MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres
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
Record details
Published: 5 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Environment · Finance, VC & PE
Retrieved: 6 August 2026
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ethics.ai (5 August 2026), “MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres,” evidence record 16922, https://ethics.ai/record/16922 (originally published by arXiv cs.AI).
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