The Bay of Bengal (BoB) exhibits a distinctive pattern of surface freshening primarily resulting from runoff originating from several major rivers and the monsoon precipitation. This freshening significantly modulates the spatial and temporal variations in the thermohaline structure, ultimately shaping the sound speed structure within this region. This study investigates the seasonal impact of river input on the sound speed structure of the BoB through two numerical simulations with and without river input using the Regional Ocean Modeling System (ROMS). The findings indicate that river inputs consistently reduce the surface sound speed across the domain throughout the year, with the most noticeable effect occurring in the northern part of BoB during the post-monsoon months of October and November. During this period, the surface variability is predominately driven by salinity variations induced by river inputs. In contrast, in the subsurface layers, the influence of reduced salinity becomes less pronounced with increasing depth, and the temperature modulations brought about by river inputs play a more important role. Freshening in the surface layers leads to the creation of a stratified barrier layer just below the mixed layer. Consequently, this results in the formation of warm temperature inversions in the subsurface layers, with cooling occurring beneath them. These phenomena contribute to variations in the sound speed, causing it to increase within the inversion layer and decrease below it. Notably, the sonic layer depth (SLD) is found to become shallower in the presence of river inputs during the post-monsoon and winter seasons in the northern BoB. The combination of enhanced vertical salinity gradients and subsurface temperature inversions significantly amplifies the vertical gradient of sound speed above the SLD. This, in turn, may lead to the development of more robust surface ducts and the expansion of shadow zones beneath the SLD.

Cost-effective seafloor mapping at high resolution is yet to be attained. A possible solution consists of using a mobile, wide-aperture, sparse array with subarrays distributed across multiple autonomous surface vessels. Such wide-area mapping with multiple dynamic sources and receivers require accurate modeling and processing systems for imaging the seabed. In this paper, we focus on computational schemes and challenges for such high-resolution acoustic imaging or migration. Starting from the imaging condition from the adjoint-state method, we derive a closed-form expression for Gaussian beam migration in stratified media. We employ this technique on simulated data and on real data collected with our novel acoustic array over shipwrecks in the Boston Harbor. We compare Gaussian beam migration with diffraction stack and Kirchhoff migration, and we find that Gaussian beam migration produces the clearest images with the fewest artifacts.

The multi-scale dynamics of oceanic processes and the complex propagation of acoustic waves are fundamental challenges in marine sciences and operations. Recent computing advances enable such multiresolution ocean and acoustic modeling, but a fully integrated system for sustained coupled predictions and Bayesian data assimilation remains needed. In this study, we integrate the MSEAS Primitive Equation (PE) ocean modeling system and the MSEAS acoustic Parabolic Equation (ParEq) solver, enabling real-time coupled ocean and acoustic predictions. Realistic applications in Massachusetts Bay, the Norwegian Sea, the western Mediterranean Sea, and the New York Bight are used to demonstrate capabilities and validate predictions in diverse shallow and deep-water environments. Results provide the foundation for an end-to-end system for coupled ocean-acoustic probabilistic modeling, Bayesian inversion, and learning.

Developing accurate and computationally efficient models for underwater sound propagation in the uncertain, dynamic ocean environment is inherently challenging. In this work, we evaluate the potential of dynamic reduced-order modeling for stochastic ray tracing. We obtain and implement the stochastic dynamically-orthogonal (DO) differential equations for Ray Tracing (DO-Ray). With stochastic DO-Ray, we can start from non-Gaussian environmental uncertainties and compute the stochastic acoustic ray fields in a dynamic reduced order fashion, all while preserving the dominant complex statistics of the ocean environment and the nonlinear relations with ray dynamics. We develop varied algorithms and discuss implementation challenges and solutions, using direct Monte Carlo for comparison. We showcase results in an uncertain deep-sound channel example and observe the ability to represent the stochastic ray trace fields in a dynamic reduced-order fashion.

