{
  "id": 16922,
  "url": "https://arxiv.org/abs/2608.05054v1",
  "title": "MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres",
  "summary": "We investigate the transferability of Earth weather foundation models to planetary atmospheres by adapting the GraphCast graph neural weather forecasting model to Mars. While GraphCast achieves state-of-the-art performance for terrestrial forecasting, its applicability to non-Earth environments remains unexplored. Using the Mars Climate Database (MCD), which provides global atmospheric fields across vertical altitude levels (similar to Earth pressure levels), we evaluate zero-shot and fine-tuned",
  "authors": "M. L. Carroll, J. Li, S. D. Guzewich, G. Villanueva, J. A. Caraballo-Vega, M. J. Frost",
  "category": "research",
  "topics": "environment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T17:03:13.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16922",
  "original_url": "https://arxiv.org/abs/2608.05054v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}