Skillful statistical prediction of subseasonal temperature by training on dynamical model data
Skillful statistical prediction of subseasonal temperature by training on dynamical model data
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DOI：
10.1017/eds.2023.2
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
L. Trenary;T. DelSole
中科院分区：
文献类型：
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作者：
L. Trenary;T. DelSole
摘要本文建立了预测美国西部冬季亚季节温度的统计模型。统计模型在两个独立的数据集上进行训练，即观测和动态模型模拟，并基于最小绝对收缩和选择算子（lasso）。令人惊讶的是，经过动态模型模拟训练的统计模型比经过观测训练的模型能更好地预测观测结果。其中一个原因是，模拟所涉及的数据比观测数据集要多得多。
Abstract This paper derives statistical models for predicting wintertime subseasonal temperature over the western US. The statistical models are trained on two separate datasets, namely observations and dynamical model simulations, and are based on least absolute shrinkage and selection operator (lasso). Surprisingly, statistical models trained on dynamical model simulations can predict observations better than observation-trained models. One reason for this is that simulations involve orders of magnitude more data than observational datasets.