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Long Jin, Cai Yao, and Xiao-Yan Huang

in self-adaptative learning and nonlinear mapping, in-depth studies have shown that ANNs lack the guidance of a rigorous theoretical system in determining adequate network structure; the effect of the application mainly depends on personal experience. In particular, “overfitting” of the ANN method frequently occurs in meteorological prediction modeling due to subjective determination of hidden nodes of the network, impeding its wide application ( Jin 2005a , b ). A genetic algorithm (GA) is a

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Dmitri Kondrashov, Sergey Kravtsov, and Michael Ghil

1. Introduction and motivation We are interested here in the nonlinear dynamics of planetary waves in fairly realistic, high-dimensional atmospheric models. A promising way of better understanding the dynamics is to investigate the properties of state-averaged trajectories in a low-dimensional subspace of the full model’s phase space. The mean (i.e., state-averaged) phase-space tendencies describe long-term preferred motion in the observed or simulated atmosphere and can possibly be related to

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Dorukhan Ardag and Donald T. Resio

combinations. Although considerable evaluation of 3G models continues today in terms of integrated wave parameters (significant wave height, mean period, and direction, etc.), little attention has been focused on details of spectral shapes. The question that seems to have been neglected is whether or not interactions between a fixed point and four points near that point is sufficient to obtain an accurate estimate of the complete nonlinear source term at this point in terms of its contribution to spectral

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Mikhail Ovtchinnikov and Richard C. Easter

spatial distribution of advected variables (e.g., Chlond 1994 ; Walcek 2000 , hereinafter W00 ). Although these modifications provide the algorithms with desired properties such as monotonicity and improved gradient preservation, they also make these algorithms nonlinear. Consequently, any relations among interrelated tracers advected separately are not necessarily preserved. This presents a serious problem for models in which variables derived from several tracers represent important properties

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J. D. Mirocha, J. K. Lundquist, and B. Kosović

the SFS stresses, including the two standard WRF SFS stress models and two formulations of a new nonlinear SFS stress model implemented into WRF (distributed in WRF version 3.2). 2. Large-eddy simulation using WRF The LES technique is predicated on application of a low-pass filter, which removes the smaller scales of turbulence from the flow field, leaving the larger, energy-producing scales to be explicitly resolved. The low-pass-filtered, or resolved component of the flow, for

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Roger Grimshaw, Caixia Wang, and Lan Li

1. Introduction Internal solitary waves in the ocean are modeled by nonlinear evolution equations of the Korteweg–de Vries type [see the reviews by Grimshaw (2001) and Helfrich and Melville (2006) for instance]. When the topography and hydrology vary in the horizontal direction, then a variable coefficient Korteweg–de Vries (vKdV) equation is used [see the recent review by Grimshaw et al. (2010) ]. Here, we are concerned with the situation when the coefficient of the quadratic term in the

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Ross Tulloch and K. Shafer Smith

the Blumen model simulation (solid lines) are shown in Fig. 2 . The Blumen simulation is normalized and averaged in the same way as the vertically discrete QG solution, and only three of its levels are plotted. The 64-level simulation is clearly a good representation of the nonlinear Eady model at this horizontal resolution; at higher horizontal resolutions, however, the discrete model will fail to resolve smaller horizontal scales at the surfaces unless its vertical resolution is concomitantly

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Navid Tahvildari and James M. Kaihatu

near kh ~1. Representation of the wave attenuation by mud in numerical wave models has recently been done for phase-averaged spectral models ( Winterwerp et al. 2007 ; Kranenburg 2008 ) and phase-resolving wave models ( Kaihatu et al. 2007 ; Huang and Chen 2008 ). This has allowed investigations of the effects of mud on various nearshore wave processes. For instance, the model by Kaihatu et al. (2007) accounts for the effect of a thin mud layer on near-resonant nonlinear interactions over the

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T. Vukicevic and D. Posselt

and (ii) use of complex nonlinear models with poorly known uncertainties. The problem of a large phase space has been addressed through use of data assimilation techniques that incorporate efficient computation of sensitivities of model simulated measurements to control parameters or state, together with advances in computing power. Contemporary data assimilation techniques belong to one of two classes: variational least squares and ensemble filters or smoothers. Variational least squares

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Edmund K. M. Chang and Pablo Zurita-Gotor

obtain stronger storm tracks in fall and spring than in midwinter. One of the weaknesses of linear storm-track models is that the amplitude of the model storm track is directly proportional to the arbitrary amplitude of the prescribed forcing. While one can argue that using the same amplitude of forcing for the different months may be a reasonable first start, there is no theoretical justification that the amount of nonlinear scattering, which is what the stochastic forcing represents, should

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