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Tapovan Lolla and Pierre F. J. Lermusiaux

Abstract

Retrospective inference through Bayesian smoothing is indispensable in geophysics, with crucial applications in ocean and numerical weather estimation, climate dynamics, and Earth system modeling. However, dealing with the high-dimensionality and nonlinearity of geophysical processes remains a major challenge in the development of Bayesian smoothers. Addressing this issue, a novel subspace smoothing methodology for high-dimensional stochastic fields governed by general nonlinear dynamics is obtained. Building on recent Bayesian filters and classic Kalman smoothers, the fundamental equations and forward–backward algorithms of new Gaussian Mixture Model (GMM) smoothers are derived, for both the full state space and dynamic subspace. For the latter, the stochastic Dynamically Orthogonal (DO) field equations and their time-evolving stochastic subspace are employed to predict the prior subspace probabilities. Bayesian inference, both forward and backward in time, is then analytically carried out in the dominant stochastic subspace, after fitting semiparametric GMMs to joint subspace realizations. The theoretical properties, varied forms, and computational costs of the new GMM smoother equations are presented and discussed.

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Thomas Sondergaard and Pierre F. J. Lermusiaux

Abstract

The properties and capabilities of the Gaussian Mixture Model–Dynamically Orthogonal filter (GMM-DO) are assessed and exemplified by applications to two dynamical systems: 1) the double well diffusion and 2) sudden expansion flows; both of which admit far-from-Gaussian statistics. The former test case, or twin experiment, validates the use of the Expectation-Maximization (EM) algorithm and Bayesian Information Criterion with GMMs in a filtering context; the latter further exemplifies its ability to efficiently handle state vectors of nontrivial dimensionality and dynamics with jets and eddies. For each test case, qualitative and quantitative comparisons are made with contemporary filters. The sensitivity to input parameters is illustrated and discussed. Properties of the filter are examined and its estimates are described, including the equation-based and adaptive prediction of the probability densities; the evolution of the mean field, stochastic subspace modes, and stochastic coefficients; the fitting of GMMs; and the efficient and analytical Bayesian updates at assimilation times and the corresponding data impacts. The advantages of respecting nonlinear dynamics and preserving non-Gaussian statistics are brought to light. For realistic test cases admitting complex distributions and with sparse or noisy measurements, the GMM-DO filter is shown to fundamentally improve the filtering skill, outperforming simpler schemes invoking the Gaussian parametric distribution.

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Tapovan Lolla and Pierre F. J. Lermusiaux

Abstract

The nonlinear Gaussian Mixture Model Dynamically Orthogonal (GMM–DO) smoother for high-dimensional stochastic fields is exemplified and contrasted with other smoothers by applications to three dynamical systems, all of which admit far-from-Gaussian distributions. The capabilities of the smoother are first illustrated using a double-well stochastic diffusion experiment. Comparisons with the original and improved versions of the ensemble Kalman smoother explain the detailed mechanics of GMM–DO smoothing and show that its accuracy arises from the joint GMM distributions across successive observation times. Next, the smoother is validated using the advection of a passive stochastic tracer by a reversible shear flow. This example admits an exact smoothed solution, whose derivation is also provided. Results show that the GMM–DO smoother accurately captures the full smoothed distributions and not just the mean states. The final example showcases the smoother in more complex nonlinear fluid dynamics caused by a barotropic jet flowing through a sudden expansion and leading to variable jets and eddies. The accuracy of the GMM–DO smoother is compared to that of the Error Subspace Statistical Estimation smoother. It is shown that even when the dynamics result in only slightly multimodal joint distributions, Gaussian smoothing can lead to a severe loss of information. The three examples show that the backward inferences of the GMM–DO smoother are skillful and efficient. Accurate evaluation of Bayesian smoothers for nonlinear high-dimensional dynamical systems is challenging in itself. The present three examples—stochastic low dimension, reversible high dimension, and irreversible high dimension—provide complementary and effective benchmarks for such evaluation.

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Thomas Sondergaard and Pierre F. J. Lermusiaux

Abstract

This work introduces and derives an efficient, data-driven assimilation scheme, focused on a time-dependent stochastic subspace that respects nonlinear dynamics and captures non-Gaussian statistics as it occurs. The motivation is to obtain a filter that is applicable to realistic geophysical applications, but that also rigorously utilizes the governing dynamical equations with information theory and learning theory for efficient Bayesian data assimilation. Building on the foundations of classical filters, the underlying theory and algorithmic implementation of the new filter are developed and derived. The stochastic Dynamically Orthogonal (DO) field equations and their adaptive stochastic subspace are employed to predict prior probabilities for the full dynamical state, effectively approximating the Fokker–Planck equation. At assimilation times, the DO realizations are fit to semiparametric Gaussian Mixture Models (GMMs) using the Expectation-Maximization algorithm and the Bayesian Information Criterion. Bayes’s law is then efficiently carried out analytically within the evolving stochastic subspace. The resulting GMM-DO filter is illustrated in a very simple example. Variations of the GMM-DO filter are also provided along with comparisons with related schemes.

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Arthur J. Miller, Pierre F. J. Lermusiaux, and Pierre-Marie Poulain

Abstract

An array of current meter moorings along 12°W on the southern side of the lceland-Faeroe Ridge reveals a narrowband barotropic oscillation with period 1.8 days in spectra of velocity. The signal is coherent over at least 55-km scales and propagates phase with shallow water on the right (toward the northwest). Velocity ellipses tend to be elongated (crossing contours of f/H) and rotate anticyclonically. Solutions of the rigid-lid barotropic shallow-water equations predict the occurrence of a topographic-Rossby normal mode on the south side of the ridge with spatial scales exceeding 250 km and with intrinsic period near 1.84 days. This fundamental mode of the south side of the ridge has predicted spatial structure, phase propagation, and velocity ellipses consistent with the observed oscillation. The frictional amplitude e-folding decay time for this normal mode is estimated from the observations to be 13 days. The observed ocean currents are significantly coherent with zonal wind stress fluctuations (but not with wind stress curl) in the relevant period band, which indicates the oscillation is wind forced. This appears to be the first clear evidence of a stochastically forced resonant barotropic topographic-Rossby normal mode in the ocean.

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Samuel M. Kelly, Pierre F. J. Lermusiaux, Timothy F. Duda, and Patrick J. Haley Jr.

Abstract

A hydrostatic, coupled-mode, shallow-water model (CSW) is described and used to diagnose and simulate tidal dynamics in the greater Mid-Atlantic Bight region. The reduced-physics model incorporates realistic stratification and topography, internal tide forcing from a priori estimates of the surface tide, and advection terms that describe first-order interactions of internal tides with slowly varying mean flow and mean buoyancy fields and their respective shear. The model is validated via comparisons with semianalytic models and nonlinear primitive equation models in several idealized and realistic simulations that include internal tide interactions with topography and mean flows. Then, 24 simulations of internal tide generation and propagation in the greater Mid-Atlantic Bight region are used to diagnose significant internal tide interactions with the Gulf Stream. The simulations indicate that locally generated mode-one internal tides refract and/or reflect at the Gulf Stream. The redirected internal tides often reappear at the shelf break, where their onshore energy fluxes are intermittent (i.e., noncoherent with surface tide) because meanders in the Gulf Stream alter their precise location, phase, and amplitude. These results provide an explanation for anomalous onshore energy fluxes that were previously observed at the New Jersey shelf break and linked to the irregular generation of nonlinear internal waves.

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