Incremental 4-D Variational Data Assimilation, Efficient Optimization and Parameter Estimation Techniques

增量 4-D 变分数据同化、高效优化和参数估计技术

• 批准号: 9731472
• 负责人: Ionel Navon
• 金额: $ 30.94万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-05-15 至 2002-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9731472&HistoricalAwards=false
• 关键词: Incremental; Variational; Data; Assimilation; Efficient

9731472 Navon The overall objective of this project is to test and apply new approaches and mathematical methods for four-dimensional variational data assimilation, taking advantage of the availability of the adjoints of the full physics versions of global forecast models to improve the computational efficiency and representation of physical processes in the dynamical assimilation process. Dr. Navon will employ the Florida State University Global Spectral Model (FSU GSM), developed by Dr. Krishnamurti, as the primary model for the studies. The three major thrusts of this research are: (1) to develop more efficient methods for parameter estimations for key parameters identified by the modeling community as having a sizeable impact on model forecasting performance and physical initialization; (2) to evaluate the effectiveness of two computationally efficient methods for approximating the full 4-D variational process - the incremental approach and the multiple truncation incremental approach; and (3) to test the Discrete Truncated Newton method with memory, a new large-scale unconstrained minimization algorithm. The last will be done in collaboration with Dr. Fisher of ECMWF and Dr. Berger of OPTEAM, Ltd. Efficient and effective four-dimensional data assimilation is a critical component in the effort to improve numerical weather forecasting. This research has considerable potential for advancing that effort.

小行星9731472 该项目的总体目标是测试和应用四维变分数据同化的新办法和数学方法，利用全球预报模式的完整物理版本的伴随的可用性，以提高计算效率和动态同化过程中物理过程的表示。 Navon博士将采用由Krishnamurti博士开发的佛罗里达州立大学全球光谱模型（FSU GSM）作为研究的主要模型。 本研究的三个主要目标是：（1）为模型界认为对模型预报性能和物理初始化有相当大影响的关键参数开发更有效的参数估计方法;（2）评估两种计算效率高的方法近似全四维变分过程的有效性-增量法和多重截断增量法;（3）检验一种新的大规模无约束极小化算法--带记忆的离散截断牛顿法。 最后一项工作将与ECMWF的Fisher博士和OPTEAM有限公司的Berger博士合作完成。 高效和有效的四维数据同化是努力改善数值天气预报的一个关键组成部分。这项研究在推进这一努力方面具有相当大的潜力。