THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

FANG Chang-luan; ZHENG Qin

Abstract:

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the “on-off” switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization “on-off” switches in the forcing term, the impacts of “on-off” switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

Get Citation+

FANG Chang-luan, ZHENG Qin. THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL [J]. Journal of Tropical Meteorology, 2009, 15(1): 13-19.

Export:

Article Metrics

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

Institute of Science, PLA University of Science and Technology

Abstract: In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the “on-off” switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization “on-off” switches in the forcing term, the impacts of “on-off” switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

HTML

Reference (18)

[1]	LORENZ E N. A study of the predictability of a 28-variable atmospheric model [J]. Tellus, 1965, 17: 321-333.
[2]	MU M, DUAN W S, WANG B. Conditional nonlinear optimal perturbation and its applications [J]. Nonlin. Processes Geophy., 2003, 10: 493-501.
[3]	MU M, ZHANG Z Y. Conditional nonlinear optimal perturbation of a two-dimensional quasigeostrophic model [J]. J. Atmos. Sci., 2006, 63:1587-1604.
[4]	MU M, DUAN W S. A new approach to studying ENSO predictability: Conditional nonlinear optimal perturbation [J]. Chin. Sci. Bull., 2003, 48: 1045-1047.
[5]	MU M, JIANG Z N. A new approach to the generation of initial perturbations for ensemble prediction: Conditional nonlinear optimal perturbation [J]. Chin. Sci. Bull., 2007, 52: 1457-1462.
[6]	DUAN W S, LIU X C, ZHU K Y, MU M. Exploring the initial error that causes a significant spring predictability barrier for El Nino events [J]. J. Geophys. Res., 2009, Accepted.
[7]	MU M, XU H, DUAN W S. A kind of initial errors related to “spring predictability barrier” for El Nino event in Zebiak-Cane model [J]. Geophys. Res. Lett., 2007, 34, L03709, doi:10.1029/2006GL027 412.
[8]	DUAN W S, XUE F, MU M. Investigating a nonlinear characteristic of El Nino events by conditional nonlinear optimal perturbation [J]. Atmos. Res., 2008, doi:10.1016/j.atmosres.2008.09. 003.
[9]	DUAN W S, XU H, MU M. Decisive role of nonlinear temperature advection in El Nino and La Nina amplitude asymmetry [J]. J. Geophys. Res., 2008, 113, C01014, doi:10.1029/2006JC003974.
[10]	DUAN W S, MU M. Conditional nonlinear optimal perturbation: applications to stability, sensitivity and predictability [J]. Sci. in China (Ser. D), 2009, Accepted.
[11]	MU M, WANG H L, ZHOU F F. A preliminary application of conditional nonlinear optimal perturbation to adaptive observation [J]. Chin. J. Atmos. Sci., 2007, 31(6):1102-1112.
[12]	XU Q. Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples [J]. J. Atmos. Sci., 1996, 53: 1123-1142.
[13]	XU Q. Generalized adjoint for physical processes with parameterized discontinuities. Part II: Vector formulations and matching conditions [J]. J. Atmos. Sci., 1996, 53: 1143-1155.
[14]	ZOU X L. Tangent linear and adjoint of ‘on-off’ processes and their feasibility for use in 4-dimensional variational data assimilation [J]. Tellus, 1997, 49A: 3-31.
[15]	MU M, WANG J F. An adjoint method for variational data assimilation with physical “on-off” processes [J]. J. Atmos. Sci., 2003, 60: 2010-2018.
[16]	MU M, ZHENG Q. Zigzag oscillations in variational data assimilation with physical “on-off” processes [J]. Mon. Wea. Rev., 2005, 133: 2711-2720.
[17]	ZHENG Q, MU M. The effects of the model errors generated by discretization of “on-off” processes on VDA [J]. Nonlin. Processes Geophys., 2006, 13: 309-320.
[18]	ZHU J, KAMACHI M, ZHOU G Q. Nonsmooth optimization approaches to VDA of models with on/off parameterizations: Theoretical issues [J]. Adv. Atmos. Sci., 2002, 19(3): 405-424.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

Abstract:

References

Get Citation+

Share Article

Article Metrics

Proportional views

Manuscript History

Online:

通讯作者: 陈斌, bchen63@163.com