The widely-used Global Positioning System (GPS) does not work underwater. This presents a severe limitation on the communication capabilities and deployment options for undersea assets such as AUVs and UUVs. To address this challenge, the Positioning System for Deep Ocean Navigation (POSYDON) program aims to develop an undersea system that provides omnipresent, robust positioning across ocean basins. To do so, it is critically important to accurately model sound waves and signals under diverse, and often uncertain, undersea environmental conditions. Probabilistic estimates of the four-dimensional variability of the fields of sound speed, salinity, temperature, and currents are thus needed. In this paper, we employ our MSEAS primitive-equation and error subspace data-assimilative ensemble ocean forecasting system during two real-time POSYDON sea exercises, one in winter 2017 and another in August 2018. We provide real-time high-resolution estimates of sound speed fields and their uncertainty, and describe the ocean conditions from submesoscales eddies and internal tides to warm core rings and larger-scale circulations. We verify our results against independent data of opportunity; in all cases, we show that our probabilistic forecasts demonstrate skill.

The reliability of sonar systems in the littoral environment is greatly affected by the variability of the surrounding nonlinear ocean dynamics. This variability occurs on multiple scales in space and time, and involves multiple interacting processes, from internal tides and waves to meandering fronts, eddies, boundary layers, and strong air-sea interactions. We utilize our high-resolution MSEAS-PE ocean modeling system to hindcast the ocean physical environment off the New Jersey continental shelf for the end of June 2009, and then utilize our new MSEAS probabilistic acoustic NAPE and WAPE solvers in a coupled ocean physics-acoustic modeling fashion to predict the transmission and integrated transmission losses, respectively. The coupled models are described, and their predictions verified against independent ocean physics observations and sound propagation measurements from acoustic sources and receivers in the region. Our high-resolution ocean simulations are shown to substantial reduce the RMSE and bias of the coarser simulations. Our acoustic simulations of deterministic and stochastic TL fields also show significant skill.

Regional ocean models have long been integrated with acoustic propagation and scattering models, including work in the 1990s by Robinson and Lee. However, the dynamics in these models has been not inclusive enough to represent submesoscale features that are now known to be very important acoustically. The features include internal waves, thermohaline intrusions, and details of fronts. In practice, regional models predict internal tides at many locations, but the nonlinear steepening of these waves and their conversion to short nonlinear waves is often improperly modeled, because computationally prohibitive nonhydrostatic pressure is needed. To include the small-scale internal waves of tidal origin, a nested hybrid model is under development. The approach is to extract long-wavelength internal tide wave information from tidally forced regional models, use ray methods or mapping methods to determine internal-tide propagation patterns, and then solve two-dimensional high-resolution nonhydrostatic wave models to “fill-in” the internal wave details. The resulting predicted three-dimensional environment is then input to a fully three-dimensional parabolic equation acoustic code. The output from the nested ocean model, run in hindcast mode, is to be compared to field data from the Shallow Water 2006 (SW06) experiment to test and ground truth purposes

Today’s situational awareness requirements in the undersea environment present severe challenges for acoustic communication systems. Acoustic propagation through the ocean environment severely limits the capacity of existing underwater communication systems. Specifically, the presence of internal waves coupled with the ocean sound channel creates a stochastic field that introduces deep fades and significant intersymbol interference (ISI) thereby limiting reliable communication to low data rates. In this paper we present a communication architecture that optimally predistorts the acoustic wave via spatial modulation and detects the acoustic wave with optimal spatial recombination to maximize reliable information throughput. This effectively allows the system to allocate its power to the most efficient propagation modes while mitigating ISI. Channel state information is available to the transmitter through low rate feedback. New results include the asymptotic distribution of singular values for a large number of apertures. Further, we present spatial modulation at the transmitter and spatial recombination at the receiver that asymptotically minimize bit error rate (BER). We show that, in many applications, the number of apertures can be made large enough so that asymptotic results approximate finite results well. Additionally, we show that the interference noise power is reduced proportional to the inverse of the number of receive apertures. Finally, we calculate the asymptotic BER for the sparse aperture acoustic system.

We present the spatial and temporal variability of the acoustic field in Dabob Bay during the PLUSNet07 Exercise. The study uses a 4-D data-assimilative numerical ocean model to provide input to an acoustic propagation model. The ocean physics models (primitive-equations and tidal models), with CTD data assimilation, provided ocean predictions in the region. The output ocean forecasts had a 300m and 1-5m resolution in the horizontal and vertical directions, at 3-hour time intervals within a 15-day period. This environmental data, as the input to acoustic modeling, allowed for the prediction and study of the temporal variations of the acoustic field, as well as the varying spatial structures of the field. Using a one-way coupled-normal-mode code, along- and across-sections in the Dabob Bay acoustic field structures at 100, 400, and 900 Hz were forecasted and described twice-daily, for various source depths. Interesting propagation effects, such as acoustic fluctuations with respect to the source depth and frequency as a result of the regional ocean variability, wind forcing, and tidal effects are discussed. The novelty of this work lies in the possibility of accurate acoustic TL prediction in the littoral region by physically coupling the real-time ocean prediction system to real-time acoustic modeling.

