Nonlinear optimization method in the atmosphere - in marine scientific research several applications dedicated to Professor
Abstract
Keywords
Full Text:
PDFReferences
Lorenz E. Climate predictability:the Physical basis of Climate modeling. Global Atmosphere Programme (GARP) Publication Series, World Meteorological Organisation (WMO), 1975, 16:132-136
Mu M, Duan W S, Chou J. Recent advances in predictability studies in (1999-2002). ADV Atmos Sci, 21:437-443
Palmer T, Hagedorn R. Predictability of Weather and Climate. Cambridge:cambridge University Press, 2006
tennekes H. Karl Popper and the accountability of numerical forecasting. In:new developments in predictabil-ity. ECMWF Workshop Proceedings, ECMWF, Shinfield Park, Reading, Royaume-uni. London:european Centre for Medium-range Weather Forecasts, 1991, 21-28
Lorenz E N. A study of the predictability of a 28-variable atmospheric model. Tellus, 1965, 17:321-333
Farrell B F. Small error dynamics and the predictability of atmospheric flows. J Atmos Sci, 1990, 47:2409-2416
Thompson C J. Initial conditions for optimal growth in a coupled ocean-atmosphere model of ENSO. J Atmos Sci, 1998, 55:537-557
Zanna L, Heimbach P, Moore A M, et al Optimal excitation of interannual Atlantic meridional overturning Circu-la tion variability. J Climate, 24:413-427
Palmer T N, Zanna L. Singular vectors, predictability and ensemble for prediction. J Phys A, 2013, 46:254018
Ten Moolenaar H E, Selten F m. Finding effective parameter perturbations in atmospheric models:the LORENZ63 MoD El as case study. Tellus, 56:47-55
One Barkmeijer J, Iversen t, Palmer t N. Forcing singular vector and other sensitivity model structures. Q J Roy Meteor Soc, 2003, 129:2401-2423
Mu M, Duan W, Wang B. Conditional Nonlinear optimal perturbation and its applications. Nonlinear Process Geophys, 2003, 10:493-501
Cherubini S, De Palma P, Robinet J C, et al. Rapid path to transition via nonlinear localised optimal Pertur-bati ONS in a boundary layer flow. Phys Rev E (3), 82:066302
Pringle C T, Kerswell R. Using Nonlinear transient growth to construct the minimal seed for shear flow Tur-bu Lence. Phys Rev Lett, 105:154502
Duan W S, Liu X C, Zhu K Y, et al exploring the initial errors that cause a significant "spring predictabil-ity Barrier "for El Nino events. J Geophys Res, 2009, 114:c04022
Mu M, Duan W, Wang B. Season-dependent Dynamics of Nonlinear optimal error growth and El Nino-southern Oscilla tion predictability in a theoretical model. J Geophys Res, 2007, 112:d10113
Terwisscha van Scheltinga a D, Dijkstra H A. Conditional nonlinear optimal perturbations of the Double-gyre Ocea N Circulation. Nonlinear Process Geophys, 2008, 15:727-734
Mu M, Sun L, Dijkstra H A. The sensitivity and stability of the ocean ' s thermocline circulation to finite ampli-tude freshwater perturbations. J phys oceanogr, 34:2305-2315
Sun L, Mu M, Sun D J, et al. Passive mechanism decadal variation of thermohaline circulation. J Geophys Res, 110:c07025
Wang Q, Mu M, Dijkstra H A. Effects of nonlinear physical processes on optimal error growth in predictability ex Periments of the Kuroshio large meander. J Geophys Res, 2013, 118:6425-6436
Mu M, Duan W S, Wang Q, et al. An extension of conditional optimal perturbation approach and its applica-tions. Nonlinear Process Geophys, 17:211-220
Duan W S, Zhou F. non-linear forcing singular vector of a two-dimensional quasi-geostrophic model. Tellus, 2013, 65:18452
Wang Q, Mu M. A new application of conditional nonlinear optimal perturbation approach to boundary condi-tion. J Geophys Res, 2015, 120:7979-7996
Mumu , segment late lock . The application of conditional nonlinear optimal perturbation in the research nonpo-rous of predictability problems . Atmospheric Science ,2013,37:281-296
Duan W S, Huo Z H. Generating mutually independent initial perturbations for ensemble forecasts:orthogonal conditional Nonlin Ear optimal perturbations. J Atmos Sci, 2016, 73:997-1014
Ehrendorfer M, Tribbia J. Optimal prediction of forecast error covariances through singular. J Atmos Sci 1997, 53:286-313
birgin E G, Martinez J m, Raydan m. Nonmonotone spectral projected gradient methods on convex sets. SIAM J Optim, 10:1196-1211
