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: 0327932
• 负责人: Ljupco Kocarev
• 金额: $ 35万
• 依托单位: University of California-San Diego
• 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 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0327932&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.

该项目探讨了在同步两个混沌系统方法的基础上开发数据同化方法的想法，一个是真实的大气，另一个是大气的数值预报模型。 研究将从简单的低维模式发展到更具有天气模式特征的模式。 同步方法之一将以繁殖矢量为基础，可提高环境预报系统效率或效力的数据同化创新可能有一些重要的应用，如预测陆地和空间天气、沿海洋流和生态系统或地表和次地表水文学。