New data assimilation system DNDAS for high-dimensional models

doi:10.1088/1674-1056/25/5/050502

Abstract 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.

Keywords: data assimilation dual-number optimization dual-number data assimilation system

Received: 17 November 2015
Revised: 07 January 2016
Accepted manuscript online:

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	02.60.-x	(Numerical approximation and analysis)
	02.30.Zz	(Inverse problems)
	02.60.Pn	(Numerical optimization)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 41475094 and 41375113).

Corresponding Authors: Qun-bo Huang
E-mail: hqb09@163.com

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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) New data assimilation system DNDAS for high-dimensional models 2016 Chin. Phys. B 25 050502

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