Collaborative Research: ITR/AP: Towards The Development Of Operational Adjoint Method Based Ensemble Prediction Techniques For El Nino Southern Oscillation

这项研究将探索基于伴随的集合预报方法在厄尔尼诺南方涛动(ENSO)预报中的应用，以期为发展新的ENSO业务集合预报方案提供支持。PIS将使用代表海洋-大气环流耦合模式(GCM)不同近似程度的ENSO耦合模式的层次结构。除了探索基于奇异向量和随机最优的集合预报技术外，他们还将把这些技术与国家环境预报中心目前用于集合天气预报的繁殖模式方法进行比较。集合预报试验将使用常规统计技术和基于信息论的新理论框架进行分析。这项研究建立在现有的方法和思想的基础上，这些方法和想法已经成功地应用于使用复杂的最先进的大气GCM进行5-10天时间尺度的数值天气预报。这些技术已经成功地应用于简单耦合模式的ENSO预报，然而，将现有的思想应用于复杂的ENSO预报提出了一些需要解决的重大新挑战。这项工作之所以重要，是因为它有可能改善ENSO预测，这具有重要的社会效益。

This research project will explore the utility of adjoint-based methods of ensemble prediction for El Nino Southern Oscillation (ENSO), with the view to support the development of a new operational ensemble prediction scheme for ENSO. The PIs will use a hierarchy of coupled ENSO models that represent different levels of approximation of coupled ocean-atmosphere General Circulation Models (GCM). In addition to exploring singular vector and stochastic optimal based ensemble prediction techniques, they will compare these techniques with the method of bred modes that is currently used for ensemble weather prediction at the National Centers for Environmental Prediction. The ensemble prediction experiments will be analyzed using conventional statistical techniques and using a new theoretical framework based on information theory. The research builds on existing methods and ideas that have been applied successfully to Numerical Weather Prediction on timescales of 5-10 days using complex state-of-the-art atmospheric GCMs. The techniques have been applied successfully to ENSO prediction using simple coupled models, however, the application of existing ideas to complex ENSO prediction presents some significant new challenges that need to be addressed. The work is important because it has the potential to improve ENSO forecasts, which has important societal benefits.