Powell M J vmcwd:a FORTRAN subroutine for constrained optimization. ACM sigmap Bull, 1983, 32:4-16
Wang B, Tan X W. A Fast algorithm for solving CNOP and associated target observation tests. Acta Meteorol Sin, 2009, 23:387-402
Sun G D, Mu M. A Preliminary application of the differential evolution algorithm to calculate the CNOP. Atmos Ocean Sci Lett, 2009, 2:381-385
Leith C E. Theoretical skill of Monte Carlo forecasts. Mon WEA Rev. 1974, 102:409-418
Toth Z, Kalnay E. Ensemble forecasting at Nmc:the generation of perturbations. Bull Amer Meteor Soc, 1993, 74:2317-2330
Mu M, Jiang Z N. A New approach to the generation of initial perturbations for ensemble prediction:conditional non-linear Optimal Perturbat Ion. Chinese Sci Bull, 2008, 53:2062-2068
Wang X G, Bishop C H. A Comparison of breeding and ensemble transform Kalman Filter Ensemble forecast. J Atmos Sci, 2003, 60:1140-1158
Annan J D. On the orthogonality of bred vectors. Mon WEA Rev, 2004, 132:843-849
Wei M Z, Toth z, Wobus R, et al. Ensemble Transform Kalman filter-based Ensemble perturbations in an operational global PR Ediction system at NCEP. Tellus, 2006, 58:28-44
Feng J, Ding R q, Liu D Q, et al. The application of nonlinear local Lyapunov vectors to ensemble predictions in Lo-renz. J Atmos Sci, 2014, 71:3554-3567
Lorenz E N. Predictability:a problem partly solved. In:proceedings of ECMWF Workshop on predictabil-ity. Cambridge:cambridge University Press, 1995, 40-58
Ho Zhenhua . Application of Nonlinear optimal perturbation method in set prediction . Ph. d. disserta-tions . Beijing : Institute of Atmospheric Physics, Chinese Academy of Sciences, Nonporous , 2016
Dudhia J. A nonhydrostatic version of the Penn State/ncar mesoscale model:validation tests and simulation of an At-lantic cyclone D Cold front. Mon WEA Rev. 1993, 121:1493-1513
Toth Z, Kalnay E. Ensemble forecasting at NCEP and the breeding method. Mon WEA Rev, 1997, 125:3297-3318 En-field D B, Mestas-nunez A M, Trimble P J. The Atlantic Multidecadal Oscillation and its relation to rainfall and river US. Geophys Res Lett, 2001, 28:2077-2080
Wang Y, Li S, Luo D. Seasonal response of Asian monsoonal climate to the Atlantic Multidecadal. J Geophys Res, 2009, 114:d02112
Wang C, Dong S, Munoz E. Seawater density variations in the North Atlantic and the Atlantic meridional overturn-ing tion. Climate Dyn, 2010, 34:953-968
Robson J, Hodson D, Hawkins E, et al. Atlantic overturning in decline? Nat Geosci, 2014, 7:2-3
Zu Z, Mu M, Dijkstra H A. Optimal initial excitations of decadal modification of the Atlantic meridional overturning Circu Lation under the prescribed heat and freshwater flux boundary. J Phys Oceanogr, 2016, 46:2029-2047 Bel-kin I M, Levitus S, Antonov J, et al. "The very very salinity anomalies" in the North Atla Ntic. Prog Oceanogr, 1998, 41:
-68
Pitman a J. Assessing the sensitivity of a land-surface scheme to the parameter values using a single column model. J Climate, 1994, 7:1856-1869
Bastidas L A, Hogue T S, Sorooshian S, et al. Parameter sensitivity analysis for different complexity land surface mod-els Using Multicriteria methods. J Geophys Res, 2006, 111:d20101
Zaehle S, Sitch S, Smith B, et al effects of parameter uncertainties on the modeling of terrestrial biosphere. Global Bi-ogeochem Cycles, 19:gb3020
Sun G D, Mu M. A New approach to identify the sensitivity and importance of physical parameters combination within numerical models, usin G The Lund-potsdam-jena (LPJ) model as an example. Theor Appl Climatol, doi:10.1007/s00704-015-1690-9, 2016
McPhaden m J, Zebiak S E, Glantz m H. ENSO as a integrating concept in the Earth science. Science, 2006, 314:1740-1745
Philander S G H. El Nino Southern Oscillation phenomena. Nature, 1983, 302:295-301
Duan W S, Zhao P. Revealing most disturbing tendency error of Zebiak-cane model associated with El Nino
Predictions by nonlinear forcing singular vector approach. Clim Dyn, 2015, 44:2351-2367
Zebiak S E, Cane A. A model El Nino-southern Oscillation. Mon WEA Rev. 1987, 115:2262-2278
Zhao P, Duan W S. Time-dependent nonlinear forcing singular vector-type the error of the tendency model.
