一种基于Koopman谱分析的可预测性更强的马登-朱利安振荡指数
A more predictable Madden-Julian Oscillation index derived from Koopman spectral analysis
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中文总结 AI 辅助
本研究利用Koopman算子谱分析定义MJO指数，其演变更平滑，预测技巧达46天，远超RMM的11天，可补充现有MJO诊断以改进延伸期预报。
中文摘要 AI 辅助
马登-朱利安振荡（MJO）是次季节到季节（S2S）可预测性的主要来源。MJO通常使用诸如实时多变量MJO（RMM）指数等指标进行定义和追踪。尽管RMM提供了对MJO的有用描述，但其演变可能嘈杂且难以预测。我们利用Koopman算子的数据驱动近似定义了一个MJO指数。该Koopman指数捕捉到与RMM相似的热带环流和对流模式，但演变更加平滑和可预测。在相同的预测框架下，Koopman指数的技巧性预测可延伸至46天，而RMM仅为11天。虽然这种新方法在恢复RMM方面不如业务化S2S模型（其提供长达35天的技巧性预报），但Koopman指数可补充现有的MJO诊断方法，用于评估和开发延伸期预报系统。
英文摘要
The Madden-Julian oscillation (MJO) is a major source of subseasonal-to-seasonal (S2S) predictability. The MJO is commonly defined and tracked with indices such as the Real-time Multivariate MJO (RMM) index. Although the RMM provides a useful description of the MJO, its evolution can be noisy and difficult to predict. We define an MJO index using a data-driven approximation of the Koopman operator. The Koopman index captures similar tropical circulation and convection patterns to the RMM but evolves more smoothly and predictably. Skillful prediction extends to 46 days for the Koopman index compared to 11 days for the RMM under the same prediction framework. While this new approach does not recover the RMM as well as operational S2S models, which provide skillful forecasts up to 35 days, the Koopman index could complement existing MJO diagnostics in evaluating and developing extended-range forecast systems.
