用更长的时间步训练，让机器学习模型在地球系统模拟中更稳健。
Regularization of ML models for Earth systems by using longer model timesteps
- 通过延长模型时间步，自然增强对混沌系统的正则化效果。
- 在ORAS5数据上验证，28天时间步比常用设置更真实。
- 适合做地球系统建模的科研人员，尤其关注泛化能力提升者。
正则化是提升机器学习模型泛化能力的常用技术。传统方法是在输入相似但输出不同的数据上训练，防止模型过度自信。本文发现，在模拟混沌的地球系统时，使用更长的时间步可自然带来此类正则化。我们在两个领域验证了这一现象：解释了更长时间步如何改善模型表现，并证明增强正则化是其中关键原因。我们提出了选择合适模型时间步的流程，并在ORAS5海洋再分析数据上进行了基准测试，结果表明28天时间步比通常采用的更合理，能生成更真实的模拟。由于地球系统具有混沌特性，这种正则化易于实现，未来在地球系统建模中将有广泛应用前景。
原文摘要 · Abstract (English)
Regularization is a technique to improve generalization of machine learning (ML) models. A common form of regularization in the ML literature is to train on data where similar inputs map to different outputs. This improves generalization by preventing ML models from becoming overconfident in their predictions. This paper shows how using longer timesteps when modelling chaotic Earth systems naturally leads to more of this regularization. We show this in two domains. We explain how using longer model timesteps can improve results and demonstrate that increased regularization is one of the causes. We explain why longer model timesteps lead to improved regularization in these systems and present a procedure to pick the model timestep. We also carry out a benchmarking exercise on ORAS5 ocean reanalysis data to show that a longer model timestep (28 days) than is typically used gives realistic simulations. We suggest that there will be many opportunities to use this type of regularization in Earth system problems because the Earth system is chaotic and the regularization is so easy to implement.
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