利用可解释人工智能与迁移学习在有限观测数据下理解并预测大西洋阻塞的维持
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data
AI总结：
本研究针对气候模型低估大西洋阻塞持续时间、观测数据有限导致预测困难的问题，结合CNN、SHAP与迁移学习，在理想模型与ERA5再分析数据中验证了机器学习提取阻塞前兆、提升有限数据下预测效果的潜力。
摘要：
阻塞事件是极端天气的重要诱因，尤其是能将天气系统滞留原地的长持续时间阻塞事件。然而，气候模型对阻塞事件的持续时间存在低估问题。可解释人工智能（Explainable Artificial Intelligence）是一类数据分析方法，可帮助识别阻塞事件延长的物理成因并诊断模型缺陷。我们在Marshall和Molteni（1993）开发的理想准地转模型上验证了该方法。我们训练了一个卷积神经网络（convolutional neural network, CNN），随后构建了一个以初始高压异常为条件的大西洋阻塞持续性稀疏预测模型。沙普利加性解释（Shapley Additive ExPlanation, SHAP）分析显示，被加拿大大西洋上空槽分隔的美国东南部和北大西洋高压异常，对大西洋地区持续阻塞事件的预测有显著贡献。这与此前通过波列分析在相同区域识别出前兆的研究结果一致。当我们将同一CNN应用于ERA5大气再分析资料中的阻塞事件时，由于数据不足，无法准确预测持续性阻塞。我们通过在Marshall-Molteni模型的海量数据上预训练CNN，再利用迁移学习（Transfer Learning），取得了优于直接训练的预测效果，部分克服了这一局限。通过迁移学习前后的SHAP分析，我们对比了再分析资料与准地转模型中的预测特征，量化了理想模型中的动力学偏差。本研究证明了机器学习方法在提取极端天气事件的有效前兆、并利用有限观测数据实现更优预测方面的潜力。
英文摘要：
Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic model developed by Marshall and Molteni (1993). We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high-pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high-pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre-training the CNN on the plentiful data of the Marshall-Molteni model, and then using Transfer Learning to achieve better predictions than direct training. SHAP analysis before and after transfer learning allows a comparison between the predictive features in the reanalysis and the quasigeostrophic model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
