# Earth Virtualization Engines -- A Technical Perspective

arXiv: https://arxiv.org/abs/2309.09002 · HTML: https://mlsanity.com/paper/2309.09002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Learning for Post-Processing Ensemble Weather Forecasts](https://mlsanity.com/paper/2005.08748/) (2020)
- [Compressing multidimensional weather and climate data into neural networks](https://mlsanity.com/paper/2210.12538/) (2022)
- [SimFS: A Simulation Data Virtualizing File System Interface](https://mlsanity.com/paper/1902.03154/) (2019)

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

- [Artificial Intelligence for Atmospheric Sciences: A Research Roadmap](https://mlsanity.com/paper/2506.16281/) (2025)

## Abstract (arXiv)

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change.