谷歌的天气模型GraphCast被拿去预报火星天气了，微调10轮就能预测10天火星温度，未来能帮火星任务避沙尘暴。
研究者将地球天气基础模型GraphCast迁移到火星，使用火星气候数据库（MCD）训练，评估其对火星温度和风场的预测能力。零样本预测能大致还原当前状态，但无法捕捉昼夜变化，且会快速退化为气候均值。微调后模型在10个训练周期内学会火星热力变化，可预报10天内的季节和垂直温度结构。预测质量随训练样本增加而提升，并对季节初始化敏感。
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 GraphCast predictions of Martian temperature and wind fields. Zero-shot forecasts produce a surprisingly accurate depiction of current conditions but fail to reproduce diurnal variability and rapidly decay toward climatological mean states. To address this limitation, we fine-tune GraphCast using MCD variables and top-of-atmosphere solar radiation forcing while holding humidity constant. Fine-tuning enables rapid learning of Martian thermal variability. Within as few as 10 training epochs, the model begins to capture the diurnal cycle and forecasts up to 10 days reproduce seasonal and vertical temperature structure. Prediction quality improves with training sample size and exhibits sensitivity to seasonal initialization. These results demonstrate that Earth-trained AI weather models can be adapted to simulate Martian atmospheric dynamics, providing a pathway toward rapid planetary weather prediction to support mission operations, dust storm risk mitigation, and future human exploration.