Atmos Oceanic Sci Lett, 2014, 7:395-399
Zou X, Kuo Y H. Rainfall assimilation through a optimal control of initial and boundary conditions in a Lim-ited-area Scale model. Mon WEA Rev. 1996, 124:2859-2882
Huisman J, Thi n P, Karl D M, et al reduced mixing generates oscillations and chaos in the oceanic deep chlorophyll max Imum. Nature, 2006, 439:322-325
Navarro G, Ruiz J. hysteresis conditions the vertical position of deep chlorophyll in the maximum temperate. Global Biogeochem Cycles, 2013, 27:1013-1022
Reynolds C. Dynamics, selection and composition of phytoplankton in relation to vertical in structure. Ergeb Limnol, 1992, 35:13-31
klausmeier C A, Litchman E. Algal games:the vertical distribution of phytoplankton in poorly mixed water column S. Limnol Oceanogr, 2001, 46:1998-2007
Beckmann A, hense beneath surface:characteristics oceanic ecosystems under weak mixing conditions-a t Heoretical investigation. Prog Oceanogr, 2007, 75:771-796
Zhang K, Wang Q, Mu M, et al. Effects of optimal initial errors on predicting the seasonal reduction of the UPST Ream Kuroshio Transport. Deep-sea Res I, 2016, 116:220-235
All Yuan S, Wen S, Li H, et al. An optimization framework for adjoint-based Climate simulations:a case study of the Zebiak-cane model. Internat J High Performance Comput Appl, 2014, 28:174-182
Chen L, Duan W S, Xu H. A svd-based Ensemble projection algorithm for calculating the conditional nonlinear opti-mal. SCI, 2015, 58:385-394
$ Tian x J, Feng x B, Zhang H Q, et al. An enhanced ensemble-based method for computing cnops using a efficient localization implementation scheme and a two-step Optimization Strategy:formulation and preliminary tests. Q J Roy Meteor Soc, 2016, 142:1007-1016
Zheng Q, Dai Y, Zhang L, et al. On the application of a genetic algorithm to the predictability problems involving "on-off" switches. ADV Atmos Sci, 29:422-434
Fang C L, Zheng Q. The effectiveness of genetic algorithm in capturing conditional nonlinear optimal perturbation with parameterization "On-o FF "switches included by a model. J trop meteorol, 2009, 13:13-19
With Zhang L, Yuan S J, Mu B, et al cnop-based sensitive areas identification for tropical Cyclone adaptive Ations with Pcaga method. Asia-pacific J Atmos Sci, in press, 2016
Applications of nonlinear optimization approach to atmospheric and oceanic sciences
mu mu & WANG Qiang
Abstract This paper mainly introduces recent applications of nonlinear optimization approach to atmospheric and oceanic s Ciences. Emphasis is placed on the theoretical framework of the conditional nonlinear opti-mal-perturbation (Cnop) method is Ba sed on nonlinear optimization, and the works aiming to make it compre-hensive. The application progresses of the Cnop method in atmospheric and oceanic Sciences are briefly, pre-sented the app Lications for ensemble forecast, predictability of some high-impact ocean-atmospheric environ-mental events, recognition of Model parameter sensitivities, assessments of model tendency error and boundary error. In addition, we also discuss the difficulties and challenges for the application of the CNOP approach and suggest NS for future development.
Keywords nonlinear optimization, conditional nonlinear optimal perturbation (CNOP), Atmosphere, Ocean MSC (+) 35q93, 49N 65k10, 65m32, 90C30, 90C31
doi:10.1360/n012016-00200
DOI: http://dx.doi.org/10.18686/jaoe.v4i1.1161
Refbacks
- There are currently no refbacks.