Evidence record 16922 · automatically gathered

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

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

How to cite this record

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).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.