Variabilities in the coastal ocean environment span a wide range of spatial and temporal scales. From an acoustic viewpoint, the limited oceanographic measurements and today’s ocean modeling capabilities can’t always provide oceanic-acoustic predictions in sufficient detail and with enough accuracy. Adaptive Rapid Environmental Assessment (AREA) is a new adaptive sampling concept being developed in connection with the emergence of the Autonomous Ocean Sampling Network (AOSN) technology. By adaptively and optimally deploying in-situ measurement resources and assimilating these data in coupled nested ocean and acoustic models, AREA can dramatically improve the ocean estimation that matters for acoustic predictions and so be essential for such predictions. These concepts are outlined and preliminary methods are developed and illustrated based on the Focused Acoustic Forecasting-05 (FAF05) exercise. During FAF05, AREA simulations were run in real-time and engineering tests carried out, within the context of an at-sea experiment with Autonomous Underwater Vehicles (AUV) in the northern Tyrrhenian sea, on the eastern side of the Corsican channel.

Ocean science and ocean acoustics today are engaged in coupled interdisciplinary research on both fundamental dynamics and applications. In this context interdisciplinary data assimilation, which melds observations and fundamental dynamical models for field and parameter estimation is emerging as a novel and powerful methodology, but computational demands present challenging constraints which need to be overcome. These ideas are developed within the concept of an interdisciplinary system for assessing sonar system performance. An end-to-end system, which couples meteorology-physical oceanography-geoacoustics-ocean acoustics-bottom-noise-target-sonar data and models, is used to estimate uncertainties and their transfers and feedbacks. The approach to interdisciplinary data assimilation for this system importantly involves a full, interdisciplinary state vector and error covariance matrix. An idealized end-to-end system example is presented based upon the Shelfbreak PRIMER experiment in the Middle Atlantic Bight. Uncertainties in the physics are transferred to the acoustics and to a passive sonar using fully coupled physical and acoustical data assimilation.

The uncertainties in the predicted acoustic wavefield associated with the transmission of low- frequency sound from the continental slope, through the shelfbreak front, onto the continental shelf are examined. The locale and sensor geometry being investigated is that of the New England continental shelfbreak with a moored low-frequency sound source on the slope. Our method of investigation employs computational fluid mechanics coupled with computational acoustics. The coupled methodology for uncertainty estimation is that of Error Subspace Statistical Estimation. Specifically, based on observed oceanographic data during the 1996 Shelfbreak Primer Experiment, the Harvard University primitive-equation ocean model is initialized with many realizations of physical fields and then integrated to produce many realizations of a five-day regional forecast of the sound speed field. In doing so, the initial physical realizations are obtained by perturbing the physical initial conditions in statistical accord with a realistic error subspace. The different forecast realizations of the sound speed field are then fed into a Naval Postgraduate School coupled-mode sound propagation model to produce realizations of the predicted acoustic wavefield in a vertical plane across the shelfbreak frontal zone. The combined ocean and acoustic results from this Monte Carlo simulation study provide insights into the relations between the uncertainties in the ocean and acoustic estimates. The modeled uncertainties in the transmission loss estimate and their relations to the error statistics in the ocean estimate are discussed.

An interdisciplinary team of scientists is collaborating to enhance the understanding of the uncertainty in the ocean environment, including the sea bottom, and characterize its impact on tactical system performance. To accomplish these goals quantitatively an end-to-end system approach is necessary. The conceptual basis of this approach and the framework of the end-to-end system, including its components, is the subject of this presentation. Specifically, we present a generic approach to characterize variabilities and uncertainties arising from regional scales and processes, construct uncertainty models for a generic sonar system, and transfer uncertainties from the acoustic environment to the sonar and its signal processing. Illustrative examples are presented to highlight recent progress toward the development of the methodology and components of the system.