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

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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.