Regularization of ML models for Earth systems by using longer model timesteps
Regularization of ML models for Earth systems by using longer model timesteps
海洋学大气科学（气象学）地球物理学
Raghul Parthipan,Mohit Anand,Hannah M Christensen,Frederic Vitart,Damon J Wischik,Jakob Zscheischler.Regularization of ML models for Earth systems by using longer model timesteps[EB/OL].(2025-03-23)[2025-10-25].https://arxiv.org/abs/2503.18023.点此复制
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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