中国物理B ›› 2016, Vol. 25 ›› Issue (5): 50502-050502.doi: 10.1088/1674-1056/25/5/050502
New data assimilation system DNDAS for high-dimensional models
Qun-bo Huang(皇群博), Xiao-qun Cao(曹小群), Meng-bin Zhu(朱孟斌), Wei-min Zhang(张卫民), Bai-nian Liu(刘柏年)
- 1. College of Computer, National University of Defense Technology, Changsha 410073, China;
2. Weather Center of PLA Air Force, Beijing 100843, China;
3. Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China
New data assimilation system DNDAS for high-dimensional models
Qun-bo Huang(皇群博)1,2,3, Xiao-qun Cao(曹小群)1,3, Meng-bin Zhu(朱孟斌)1,3, Wei-min Zhang(张卫民)1,3, Bai-nian Liu(刘柏年)1,3
- 1. College of Computer, National University of Defense Technology, Changsha 410073, China;
2. Weather Center of PLA Air Force, Beijing 100843, China;
3. Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China
摘要： The tangent linear (TL) models and adjoint (AD) models have brought great difficulties for the development of variational data assimilation system. It might be impossible to develop them perfectly without great efforts, either by hand, or by automatic differentiation tools. In order to break these limitations, a new data assimilation system, dual-number data assimilation system (DNDAS), is designed based on the dual-number automatic differentiation principles. We investigate the performance of DNDAS with two different optimization schemes and subsequently give a discussion on whether DNDAS is appropriate for high-dimensional forecast models. The new data assimilation system can avoid the complicated reverse integration of the adjoint model, and it only needs the forward integration in the dual-number space to obtain the cost function and its gradient vector concurrently. To verify the correctness and effectiveness of DNDAS, we implemented DNDAS on a simple ordinary differential model and the Lorenz-63 model with different optimization methods. We then concentrate on the adaptability of DNDAS to the Lorenz-96 model with high-dimensional state variables. The results indicate that whether the system is simple or nonlinear, DNDAS can accurately reconstruct the initial condition for the forecast model and has a strong anti-noise characteristic. Given adequate computing resource, the quasi-Newton optimization method performs better than the conjugate gradient method in DNDAS.
中图分类号: (Nonlinear dynamics and chaos)
- 05.45.-a