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Stochastic Oceanographic-Acoustic Prediction and Bayesian Inversion for Wide Area Ocean Floor Mapping

Ali, W.H., M.S. Bhabra, P.F.J. Lermusiaux, A. March, J.R. Edwards, K. Rimpau, and P. Ryu, 2019. Stochastic Oceanographic-Acoustic Prediction and Bayesian Inversion for Wide Area Ocean Floor Mapping. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, doi:10.23919/OCEANS40490.2019.8962870

Covering the vast majority of our planet, the ocean is still largely unmapped and unexplored. Various imaging techniques researched and developed over the past decades, ranging from echo-sounders on ships to LIDAR systems in the air, have only systematically mapped a small fraction of the seafloor at medium resolution. This, in turn, has spurred recent ambitious efforts to map the remaining ocean at high resolution. New approaches are needed since existing systems are neither cost nor time effective. One such approach consists of a sparse aperture mapping technique using autonomous surface vehicles to allow for efficient imaging of wide areas of the ocean floor. Central to the operation of this approach is the need for robust, accurate, and efficient inference methods that effectively provide reliable estimates of the seafloor profile from the measured data. In this work, we utilize such a stochastic prediction and Bayesian inversion and demonstrate results on benchmark problems. We first outline efficient schemes for deterministic and stochastic acoustic modeling using the parabolic wave equation and the optimally-reduced Dynamically Orthogonal equations and showcase results on stochastic test cases. We then present our Bayesian inversion schemes and its results for rigorous nonlinear assimilation and joint bathymetry-ocean physics-acoustics inversion.

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Many Task Computing for Multidisciplinary Ocean Sciences: Real-Time Uncertainty Prediction and Data Assimilation

Evangelinos, C., P.F.J. Lermusiaux, J. Xu, P.J. Haley, and C.N. Hill, 2009. Many Task Computing for Multidisciplinary Ocean Sciences: Real-Time Uncertainty Prediction and Data Assimilation. Conference on High Performance Networking and Computing, Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers (Portland, OR, 16 November 2009), 10pp. doi.acm.org/10.1145/1646468.1646482.

Error Subspace Statistical Estimation (ESSE), an uncertainty prediction and data assimilation methodology employed for real-time ocean forecasts, is based on a characterization and prediction of the largest uncertainties. This is carried out by evolving an error subspace of variable size. We use an ensemble of stochastic model simulations, initialized based on an estimate of the dominant initial uncertainties, to predict the error subspace of the model fields. The dominant error covariance (generated via an SVD of the ensemble-generated error covariance matrix) is used for data assimilation. The resulting ocean fields are provided as the input to acoustic modeling, allowing for the prediction and study of the spatiotemporal variations in acoustic propagation and their uncertainties. The ESSE procedure is a classic case of Many Task Computing: These codes are managed based on dynamic workflows for the: (i) perturbation of the initial mean state, (ii) subsequent ensemble of stochastic PE model runs, (iii) continuous generation of the covariance matrix, (iv) successive computations of the SVD of the ensemble spread until a convergence criterion is satisfied, and (v) data assimilation. Its ensemble nature makes it a many task data intensive application and its dynamic workflow gives it heterogeneity. Subsequent acoustics propagation modeling involves a very large ensemble of short-in-duration acoustics runs.
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Adaptive Acoustical-Environmental Assessment for the Focused Acoustic Field-05 At-sea Exercise

Wang, D., P.F.J. Lermusiaux, P.J. Haley, W.G. Leslie and H. Schmidt, 2006. Adaptive Acoustical-Environmental Assessment for the Focused Acoustic Field-05 At-sea Exercise, Oceans 2006, 6pp, Boston, MA, 18-21 Sept. 2006, doi: 10.1109/OCEANS.2006.306904.

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.
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Progress and Prospects of U.S. Data Assimilation in Ocean Research

Lermusiaux, P.F.J., P. Malanotte-Rizzoli, D. Stammer, J. Carton, J. Cummings and A.M. Moore, 2006. "Progress and Prospects of U.S. Data Assimilation in Ocean Research". Oceanography, Special issue on "Advances in Computational Oceanography", T. Paluszkiewicz and S. Harper, Eds., 19, 1, 172-183.

