论文标题
用于数据驱动预测的元模型策略
Meta-modeling strategy for data-driven forecasting
论文作者
论文摘要
准确地预测天气是缓解气候变化的关键要求。数据驱动的方法提供了进行更准确的预测的能力，但缺乏可解释性，如果模型未仔细开发，则训练和部署可能很昂贵。在这里，我们利用两个历史气候数据集和机器学习工具，以准确预测温度场。此外，我们能够使用廉价的训练和评估的低忠诚模型，有选择地避免昂贵的高保真功能评估，并揭示了预测能力的季节性变化。这允许制定自适应训练策略，以进行计算有效的地球物理仿真。
Accurately forecasting the weather is a key requirement for climate change mitigation. Data-driven methods offer the ability to make more accurate forecasts, but lack interpretability and can be expensive to train and deploy if models are not carefully developed. Here, we make use of two historical climate data sets and tools from machine learning, to accurately predict temperature fields. Furthermore, we are able to use low fidelity models that are cheap to train and evaluate, to selectively avoid expensive high fidelity function evaluations, as well as uncover seasonal variations in predictive power. This allows for an adaptive training strategy for computationally efficient geophysical emulation.
