. To address this, we introduce NIVA, a multimodal foundation model designed to learn unified representations across Earth system components. While the full framework targets atmosphere, ocean, ice, and land interactions, we focus here on a two-mo
Advances in AI-driven weather and climate modeling have created the foundation for more comprehensive Earth system intelligence, pushing the boundary of predictability.
A comprehensive multimodal foundation model for Earth systems can significantly improve long-range environmental predictions, impacting resource management, disaster preparedness, and climate policy.
The ability to model coupled Earth system dynamics with greater accuracy and extended predictability beyond short-term horizons is now being actively pursued through advanced AI models.
- · Climate scientists
- · Governments (disaster preparedness)
- · Agriculture sector
- · Risk assessment & insurance
- · Traditional forecasting models
- · Sectors reliant on short-term reactive planning
Improved long-range weather and climate forecasts become available.
Better planning and adaptation strategies can be developed for extreme weather events and climate change impacts.
Enhanced Earth system intelligence may inform geopolitical resource management and contribute to global stability or competition over climate-sensitive regions.
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Read at arXiv cs.LG