Brian R. Hunt

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Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system’s time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to “forecast” the current state,(More)
In this paper, we introduce a new, local formulation of the ensemble Kalman Filter approach for atmospheric data assimilation. Our scheme is based on the hypothesis that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than(More)
We present a measure-theoretic condition for a property to hold “almost everywhere” on an infinite-dimensional vector space, with particular emphasis on function spaces such as C and L. Like the concept of “Lebesgue almost every” on finite-dimensional spaces, our notion of “prevalence” is translation invariant. Instead of using a specific measure on the(More)
We study the transition from incoherence to coherence in large networks of coupled phase oscillators. We present various approximations that describe the behavior of an appropriately defined order parameter past the transition and generalize recent results for the critical coupling strength. We find that, under appropriate conditions, the coupling strength(More)
The accuracy and computational efficiency of a parallel computer implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme on the model component of the 2004 version of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) is investigated. Numerical experiments are carried out at(More)
In Ensemble Kalman Filter data assimilation, localization modifies the error covariance matrices to suppress the influence of distant observations, removing spurious long distance correlations. In addition to allowing efficient parallel implementation, this takes advantage of the atmosphere's lower dimensionality in local regions. There are two primary(More)
The largest eigenvalue of the adjacency matrix of networks is a key quantity determining several important dynamical processes on complex networks. Based on this fact, we present a quantitative, objective characterization of the dynamical importance of network nodes and links in terms of their effect on the largest eigenvalue. We show how our(More)
The largest eigenvalue of the adjacency matrix of a network plays an important role in several network processes (e.g., synchronization of oscillators, percolation on directed networks, and linear stability of equilibria of network coupled systems). In this paper we develop approximations to the largest eigenvalue of adjacency matrices and discuss the(More)
We consider the image of a fractal set X in a Banach space under typical linear and nonlinear projections π intoR . We prove that whenN exceeds twice the box-counting dimension of X, then almost every (in the sense of prevalence) such π is one-to-one on X, and we give an explicit bound on the Hölder exponent of the inverse of the restriction of π toX. The(More)