|
Zoltan Szuts Head, Ocean Physics Department & Principal Oceanographer zszuts@uw.edu Phone 206-616-7918 |
Research Interests
Salinity Fluxes in the Subtropical Gyre; Baroclinic Waves Across a Subtropical Gyre; Motional Induction
Biosketch
Zoli is an oceanographer who develops and uses new technologies to gain insight into ocean circulation. With a focus on resolving subsurface circulation, he is interested in turbulent motion, internal waves, and ultimately the oceanic transport of heat, salt, and, energy from local to basin scales.
Zoli collects and analyzes observations from a wide variety of profiling floats sampling in custom patterns. He works closely with a team of engineers to develop profiling floats with custom sensors and control firmware that advance ocean observing capabilities. Current and recent projects include: developing autonomous control systems for profiling floats and other autonomous vehicles; predicting ocean circulation from direct but sparse measurements; collecting observations showing proof of concept for an estuarine sensor for remotely measuring salinity stratification; quantifying energy pathways of small-scale waves (inertial and tidal) to large-scale waves (boundary and planetary waves); investigating how water masses in the Florida Straits illuminate the drivers and sources of salt carried by the Atlantic Meridional Overturning Circulation; improving the sampling and control capability of profiling floats that have electromagnetic velocity sensors; designing and building an electromagnetic velocity sampling subsystem that is rated to the deepest part of the ocean (12,000 m deep); and measuring how eddying and mixing in a glacial fjord impacts the overturning heat flux to the glacier.
Department Affiliation
Ocean Physics |
Education
B.A. Biology, Oberlin College, 2001
M.S. Oceanography, University of Washington, 2004
Ph.D. Oceanography, University of Washington, 2008
Projects
|
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. |
More Info |
26 Feb 2024
|
|||||||
|
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. |
|||||||||
Videos
|
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
|
|
Publications |
2000-present and while at APL-UW |
Residence time of Lagrangian profiling floats: Observational assessment and numerical simulation of station keeping ability Ma, B.B., Z.B. Szuts, T.B. Curtin, "Residence time of Lagrangian profiling floats: Observational assessment and numerical simulation of station keeping ability," Earth Space Sci., 13, doi:10.1029/2026EA005018, 2026. |
More Info |
11 Oct 2026 |
|||||||
|
Autonomous profiling floats have revolutionized global ocean observation, yet their passive drift limits their ability to remain within desired regions. Using observations and simulations with numerical ocean models, we quantify observed and potential residence time (RT) of profiling floats and explore station-keeping strategies. Analysis of Argo float trajectories, performing fixed Argo-style sampling, reveals strong spatial variability in RT, from <20 days in western boundary currents to >100 days in subtropical gyre interiors and marginal seas. To quantify RT for alternate sampling behaviors, trajectories are calculated using velocity fields from an assimilative ocean reanalysis model in the dynamically complex western Pacific. A simple selection criterion of choosing to park at the depth of minimum horizontal velocity increases the mean RT by tens to several hundreds of percent, compared with fixed parking at 1,000 m. More sophisticated selection strategies lengthen RT further such as accounting for flow direction or changing the park depth multiple times during one dive. These control methods provide upper limits on the RT by assuming perfect knowledge of the ocean's velocity profile. These results demonstrate the potential of adaptive park-depth selection for mission-specific profiling floats and regional observing applications where enhanced RT is a primary objective. Such strategies are not intended for the core Argo array, for which consistent parking-depth sampling is important for maintaining the long-term middepth velocity record. |
|||||||||
Adaptive sampling in the Philippine Sea using autonomous profiling floats and FloatCast Tolone, J.R., T.W. Harrison, Z.B. Szuts, and D.A. Paley, "Adaptive sampling in the Philippine Sea using autonomous profiling floats and FloatCast," Front. Robot. AI, 13, doi:10.3389/frobt.2026.1813102, 2026. |
More Info |
30 Jul 2026 |
|||||||
|
This work details a method for manipulating the horizontal position of free-drifting ocean-profiling floats using vertical depth control. Profiling floats are often used to study oceanographic processes of varying spatial and temporal scales. Since deploying floats is logistically demanding and expensive, active floats must collect measurements most relevant to the sampling objectives. To that end, we present the FloatCast framework for managing drift in a fleet of floats via vertical depth control. The FloatCast software selects float dive commands that are anticipated to align best with sampling objectives by combining tools from machine learning, optimization, and feedback control. First, an echo state network is used to generate forecasts of ocean surface flow. These forecasts are used to compute float trajectory predictions for each set of candidate dive commands. The performance of each set of dive commands is then ranked using a mapping error scoring metric, and the highest-ranking command set is provided to the floats for their next dive. Reported here are simulation and real-time experimental results from FloatCast control in the Philippine Sea. The simulation results provide insight into the broader FloatCast methodology; experimental results show that this control framework has promise, though it offers no performance guarantees. Although these floats are inherently underactuated, implementing FloatCast can increase the sampling effectiveness of a float array and, in turn, improve scientific data products. |
|||||||||
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). |
More Info |
25 Nov 2025 |
|||||||
|
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. |
|||||||||



