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    "story_id": "mp-2026-08-06-001",
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    "headline": "The Weather Model Learned Another Sky",
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    "dek": "An Earth-trained graph forecaster lost Mars's daily rhythm until researchers taught it the planet's variables and the Sun's changing input.",
    "summary": "An Earth-trained graph forecaster lost Mars's daily rhythm until researchers taught it the planet's variables and the Sun's changing input.",
    "body_text": "MarsCast adapts GraphCast, a model developed for terrestrial weather, to temperature and wind fields from the Mars Climate Database. In zero-shot tests, the transferred model captured the initial atmospheric state but its forecasts decayed toward climatology and lost much of the day-night variation. Fine-tuning with Mars-specific variables and top-of-atmosphere solar forcing recovered a diurnal cycle within ten training epochs; the authors report forecasts out to ten days that reproduce seasonal and vertical temperature structure. These are preprint experiments against a climate database, not operational forecasts or validation against a new observing campaign.",
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      "These are preprint experiments against a climate database, not operational forecasts or validation against a new observing campaign."
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        "text": "An Earth-trained graph forecaster lost Mars's daily rhythm until researchers taught it the planet's variables and the Sun's changing input.",
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    "tags": [
      "Mars",
      "weather forecasting",
      "graph neural networks",
      "transfer learning"
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      "title": "arXiv preprint 2608.05054",
      "publisher": "arXiv",
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    "title": "The Weather Model Learned Another Sky",
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    "published_at": "2026-08-06T09:00:00.000-04:00",
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