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

合作研究：ITR/AP：致力于开发基于运算伴随方法的厄尔尼诺南方涛动集合预测技术

• 批准号: 0628690
• 负责人: Andrew Moore
• 金额: $ 8.37万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-03 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0628690&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; AP; Towards

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.

该研究项目将探索厄尔尼诺南方涛动（ENSO）基于伴随的集合预测方法的实用性，以支持开发新的ENSO业务集合预测方案。 PI 将使用 ENSO 耦合模型的层次结构，代表海洋-大气耦合环流模型 (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之外，他们还将这些技术与国家环境预测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。该研究建立在现有方法和想法的基础上，这些方法和想法已成功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。这些技术已成功应用于使用简单耦合模型的 ENSO 预测，然而，将现有想法应用于复杂的 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 预报，从而产生重要的社会效益。