We consider the interactions of internal tides (ITs) with a dynamic, rotational, and heterogeneous ocean, and spatially varying topography. The IT fields are expanded using vertical modal basis functions, whose amplitudes vary horizontally and temporally. We obtain the evolution equations of modal amplitudes and energy including simultaneous three-way interactions with the mean flow, buoyancy, and topography. We apply these equations to a set of idealized and two realistic data-assimilative primitive equation simulations. These simulations reveal that significant interactions of ITs with the background fields occur at topographic features and strong currents, in particular when the scales of the background and ITs are similar. In local hot-spots, the new three-way interaction terms when compared to the total modal conversion are found to reach up to 10-30% at steep topography and about 50% in the Gulf Stream. We provide a dimensional analysis to guide the diagnosis of such strong interactions. When IT interactions are with a large-scale barotropic current (without topographic effects), our modal energy equation reduces to the conservation of modal wave action under a WKB consideration. We further derive analytical solutions of the modulation of wavenumber and energy of an IT propagating into a collinear current. For ITs propagating along the flow direction, the wavelength is stretched and the amplitude is reduced, with the degree of modulation determined by |f/ω₀|, the ratio of inertial to tidal frequencies. For ITs propagating opposite to the flow direction, a critical value of |f/ω₀| exists, below and above which the waves show remarkably different behaviors. The critical opposing current speed which triggers the wave focusing/blocking phenomenon is obtained and its implication on the propagation and dissipation of ITs is discussed.

The Red Sea Project (RSP) is based on a coastal lagoon with over 90 pristine islands. The project intends to transform the Red Sea coast into a world-class tourist destination. To better understand the regional dynamics and water exchange scenarios in the lagoon, a high-resolution numerical model is implemented. The general and tidal circulation dynamics are then investigated with a particular focus on the response of the lagoon to strong wind jets. Significant variations in winter and summer circulation patterns are identified. The tidal amplitude inside the lagoon is greater than that outside, with strong tidal currents passing over its surrounding coral reef banks. The lagoon rapidly responds to the strong easterly wind jets that occur mainly in winter; it develops a reverse flow at greater depths, and the coastal water elevation is instantly affected. Lagrangian particle simulations are conducted to study the residence time of water in the lagoon. The results suggest that water renewal is slow in winter. Analysis of the Lagrangian coherent structures (LCS) reveals that water renewal is largely linked to the circulation patterns in the lagoon. In winter, the water becomes restricted in the central lagoon with only moderate exchange, whereas in summer, more circulation is observed with a higher degree of interaction between the central lagoon and external water. The results of LCS also highlight the tidal contribution to stirring and mixing while identifying the hotspots of the phenomenon. Our analysis demonstrates an effective approach for studying regional water mixing and connectivity, which could support coastal management in data-limited regions.

Abhinav Gupta’s poster “Neural Closure Models for Dynamical Systems” has won the Best Poster Award at the annual CCSE Student Poster Session. The poster session was part of the 2021 MIT CCSE Symposium, which took place in a virtual format on March 15, 2021 through a combination of Zoom and Gather platforms. The website of the symposium may be found here. Congratulations to Abhinav!

The poster is available here. A preprint of the paper containing his work is available here.

We address the problem of finding the closest matrix Ũ to a given U under the constraint that a prescribed second-moment matrix P̃ must be matched, i.e. Ũ^TŨ=P̃. We obtain a closed-form formula for the unique global optimizer Ũ for the full-rank case, that is related to U by an SPD (symmetric positive definite) linear transform. This result is generalized to rank-deficient cases as well as to infinite dimensions. We highlight the geometric intuition behind the theory and study the problem’s rich connections to minimum congruence transform, generalized polar decomposition, optimal transport, and rank-deficient data assimilation. In the special case of P̃=I, minimum-correction second-moment matching reduces to the well-studied optimal orthonormalization problem. We investigate the general strategies for numerically computing the optimizer and analyze existing polar decomposition and matrix square root algorithms. We modify and stabilize two Newton iterations previously deemed unstable for computing the matrix square root, such that they can now be used to efficiently compute both the orthogonal polar factor and the SPD square root. We then verify the higher performance of the various new algorithms using benchmark cases with randomly generated matrices. Lastly, we complete two applications for the stochastic Lorenz-96 dynamical system in a chaotic regime. In reduced subspace tracking using dynamically orthogonal equations, we maintain the numerical orthonormality and continuity of time-varying base vectors. In ensemble square root filtering for data assimilation, the prior samples are transformed into posterior ones by matching the covariance given by the Kalman update while also minimizing the corrections to the prior samples.

Though computing power continues to grow quickly, our appetite to solve larger and larger problems grows just as fast. As a consequence, reduced-order modeling has become an essential technique in the computational scientist’s toolbox. By reducing the dimensionality of a system, we are able to obtain approximate solutions to otherwise intractable problems. And because the methodology we develop is sufficiently general, we may agnostically apply it to a plethora of problems, whether the high dimensionality arises due to the sheer size of the computational domain, the fine resolution we require, or stochasticity of the dynamics. In this thesis, we develop time integration schemes, called retractions, to efficiently evolve the dynamics of a system’s low-rank approximation. Through the study of differential geometry, we are able to analyze the error incurred at each time step. A novel, explicit, computationally inexpensive set of algorithms, which we call perturbative retractions, are proposed that converge to an ideal retraction that projects exactly to the manifold of fixed-rank matrices. Furthermore, each perturbative retraction itself exhibits high-order convergence to the best low-rank approximation of the full-rank solution. We show that these high-order retractions significantly reduce the numerical error incurred over time when compared to a naive Euler forward retraction. Through test cases, we demonstrate their efficacy in the cases of matrix addition, real-time data compression, and deterministic and stochastic differential equations.