# Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications

arXiv: https://arxiv.org/abs/2311.13691 · HTML: https://mlsanity.com/paper/2311.13691/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 2 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models](https://mlsanity.com/paper/2406.09474/) (2024)

## Abstract (arXiv)

We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations. Recommendation 1: Develop Hybrid AI-Physical Models: Emphasize the integration of AI and physical modeling for improved reliability, especially for longer prediction horizons, acknowledging the delicate balance between knowledge-based and data-driven components required for optimal performance. Recommendation 2: Emphasize Robustness in AI Downscaling Approaches, favoring techniques that respect physical laws, preserve inter-variable dependencies and spatial structures, and accurately represent extremes at the local scale. Recommendation 3: Promote Inclusive Model Development: Ensure Earth System Model development is open and accessible to diverse stakeholders, enabling forecasters, the public, and AI/statistics experts to use, develop, and engage with the model and its predictions/projections.