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

本研究将探讨基于伴随方法的厄尔尼诺-南方涛动（ENSO）集合预测方法的应用，以期为建立新的ENSO业务集合预测方案提供支持。pi将使用一系列耦合ENSO模式，这些模式代表了耦合海洋-大气环流模式（GCM）的不同近似水平。除了探索基于奇异向量和随机最优的集合预测技术外，他们还将这些技术与目前在国家环境预测中心用于集合天气预测的繁殖模式方法进行比较。集合预测实验将使用传统的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方法和思想的基础上，这些方法和思想已成功地应用于利用最先进的复杂大气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使用简单耦合模型的ENSO预测，然而，将现有思想应用于复杂的ENSO预测提出了一些需要解决的重大新挑战。这项工作很重要，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.