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Zoltan Szuts Principal Oceanographer zszuts@apl.washington.edu Phone 206-616-7918 |
Education
B.A. Biology, Oberlin College, 2001
M.S. Oceanography, University of Washington, 2004
Ph.D. Oceanography, University of Washington, 2008
Projects
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Sampling QUantitative Internal-wave Distributions SQUID Our goals are to understand the generation, propagation, and dissipation mechanisms for oceanic internal gravity waves to enable seamless, skillful modeling & forecasts of these internal waves between the deep ocean and the shore. |
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26 Feb 2024
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The SQUID team will provide a globally distributed observing program for shear, energy flux, and mixing by internal waves. We will use profiling floats measuring temperature, salinity, velocity, and turbulence that will yield new insights into internal wave regimes and parameterizations, and that will provide direct and derived data products tailored for use by modeling groups for comparison and validation. |
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Videos
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Sigma Profiler Measurements of Salinity Stratification in the Upper San Francisco Bay Estuary The Sigma Profiler, a seafloor instrument that measures the electrical conductivity of the overlying water column, monitored salinity stratification in Suisun Bay, CA, during fall 2021. The field demonstration confirms that the SP is robust and very sensitive to the changing salinity conditions in a dynamic estuarine environment with daily mixing of saline ocean and fresh river waters. |
15 May 2024
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Publications |
2000-present and while at APL-UW |
A modular control aid for profiling floats with a Gulf Stream case study Tolone, J., T. Harrison, T. Curtin, Z. Szuts, and D.A. Paley, "A modular control aid for profiling floats with a Gulf Stream case study," In Proc., OCEANS 2025 Great Lakes, Chicago, 29 September 2 October 2025, doi:10.23919/OCEANS59106.2025.11245129 (IEEE, 2025). |
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25 Nov 2025 |
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This work presents a conceptual framework, called FloatCast, for the control of a small fleet of buoyancy-controlled ocean profiling floats. The control objective is to maximize sampling coverage in a given region of interest. The framework optimizes park depth and park duration commands for each float in the fleet. FloatCast uses an Echo State Network to make a sea level anomaly forecast, which is converted into a surface flow forecast. This flow forecast informs a Lagrangian particle model of drifting vehicle dynamics. The state-space model of the float dynamics uses candidate sets of commands to predict float trajectories, which are evaluated using a mapping error scoring metric. Stochastic analysis illustrates a risk-reward tradeoff between uncertainty and potential coverage for candidate float commands. This paper introduces each of these components of FloatCast and presents initial simulation results using float data from a deployment in the Gulf Stream from July 2024. |
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Interaction of typhoon-driven near-inertial waves with an anticyclone in the Philippine Sea Lazaneo, C.Z., L. Thomas, Z.B. Szuts, J.M. Cusack, K.-F. Chang, and R.K. Shearman, "Interaction of typhoon-driven near-inertial waves with an anticyclone in the Philippine Sea," Oceanography, 37, 68-81, doi:10.5670/oceanog.2024.308, 2024. |
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1 Dec 2024 |
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The Philippine Sea in the western Pacific is a region with high mesoscale eddy kinetic energy that is buffeted by intense typhoons. Such typhoons generate strong near-inertial waves (NIWs), making this region ideal for studying interactions between typhoon-driven NIWs and mesoscale eddies. To study such interactions, a field campaign was conducted in the Philippine Sea that targeted an anticyclonic eddy after the passage of Super Typhoon Mawar. The study was part of the US Office of Naval Research Departmental Research Initiative ARCTERX (Island Arc Turbulent Eddy Regional Exchange). During the campaign, ship and float-based velocity measurements revealed layers of intense vertical shear oscillating at slightly sub-inertial frequencies in the anticyclone. The shear layers were stronger toward the eddy center and coincided with patches of elevated turbulence. An idealized numerical simulation initialized with a symmetric eddy modeled after observations and forced by reanalysis winds was used to study the formation of NIWs by Typhoon Mawar and their interactions with the eddy. The model captured the structure and vertical propagation of the observed shear layers and demonstrated how the dynamics of the NIWs in the anticyclone are consistent with NIW trapping following the theory of ς-refraction. The simulated shear layers were not as intense as those that were observed and could not explain the patches of enhanced turbulence. Processes not included in the model, more specifically the internal tides that are particularly strong in the Philippine Sea, likely contribute to the discrepancy. Energy exchange between the NIWs and the anticyclone diagnosed using the model output was weak, suggesting that typhoon-driven NIWs play a secondary role in the energetics of eddies in the Philippine Sea, or that the idealized nature of the model limited wave-mean flow energy exchange. |
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FlowPilot: Shoreside autonomy for profiling floats Szuts, Z., T. Harrison, T. Curtin, B. Kirby, and B. Ma, "FlowPilot: Shoreside autonomy for profiling floats," Proc., OCEANS, 25-28 September, Biloxi, MS, doi:10.23919/OCEANS52994.2023.10337384 (MTS/IEEE, 2023). |
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11 Dec 2023 |
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Over the last twenty years, profiling floats have revolutionized ocean observations with globally distributed Lagrangian arrays performing fixed vertical sampling cycles. Here we investigate adaptive sampling with an array of inter-dependent floats guided by a software package called FlowPilot, which uses all available float measurements to select park depths that provide favorable drifts based on sampling goals. Drift predictions are performed with multiple prediction methods, including methods that use float data (drift velocity, geostrophic velocity calculations) or from external sources like numerical ocean forecast models. A skill-based weight is assigned to each method based on how accurately it predicts recent drifts. With this generalized approach to prediction, disparate methods can be combined numerically to permit multi-method optimization. The emergent skill of FlowPilot is tested and quantified by numerical simulations that minimize dispersion by keeping a grid of floats close to the center of the deployment box. |
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