THIS REPORT summarizes goals, activities, and recommendations of a workshop on data assimilation held in Williamsburg, Virginia on September 9-11, 2003, and sponsored by the U.S. Office of Naval Research (ONR) and National Science Foundation (NSF). The overall goal of the workshop was to synthesize research directions for ocean data assimilation (DA) and outline efforts required during the next 10 years and beyond to evolve DA into an integral and sustained component of global, regional, and coastal ocean science and observing and prediction systems. The workshop built on the success of recent and existing DA activities such as those sponsored by the National Oceanographic Partnership Program (NOPP) and NSF-Information Technology Research (NSF-ITR). DA is a quantitative approach to optimally combine models and observations. The combination is usually consistent with model and data uncertainties, which need to be represented. Ocean DA can extract maximum knowledge from the sparse and expensive measurements of the highly variable ocean dynamics. The ultimate goal is to better understand and predict these dynamics on multiple spatial and temporal scales, including interactions with other components of the climate system. There are many applications that involve DA or build on its results, including: coastal, regional, seasonal, and inter-annual ocean and climate dynamics; carbon and biogeochemical cycles; ecosystem dynamics; ocean engineering; observing-system design; coastal management; fisheries; pollution control; naval operations; and defense and security. These applications have different requirements that lead to variations in the DA schemes utilized. For literature on DA, we refer to Ghil and Malanotte-Rizzoli (1991), the National Research Council (1991), Bennett (1992), Malanotte- Rizzoli (1996), Wunsch (1996), Robinson et al. (1998), Robinson and Lermusiaux (2002), and Kalnay (2003). We also refer to the U.S. Global Ocean Data Assimilation Experiment (GODAE) workshop on Global Ocean Data Assimilation: Prospects and Strategies (Rienecker et al., 2001); U.S. National Oceanic and Atmospheric Administration-Office of Global Programs (NOAA-OGP) workshop on Coupled Data Assimilation (Rienecker, 2003); and, NOAA-NASA-NSF workshop on Ongoing Analysis of the Climate System (Arkin et al., 2003).
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Prediction Systems with Data Assimilation for Coupled Ocean Science and Ocean Acoustics

Robinson, A.R. and P.F.J. Lermusiaux, 2004. Prediction Systems with Data Assimilation for Coupled Ocean Science and Ocean Acoustics, Proceedings of the Sixth International Conference on Theoretical and Computational Acoustics (A. Tolstoy, et al., editors), World Scientific Publishing, 325-342. Refereed invited Keynote Manuscript.

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.
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Four-dimensional data assimilation for coupled physical-acoustical fields

Lermusiaux, P.F.J. and C.-S. Chiu, 2002. Four-dimensional data assimilation for coupled physical-acoustical fields. In "Acoustic Variability, 2002". N.G. Pace and F.B. Jensen (Eds.), Saclantcen. Kluwer Academic Press, 417-424.

The estimation of oceanic environmental and acoustical fields is considered as a single coupled data assimilation problem. The four-dimensional data assimilation methodology employed is Error Subspace Statistical Estimation. Environmental fields and their dominant uncertainties are predicted by an ocean dynamical model and transferred to acoustical fields and uncertainties by an acoustic propagation model. The resulting coupled dominant uncertainties define the error subspace. The available physical and acoustical data are then assimilated into the predicted fields in accord with the error subspace and all data uncertainties. The criterion for data assimilation is presently to correct the predicted fields such that the total error variance in the error subspace is minimized. The approach is exemplified for the New England continental shelfbreak region, using data collected during the 1996 Shelfbreak Primer Experiment. The methodology is discussed, computational issues are outlined and the assimilation of model-simulated acoustical data is carried out. Results are encouraging and provide some insights into the dominant variability and uncertainty properties of acoustical fields.
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Advanced interdisciplinary data assimilation: Filtering and smoothing via error subspace statistical estimation.

Lermusiaux, P.F.J., A.R. Robinson, P.J. Haley and W.G. Leslie, 2002. Advanced interdisciplinary data assimilation: Filtering and smoothing via error subspace statistical estimation. Proceedings of "The OCEANS 2002 MTS/IEEE" conference, Holland Publications, 795-802.

The efficient interdisciplinary 4D data assimilation with nonlinear models via Error Subspace Statistical Estimation (ESSE) is reviewed and exemplified. ESSE is based on evolving an error subspace, of variable size, that spans and tracks the scales and processes where the dominant errors occur. A specific focus here is the use of ESSE in interdisciplinary smoothing which allows the correction of past estimates based on future data, dynamics and model errors. ESSE is useful for a wide range of purposes which are illustrated by three investigations: (i) smoothing estimation of physical ocean fields in the Eastern Mediterranean, (ii) coupled physical-acoustical data assimilation in the Middle Atlantic Bight shelfbreak, and (iii) coupled physical-biological smoothing and dynamics in Massachusetts Bay.
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Transfer of uncertainties through physical-acoustical-sonar end-to-end systems: A conceptual basis

Robinson, A.R., P. Abbot, P.F.J. Lermusiaux and L. Dillman, 2002. Transfer of uncertainties through physical-acoustical-sonar end-to-end systems: A conceptual basis. In "Acoustic Variability, 2002:. N.G. Pace and F.B. Jensen (Eds.), SACLANTCEN. Kluwer Academic Press, 603-610.

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.
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