通过在线同步优化，让气候模型自动调参，提升预测精度。
Climate Model Tuning with Online Synchronization-Based Parameter Estimation
- 用在线同步方法动态调整气候模型内部参数。
- 新方法在挑战性场景下表现接近理想模型。
- 适合需要高精度气候模拟的研究者使用。
在气候科学中，气候模型的调参是一个计算密集型问题，主要源于系统状态的高维度和长时间积分。超级模型技术通过动态耦合多个模型，在短时间尺度上训练耦合权重，展现了降低气候模型偏差的潜力。本文提出一种新方法——自适应超级模型，对超级模型成员的内部参数进行调优。我们进行了三项实验：首先直接优化单个气候模型的内部参数；其次优化传统超级模型中两成员间的耦合权重；最后针对前两种方法难以应对的情况，采用自适应超级模型，其性能接近理想模型。
原文摘要 · Abstract (English)
In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of the system state and long integration times. Supermodelling is a technique which has shown the potential for reducing climate model biases by dynamically coupling multiple models together, and training their coupling on a short timescale. Here, we introduce a new approach called \emph{adaptive supermodeling}, where the internal model parameters of the member of a supermodel are tuned. We perform three experiments. We first directly optimize the internal parameters of a climate model. We then optimize the weights between two members of a supermodel in a classical supermodel approach. For a case designed to challenge the two previous methods, we implement adaptive supermodeling, which achieves a performance similar to a perfect model.
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