Multi-resolution Probabilistic Ocean Physics-Acoustics Modeling: Validation in the New Jersey Continental Shelf

Lermusiaux, Pierre F. J.; Haley, Patrick J.; Mirabito, Chris; Ali, Wael H.; Bhabra, Manmeet S.; Abbot, Philip; Chiu, Ching‐Sang; Emerson, Chris

doi:10.1109/ieeeconf38699.2020.9389193

Cited by 10 publications

(4 citation statements)

References 55 publications

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“…Underwater acoustics computation is complex due to the variability on different scales in time and space, multiple interacting processes such as internal tides and wave, fronts, eddies, surface dynamics, and air-to-sea interactions [64,45,17,16,42].…”

Section: Ilrdmd With Underwater Acoustics Computationsmentioning

confidence: 99%

“…MSEAS has an extensive record of research related to deterministic and stochastic underwater acoustics computations. Previous works such as [35,2,3,43,42,11] employ ensemble and dynamically-orthogonal equations to predict ocean physical-acoustical field probabilities, and complete multivariate joint Bayesian estimation and inversion of physical-acoustical fields [3].…”

Section: Ilrdmd With Underwater Acoustics Computationsmentioning

confidence: 99%

“…When possible, one utilizes high-fidelity numerical ocean simulations for deterministic and stochastic underwater acoustics computation [3,42]. However, in this collaborative work with Wael H. Ali at MSEAS, we explore the feasibility of using the 3D multivariate iLRDMD forecasts for reliable deterministic underwater acoustics forecasting.…”

Section: Ilrdmd With Underwater Acoustics Computationsmentioning

confidence: 99%

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Adaptive Stochastic Reduced Order Modeling for Autonomous Ocean Platforms

Ryu

Heuss

Haley

et al. 2021

OCEANS 2021: San Diego – Porto

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Onboard forecasting and data assimilation are challenging but essential for unmanned autonomous ocean platforms. Due to the numerous operational constraints for these platforms, efficient adaptive reduced-order models (ROMs) are needed. In this thesis, we first review existing approaches and then develop a new adaptive Dynamic Mode Decomposition (DMD)-based, data-driven, reduced-order model framework that provides onboard forecasting and data assimilation capabilities for bandwidthdisadvantaged autonomous ocean platforms. We refer to the new adaptive ROM as the incremental, stochastic Low-Rank Dynamic Mode Decomposition (iLRDMD) algorithm. Given a set of high-fidelity and high-dimensional stochastic forecasts computed in remote centers, this framework enables i) efficient and accurate send and receive of the high-fidelity forecasts, ii) incremental update of the onboard reducedorder model, iii) data-driven onboard forecasting, and iv) onboard ROM data assimilation and learning. We analyze the computational costs for the compression, communications, incremental updates, and onboard forecasts. We evaluate the adaptive ROM using a simple 2D flow behind an island, both as a test case to develop the method, and to investigate the parameter sensitivity and algorithmic design choices. We develop the extension of deterministic iLRDMD to stochastic applications with uncertain ocean forecasts. We then demonstrate the adaptive ROM on more complex ocean fields ranging from univariate 2D, univariate 3D, and multivariate 3D fields from multi-resolution, data-assimilative Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) reanalyses, specifically from the real-time exercises in the Middle Atlantic Bight region. We also highlight our results using the Navy's Hybrid Coordinate Ocean Model (HYCOM) forecasts in the North Atlantic region. We then apply the adaptive ROM onboard forecasting algorithm to interdisciplinary applications, showcasing adaptive reduced-order forecasts for onboard underwater acoustics computations and forecasts, as well as for exact time-optimal path-planning with autonomous surface vehicles.For stochastic forecasting and data assimilation onboard the unmanned autonomous ocean platforms, we combine the stochastic ensemble DMD method with the Gaus-

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Section: Ilrdmd With Underwater Acoustics Computationsmentioning

confidence: 99%

Section: Ilrdmd With Underwater Acoustics Computationsmentioning

confidence: 99%

Section: Ilrdmd With Underwater Acoustics Computationsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Stochastic Reduced Order Modeling for Autonomous Ocean Platforms

Ryu

Heuss

Haley

et al. 2021

OCEANS 2021: San Diego – Porto

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“…Ocean acoustics is the science of sound processes in the ocean and is concerned in particular with sound generation, propagation, attenuation and deflection [1,2]. This science is essential to understand marine mammal communication, for seismic studies, oceanographic applications, acoustic tomography, sonar detection and underwater engineering.…”

Section: Introductionmentioning

confidence: 99%

Effect of Subsurface Mediterranean Water Eddies on Sound Propagation Using ROMS Output and the Bellhop Model

Bozroudi

Ciani

Mahdizadeh

et al. 2021

Water

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Ocean processes can locally modify the upper ocean density structure, leading to an attenuation or a deflection of sound signals. Among these phenomena, eddies cause significant changes in acoustic properties of the ocean; this suggests a possible characterization of eddies via acoustics. Here, we investigate the propagation of sound signals in the Northeastern Atlantic Ocean in the presence of eddies of Mediterranean Water (Meddies). Relying on a high-resolution simulation of the Atlantic Ocean in which Meddies were identified and using the Bellhop acoustic model, we investigated the differences in sound propagation in the presence and absence of Meddies. Meddies create sound channels in which the signals travel with large acoustic energy. The transmission loss decreases to 80 or 90 dB; more signals reach the synthetic receivers. Outside of these channels, the sound signals are deflected from their normal paths. Using receivers at different locations, the acoustic impact of different Meddies, or of the same Meddy at different stages of its life, are characterized in terms of angular distributions of times of arrivals and of energy at reception. Determining the influence of Meddies on acoustic wave characteristics at reception is the first step to inverting the acoustic signals received and retrieving the Meddy hydrological characteristics.

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