Compressing atmospheric data into its real information content

Journal article

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

Authors: Klöwer, M; Razinger, M; Dominguez, JJ; Düben, PD; Palmer, TN

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Computational Science
• Volume: 1
• Issue: 11
• Pages: 713-724
• Publication date: 2021-11-25
• DOI: 10.1038/s43588-021-00156-2
• EISSN: 2662-8457
• ISSN: 2662-8457
• Language: English
• Keywords: Data compression; Computer science; Compression (physics); Data mining; Lossless compression; Algorithm