About

Steve DiMarco is a Professor of Oceanography at Texas A&M University and holds the William R. Bryant Chair in Oceanography. He is also the Director of the Geochemical and Environmental Research Group (GERG) and has a courtesy joint appointment in the Ocean Engineering Department. His research interests include Physical Oceanography, Observing Systems such as Buoys, Autonomous Vehicles, and HF Radar, Operational Oceanography, and studies related to Marginal Seas. DiMarco's educational background includes a Ph.D. in Physics from the University of Texas at Dallas, earned in 1991, along with a Master's degree in Physics from the same university and a Bachelor's degree in Physics from the University of Dallas. His industry experience encompasses roles such as Ocean Observing Team Lead at GERG, and he has held various academic positions at Texas A&M University, progressing from Assistant Research Scientist to Professor. He has been recognized with awards including Fellow of the Marine Technology Society and the College of Geosciences Distinguished Achievement Award for Research. His work involves advancing understanding of ocean dynamics, hypoxia, and the interactions of physical and biological processes in marine environments.

Research topics

• Oceanography
• Geology
• Mechanics
• Physics
• Petrology
• Chemistry
• Optics
• Meteorology
• Environmental science
• Climatology
• Geography
• Geomorphology
• Geophysics
• Materials science

Selected publications

• Quantifying Daily Vertical Heat Flux in the Loop Current System: A Quasi-Lagrangian Assessment of Frontal Hotspots

  2026-04-13

  articleSenior author
  PublisherDOI

• Near-Real-Time Inverse Current and Wave Estimation from Multiple Sensors

  2025-03-04

  articleSenior author

  This paper presents an ANN-based methodology to inversely estimate the significant wave height and near-surface current velocities using field data collected from a buoy (TABS-K) in the Gulf of Mexico. The buoy integrates multiple sensor systems—including an Acoustic Doppler Current Profiler (ADCP), directional current sensor, compass, and inertial measurement unit (IMU)—to capture a variety of signals such as heading, pitch, roll, and accelerations. Since each sensor operates with different sampling intervals and occasionally yields missing data, the raw measurements are first processed on an hourly basis, ensuring consistency and quality control. The proposed approach focuses on predicting the significant wave height and near-surface currents. To improve the estimation accuracy of significant wave height (Hs), the measured accelerations and angular velocities from the IMU are converted into displacement signals via frequency-domain filtering, removing high-frequency noise and low-frequency drift. The proposed ANN model takes these preprocessed signals as inputs and shows a marked improvement in capturing the actual wave conditions, particularly for moderate sea states (Hs < 3 m). In addition, inversely estimating the ocean-surface current velocity using roll, pitch, heading, and other derived variables demonstrates that relatively inexpensive floater-mounted motion sensors can reproduce meaningful current velocity data. Overall, the results underscore the feasibility of near-real-time inverse estimation of the significant wave height and near-surface current velocity by combining cost-effective motion sensors, systematic data preprocessing, and robust ANN-based modeling. This approach promises broader accessibility for oceanographic monitoring, offering an efficient alternative when direct measurement instruments are unavailable or cost-prohibitive.

  PublisherDOI

• Diagnosing coastal processes using machine learning and ocean buoyancy gliders

  Limnology and Oceanography · 2025-02-05 · 1 citations

  articleOpen access

  Abstract Ocean buoyancy gliders provide a comprehensive view of the water column, offering more than simply a snapshot of a single moment in time or space. In this study, we applied the established machine learning method, k‐means clustering, to a glider dataset collected in the summer of 2015 in the northern Gulf of Mexico. Clustering analysis of chromophoric dissolved organic matter and salinity revealed the physical structure of water masses, both vertically within the water column and horizontally along the shelf. Supplementary statistical analyses, including principal component analysis and ANOVA, of individual clusters confirmed the clusters were statistically distinct from one another and provided insights into the factors contributing to their differentiation. The clusters identified in the glider dataset represent water masses variously distinguished by river plumes, wind‐induced upwelling effects, shifts in currents, density‐induced stratification, and biological processes. This study demonstrates that applying machine learning clustering methods to subsurface glider data is a novel technique that enhances the analytical capabilities of both glider and other oceanographic datasets.

