Collaborative Research: CMG: Data Assimilation by Synchronization of Truth and Model

合作研究：CMG：通过真理与模型同步进行数据同化

• 批准号: 0328402
• 负责人: Eugenia Kalnay
• 金额: $ 17.35万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0328402&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Data; Assimilation

This project explores the idea of developing an approach to data assimilation based on methods for synchronizing two chaotic systems, one being the real atmosphere and the other being a numerical forecast model of the atmosphere. The research will progress from simple low-dimensional models to models more characteristic of weather models. One of the synchronization methods will be based on bred vectors.Innovations in data assimilation that increase the efficiency or effectiveness of environmental forecasting systems might have a number of important applications such as the prediction of terrestrial and space weather, coastal ocean currents and ecosystems, or surface and sub-surface hydrology.

本项目探讨了基于同步两个混沌系统（一个是真实大气，另一个是大气的数值预报模型）的方法开发数据同化方法的想法。研究将从简单的低维模型发展到更具有天气模型特征的模型。其中一种同步方法是基于杂交载体。数据同化方面的创新提高了环境预报系统的效率或效力，可能有许多重要的应用，例如对陆地和空间天气、沿海洋流和生态系统、或地表和地下水文的预测。