发表机构
NASA Goddard Space Flight Center; ADNet Systems, Inc.（美国国家航空航天局戈达德太空飞行中心; ADNet系统公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将地球天气基础模型GraphCast适配为火星版本MarsCast，经微调后可模拟火星大气动力学，为行星天气预报及相关任务提供支持。
AI 中文摘要
我们研究地球天气基础模型向行星大气的迁移能力，通过将GraphCast图神经网络天气预报模型适配到火星来开展相关工作。GraphCast在地球天气预报中达到了顶尖性能，但其在非地球环境中的适用性尚未得到探索。我们利用火星气候数据库（Mars Climate Database, MCD），该数据库提供了垂直高度层面（类似地球的气压层面）的全球大气场，对GraphCast在零样本学习和微调后的火星温度与风场预测进行评估。零样本预测能产生令人惊讶的当前状态准确描述，但无法重现日变化性，且会迅速衰减至气候平均态。为解决这一局限，我们使用MCD变量和大气层顶太阳辐射强迫对GraphCast进行微调，同时保持湿度恒定。微调使模型能快速学习火星的热变化性，仅需10个训练轮次，模型便开始捕捉日循环，且长达10天的预测能重现季节和垂直温度结构。预测质量随训练样本量提升而改善，且对季节初始化敏感。这些结果表明，在地球训练的AI天气模型可被适配以模拟火星大气动力学，为快速行星天气预报提供了途径，以支持任务运行、沙尘暴风险缓解及未来人类探索。
英文摘要
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.
