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Register a new userShadowing the rotating annulus. Part I: Measuring candidate trajectory shadowing times
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An intuitively necessary requirement of models used to provide forecasts of a systems future is the existence of shadowing trajectories that are consistent with past observations of the system: given a system-model pair, do model trajectories exist that stay reasonably close to a sequence of observations of the system? Techniques for finding such trajectories are well-understood in low-dimensional systems, but there is significant interest in their application to high-dimensional weather and climate models. We build on work by Smith et al. [2010, Phys. Lett. A, 374, 2618-2623] and develop a method for measuring the time that individual candidate trajectories of high-dimensional models shadow observations, using a model of the thermally-driven rotating annulus in the perfect model scenario. Models of the annulus are intermediate in complexity between low-dimensional systems and global atmospheric models. We demonstrate our method by measuring shadowing times against artificially-generated observations for candidate trajectories beginning a fixed distance from truth in one of the annulus chaotic flow regimes. The distribution of candidate shadowing times we calculated using our method corresponds closely to (1) the range of times over which the trajectories visually diverge from the observations and (2) the divergence time using a simple metric based on the distance between model trajectory and observations. An empirical relationship between the expected candidate shadowing times and the initial distance from truth confirms that the method behaves reasonably as parameters are varied.
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Shadowing trajectories are model trajectories consistent with a sequence of observations of a system, given a distribution of observational noise. The existence of such trajectories is a desirable property of any forecast model. Gradient descent of indeterminism is a well-established technique for finding shadowing trajectories in low-dimensional analytical systems. Here we apply it to the thermally-driven rotating annulus, a laboratory experiment intermediate in model complexity and physical idealisation between analytical systems and global, comprehensive atmospheric models. We work in the perfect model scenario using the MORALS model to generate a sequence of noisy observations in a chaotic flow regime. We demonstrate that the gradient descent technique recovers a pseudo-orbit of model states significantly closer to a model trajectory than the initial sequence. Gradient-free descent is used, where the adjoint model is set to $lambda$I in the absence of a full adjoint model. The indeterminism of the pseudo-orbit falls by two orders of magnitude during the descent, but we find that the distance between the pseudo-orbit and the initial, true, model trajectory reaches a minimum and then diverges from truth. We attribute this to the use of the $lambda$-adjoint, which is well suited to noise reduction but not to finely-tuned convergence towards a model trajectory. We find that $lambda=0.25$ gives optimal results, and that candidate model trajectories begun from this pseudo-orbit shadow the observations for up to 80 s, about the length of the longest timescale of the system, and similar to expected shadowing times based on the distance between the pseudo-orbit and the truth. There is great potential for using this method with real laboratory data.
Scaling regions -- intervals on a graph where the dependent variable depends linearly on the independent variable -- abound in dynamical systems, notably in calculations of invariants like the correlation dimension or a Lyapunov exponent. In these applications, scaling regions are generally selected by hand, a process that is subjective and often challenging due to noise, algorithmic effects, and confirmation bias. In this paper, we propose an automated technique for extracting and characterizing such regions. Starting with a two-dimensional plot -- e.g., the values of the correlation integral, calculated using the Grassberger-Procaccia algorithm over a range of scales -- we create an ensemble of intervals by considering all possible combinations of endpoints, generating a distribution of slopes from least-squares fits weighted by the length of the fitting line and the inverse square of the fit error. The mode of this distribution gives an estimate of the slope of the scaling region (if it exists). The endpoints of the intervals that correspond to the mode provide an estimate for the extent of that region. When there is no scaling region, the distributions will be wide and the resulting error estimates for the slope will be large. We demonstrate this method for computations of dimension and Lyapunov exponent for several dynamical systems, and show that it can be useful in selecting values for the parameters in time-delay reconstructions.
The dipole phenomenology, which has been quite successful applied to various hard reactions, especially on nuclear targets, is applied for calculation of Gribov inelastic shadowing. This approach does not include ad hoc procedures, which are unavoidable in calculations done in hadronic representation. Several examples of Gribov corrections evaluated within the dipole description are presented.
Within a light-cone quantum-chromodynamics dipole formalism based on the Green function technique, we study nuclear shadowing in deep-inelastic scattering at small Bjorken xB < 0.01. Such a formalism incorporates naturally color transparency and coherence length effects. Calculations of the nuclear shadowing for the bar{q}q Fock component of the photon are based on an exact numerical solution of the evolution equation for the Green function, using a realistic form of the dipole cross section and nuclear density function. Such an exact numerical solution is unavoidable for xB > 0.0001, when a variation of the transverse size of the bar{q}q Fock component must be taken into account. The eikonal approximation, used so far in most other models, can be applied only at high energies, when xB < 0.0001 and the transverse size of the bar{q}q Fock component is frozen during propagation through the nuclear matter. At xB < 0.01 we find quite a large contribution of gluon suppression to nuclear shadowing, as a shadowing correction for the higher Fock states containing gluons. Numerical results for nuclear shadowing are compared with the available data from the E665 and NMC collaborations. Nuclear shadowing is also predicted at very small xB corresponding to LHC kinematical range. Finally the model predictions are compared and discussed with the results obtained from other models.
In the early 1970s Eisenberg and Hedlund investigated relationships between expansivity and spectrum of operators on Banach spaces. In this paper we establish relationships between notions of expansivity and hypercyclicity, supercyclicity, Li-Yorke chaos and shadowing. In the case that the Banach space is $c_0$ or $ell_p$ ($1 leq p < infty$), we give complete characterizations of weighted shifts which satisfy various notions of expansivity. We also establish new relationships between notions of expansivity and spectrum. Moreover, we study various notions of shadowing for operators on Banach spaces. In particular, we solve a basic problem in linear dynamics by proving the existence of nonhyperbolic invertible operators with the shadowing property. This also contrasts with the expected results for nonlinear dynamics on compact manifolds, illuminating the richness of dynamics of infinite dimensional linear operators.
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