  PublisherOA PDFDOI

• The Mini Adaptive Sampling Experiment: Simultaneous Deployment of Multiple Ocean Observing Platforms in the Yucatan Channel

  Marine Technology Society Journal · 2025-01-23

  article1st authorCorresponding

  Abstract We report the preliminary results of the international MASTR (Mini-Adaptive Sampling Test-Run) Experiment under the UGOS (Understanding the Gulf Ocean Systems) Program. The experiment utilized cutting-edge ocean observing technologies, including autonomous platforms, moorings, aircraft, and high-frequency radar, to collect near‐real-time temperature, salinity, and velocity observations in the southeastern Gulf of America and Yucatan Channel. These observations provided critical insights into the complex dynamics of the Loop Current (LC) and its associated eddies, which influence regional circulation and operational predictability. Six ocean buoyancy gliders were deployed in the western Yucatan Strait near Mahahual, México. Four gliders were deployed from January to April 2024; and two, from July to November 2023. The high-frequency radar system near Cancun, México, operational throughout the experiment, observed surface velocity patterns and extreme weather events, including Hurricane Idalia (August 26 to September 2). Radar data captured the spatial and temporal position of the Yucatan Current speed core and revealed the LC system's evolution from a retracted state. Observations exposed the complexity of the LC system, influenced by topographic, tidal, geostrophic, ageostrophic, and wind forcing. Nearly 3,900 temperature and salinity profiles were collected, significantly improving LC and hurricane intensity forecasts. Integrating near‐real-time observations into federal and industry models enhanced forecast accuracy. This experiment underscores the value of adaptive sampling in advancing regional circulation understanding and operational forecasting. Findings will inform the 2025 Grand Adaptive Sampling Experiment, support cost-effective observing systems, and improve offshore risk management and hurricane predictions.

  PublisherDOI

• Role of Midwater Mixed Waves in the Loop Current Separation Events From a Coupled Ocean‐Atmosphere Regional Model and In Situ Observations

  Journal of Geophysical Research Oceans · 2025-04-01 · 1 citations

  articleOpen access

  Abstract A previously uninvestigated necking‐down region in the Gulf of Mexico, associated with the Loop Current eddy (LCE) separations is defined by the 8–16 days variance below the Loop Current system (LCS) around 88.5°W, using an Ocean‐Atmosphere Coupled Regional‐Community Earth System Model (R‐CESM) 9‐year nature run, which reveals the mechanisms of Loop Current (LC) deep dynamics. Scaled wavelet analysis of flow fields in five regions at the 27.5 kg/m 3 potential density layer under the LCS shows that the 8–16 days variance is reproduced by the R‐CESM model dynamics, aligning with in situ observations. This weather band deep variance, identified as the mixed waves located between the mixed Rossby‐gravity waves and Rossby waves in the ocean wave dispersion relationship, is stimulated by the interaction between the penetrating LC and steep topography. Then, these waves are released from the constraints of the topography and become free wave trains. There are three critical regions for the propagating wave trains: the Mississippi Fan, the Yucatan Shelf, and the Florida Escapement. The various wave trains define five distinct scenarios: East Yucatan Shelf (EYSS), West Yucatan Shelf (WYSS), west Florida escarpment (WFES), Mississippi Fan (MFS), and quiescent (QS) scenarios. The scenarios passing through the west necking‐down region can be used to indicate the LCE separations. After the separations, the retracted LC may encounter interference from either the EYSS or the WFES, preventing the reattachments. These 8–16‐day waves offer insights into describing the LC shedding events from the lower layer of the LCS, enhancing the understanding of LCS dynamics.

  PublisherOA PDFDOI

• Impacts of Hurricane Harvey on Coastal Ocean Carbonate Chemistry

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Real-Time Optimal Planning and Adaptive Sampling for Multi-Platform Operations in the Gulf of Mexico

  2025-09-29

  article

  In this paper, we use our MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) including Error Subspace Statistical Estimation (ESSE) largeensemble forecasting to provide real-time probabilistic forecasts for the Gulf of Mexico during the collaborative GRand Adaptive Sampling Experiment (GRASE) from April to September 2025. These forecasts are used for optimal planning and adaptive sampling for multiple platforms deployed during the experiment. We highlight real-time forecasts for probabilistic glider reachability and optimal planning. We showcase mutual information forecasts for optimal adaptive sampling with gliders and floats, maximizing information about the Loop Current (LC) and its eddies (LCEs). We showcase reachability and flow map forecasts for floats, characterizing water mass transports and eddy filamentations. We present probabilistic LCE forecasts using clustering techniques. Finally, we guide two gliders to recovery points using reachability and heading forecasts.

