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

9731472 Navon这个项目的总体目标是测试和应用四维变分数据同化的新方法和数学方法，利用全球预报模式的完整物理版本的伴随项来改进动力同化过程中物理过程的计算效率和表示。纳文博士将采用克里希纳穆蒂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。这项研究的三个主要目的是：(1)开发更有效的方法来估计被模型界认为对模型预测性能和物理初始化有相当大影响的关键参数的参数估计；(2)评估两种计算高效的方法来逼近完整的四维变分过程的有效性-增量方法和多次截断增量方法；(3)测试带记忆的离散截断牛顿法，一种新的大规模无约束最小化算法。最后一项将与ECMWF的Fisher博士和OPTEAM，Ltd.的Berger博士合作完成。高效和有效的四维数据同化是努力改进数值天气预报的关键组成部分。这项研究对推动这一努力具有相当大的潜力。

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.