  PublisherDOI

• The Yucatan HF Radar Network: A Multinational Technology Demonstration to Advance Monitoring of Western Boundary Currents

  2025-09-29

  article

  The Yucatan High Frequency Radar (HFR) Network is a trilateral collaboration between the U.S., Mexico, and Cuba designed to monitor surface currents in the Yucatan Channel, a critical segment of the Atlantic Ocean conveyor belt. Two long-range CODAR SeaSonde systems were installed on Isla Contoy and at UNAM's Puerto Morelos facility to provide sustained, near-real-time observations of the Yucatan Current, which feeds the Loop Current and impacts hurricane intensification and climate variability. A key innovation is the use of Automatic Identification System (AIS) ship-tracking data to continuously calibrate receiver antenna patterns, eliminating the need for traditional on-water calibration. Hourly surface currents are processed, quality-controlled using QARTOD standards, and publicly distributed via an ERDDAP server. A derived velocity transect product along the strait's axis is used to analyze flow variability and validate ocean models, which often misrepresent the spatial structure observed by HFR. During the 2024 Mini-Adaptive Sampling Test Run (MASTR), real-time radar products supported autonomous vehicle missions and targeted ship sampling. Expansion to Cuba and additional Mexican sites is underway, positioning the network as a foundation for Caribbeanwide HFR coverage under GOOS and TAC-OOFS to improve ocean forecasting and hurricane prediction.

  PublisherDOI

• Real-Time Probabilistic Reachability Forecasting for Gliders in the Gulf of Mexico

  2024-09-23 · 3 citations

  article

  As part of the Mini-Adaptive Sampling Test Run (MASTR) experiment in the Gulf of Mexico (GoM) region from February to April 2024, we demonstrated real-time deterministic and probabilistic reachability analysis and time-optimal path planning to guide a fleet of four ocean gliders. The governing differential equations for reachability analysis and time-optimal path planning were numerically integrated in real-time and forced by currents from our large-ensemble ocean forecasts. We illustrate the real-time deterministic and probabilistic forward reachability analyses, reachability and path planning for glider pickups, time-optimal path planning for gliders in distress, and planning of future glider deployments. Results show that the actual paths of gliders were contained within our reachable set forecasts and in accord with the dynamic reachability fronts. Our time-optimal headings and paths also predicted real glider motions, even for longer-range predictions of weeks to a month duration. Reachability and time-optimal path planning forecasts were successfully employed for glider recovery. They also enabled exploring options for future glider deployments.

  PublisherDOI

• Real-time Ocean Probabilistic Forecasts, Reachability Analysis, and Adaptive Sampling in the Gulf of Mexico

  2024-09-23 · 6 citations

  article

  The first steps towards integrating autonomous monitoring, probabilistic forecasting, reachability analysis, and adaptive sampling for the Gulf of Mexico were demonstrated in real-time during the collaborative Mini-Adaptive Sampling Test Run (MASTR) ocean experiment, which took place from February to April 2024. The emphasis of this contribution is on the use of the MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) including Error Subspace Statistical Estimation (ESSE) large-ensemble forecasting and path planning systems to predict ocean fields and uncertainties, forecast reachable sets and optimal paths for gliders, and guide sampling aircraft and ocean vehicles toward the most informative observations. Deterministic and probabilistic ocean forecasts are exemplified and linked to the variability of the Loop Current (LC) and LC Eddies, demonstrating predictive skill by real-time comparisons to independent data. Risk forecasts in terms of probabilities of currents exceeding 1.5 kt were provided. The most informative sampling patterns for Remote Ocean Current Imaging System (ROCIS) flights were forecast using mutual information between surface currents and density anomaly. Finally, we guided four underwater gliders using probabilistic reachability and path-planning forecasts.

  PublisherDOI

Frequent coauthors

• Anthony H. Knap

  Texas A&M University

  30 shared

• Robert D. Hetland

  Pacific Northwest National Laboratory

  25 shared

• Ann E. Jochens

  Texas A&M University

  24 shared

• Worth D. Nowlin

  Texas A&M University

  24 shared

• Piers Chapman

  Texas A&M University

  23 shared

• Robert O. Reid

  University of Adelaide

  19 shared

• Travis Miles

  Rutgers Sexual and Reproductive Health and Rights

  18 shared

• Matthew K. Howard

  Texas A&M University

  18 shared

Labs

• Oceanography Department, Texas A&M UniversityPI

Education

• Ph.D., Oceanography

  Texas A&M University

  2000

• M.S., Oceanography

  University of Miami

  1996

• B.S., Oceanography

  University of Miami

  1994

Awards & honors

• Fellow, Marine Technology Society (2020)
• College of Geosciences 2013 Distinguished Achievement Award…
• The Association of Former Student of Texas A&M University, D…