Master’s degree1,500 h · Online · 11 modules

Master's in Physical Oceanography and Marine Modeling

This master's takes you into the physical processes that govern the oceans, from surface currents to the abyssal depths. You will learn to use and develop state-of-the-art numerical models to understand and predict ocean behavior, its interaction with the atmosphere and its impact on the global climate, with a strong focus on extreme events such as storm surges, tsunamis and coastal flooding, on satellite observation and on operational forecasting for coastal risk management and the sustainable management of marine ecosystems.

Duration1,500 h

ModeOnline

Start1 December

Modules11

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About the program

What is the Master's in Physical Oceanography and Marine Modeling?

This master's connects the physics of the ocean with the tools used to observe and forecast it. You will study coastal dynamics, Coriolis, Ekman transport and pressure gradients; waves, tidal currents, coastal circulation and stratification; air-sea exchange of momentum and heat; geophysical fluid dynamics, vorticity and turbulence; 2D and 3D hydrodynamic models with finite difference, finite volume and finite element schemes and tools such as ROMS, ADCIRC, Delft3D, HYCOM, MITgcm, FVCOM and SWAN; the simulation of storm surges, tsunamis, tropical cyclones and extreme waves; multiscale operational forecasting, downscaling and data assimilation with variational methods and Kalman filters; satellite remote sensing with altimetry, SAR, scatterometers and hyperspectral sensors; coupled biogeochemical modeling; and scientific programming in Python, NetCDF and CF conventions, high-performance and cloud computing and machine learning.

What sets this master's apart: rather than a general course on ocean models, it is oriented toward physical oceanography in service of risk and environmental management, with extreme events, early warning systems and decision support running through the whole program; it combines numerical modeling with satellite and in situ observation so that you learn how each one validates and improves the other; it includes a dedicated module on scientific programming, data formats, reproducibility and computing infrastructure; and it closes with a Final Project in which you develop an integrated modeling and remote sensing system to predict extreme ocean events and apply it to the sustainable management of marine ecosystems.

Program typeMaster’s degree

ModeOnline

Duration1,500 hours

Modules11

Start1 December

Program highlights

Ocean and coastal physics: Coriolis, Ekman transport, waves, tides, stratification, turbulence and air-sea interaction.
Hydrodynamic modeling with ROMS, ADCIRC, Delft3D, HYCOM, MITgcm, FVCOM and SWAN.
Extreme events: storm surges, tsunamis, tropical and extratropical storms, extreme waves and coastal flooding.
Operational multiscale forecasting, downscaling and data assimilation with variational methods and Kalman filters.
Satellite remote sensing (altimetry, SAR, scatterometry, hyperspectral) and scientific programming in Python with HPC.
Final Project: an integrated modeling and remote sensing system for predicting extreme ocean events.

Syllabus

Syllabus of the Master's in Physical Oceanography and Marine Modeling

11 modules. Select a module to see its content.

Module 1

Coastal dynamics and numerical modeling for predicting extreme ocean phenomena

1.1. Physical foundations of coastal dynamics: Coriolis forces, the Ekman effect and pressure gradients in coastal zones
1.2. Coastal hydrodynamic processes: waves, tidal currents, longshore circulation and thermal stratification
1.3. Air-sea interaction: momentum transfer, heat exchange and mixing processes in coastal zones
1.4. Numerical modeling applied to coastal dynamics: mathematical formulation, discretization and solution techniques
1.5. Implementation of hydrodynamic models: input parameters and initial and boundary conditions in open and semi-enclosed systems
1.6. Wave and storm prediction models: integration of observational and satellite data to improve forecasts
1.7. Numerical simulation of extreme ocean phenomena: storm surges, tsunamis and coastal flooding events
1.8. Validation and calibration of coastal models: statistical techniques, sensitivity analysis and uncertainty assessment
1.9. Practical applications in risk management: design of early warning systems and mitigation plans for extreme events
1.10. Case studies and analysis of recent events: methodology, results and lessons for continuous improvement of coastal modeling

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 2

Advanced foundations of ocean hydrodynamics and applied computational simulation techniques

2.1. Fundamental principles of ocean hydrodynamics: Coriolis forces, hydrostatic pressure and geostrophic gradients in marine environments
2.2. Basic and advanced equations of motion: derivation and application of the Navier-Stokes equations to marine fluids and their simplification through geophysical approximations
2.3. Vorticity and turbulence dynamics in ocean fluids: generation, transport and dissipation at mesoscale and submesoscale
2.4. Mathematical models of ocean circulation: numerical techniques for solving three-dimensional and coupled hydrodynamic models
2.5. Advanced computational simulation: finite element and finite volume methods for spatial and temporal discretization of marine dynamics
2.6. Analysis and validation of model data: integrating satellite observations, buoys and CTD profiles to calibrate and verify ocean models
2.7. Modeling tidal currents and coastal circulation: atmospheric, tidal and thermohaline forcing to predict local and regional patterns
2.8. Ocean-atmosphere interaction studies: coupled modeling and analysis of physical and chemical processes at the air-sea interface
2.9. Supercomputing and scalability in marine modeling: code optimization and parallelization for high-resolution simulations
2.10. Practical applications: prediction of extreme events, modeling of pollutant transport and support for sustainable marine resource management

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 3

Advanced numerical modeling of coastal dynamics and prediction of extreme ocean phenomena

3.1. Mathematical and physical foundations of numerical modeling for coastal dynamics: Navier-Stokes equations, conservation of mass and momentum and energy balances
3.2. Numerical hydrodynamic models for coastal environments: setup, parameterization and validation of models such as ADCIRC, ROMS and Delft3D
3.3. Air-sea interaction processes: dynamic coupling in surface and subsurface marine models
3.4. Simulation of waves and coastal currents: spectral modeling, data assimilation and space-time analysis in geographically complex areas
3.5. Advanced prediction of extreme ocean phenomena: tropical cyclones, tsunamis and storm surges through integrated hydrodynamic and hydraulic modeling
3.6. Implementation and evaluation of numerical schemes: finite differences, finite elements and finite volumes applied to coastal dynamics
3.7. Incorporating observational and remote sensing data into predictive models: buoy networks, coastal radar and satellites
3.8. Probabilistic analysis and uncertainty scenarios for coastal risk management based on numerical model output
3.9. Computational optimization and parallelization of models for high temporal and spatial resolution
3.10. Practical applications: early warning systems for tsunamis and storm surges and modeling of erosion and sedimentation on beaches and estuaries

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 4

Multiscale operational forecasting: hydrodynamic models, data assimilation and HPC for coastal risk management

4.1. Foundations of physical oceanography applied to modeling: thermohaline properties, coastal dynamics and temporal and spatial scales of ocean processes
4.2. Principles and mathematical formulation of hydrodynamic models: Navier-Stokes equations, hydrostatic and non-hydrostatic approximations and parameterization of turbulence and dissipation
4.3. Implementation and configuration of multiscale numerical models: coupling regional, coastal and high-resolution models for coherent and accurate simulations
4.4. Advanced assimilation of ocean and atmospheric data: variational techniques, Kalman filters, optimization and real-time uncertainty assessment
4.5. Integration of in situ and satellite observations to dynamically improve model states: floating platforms, buoys, HF radar and remote sensors
4.6. Supercomputing applied to physical oceanography: HPC architectures, parallelization, scalability and algorithm optimization for operational modeling
4.7. Detailed study of extreme coastal events: waves, storms and storm surges and their prediction with physical and statistical models
4.8. Development and analysis of operational forecast products for risk management decisions: early warnings, impact simulations and contingency planning support
4.9. Implementation of integrated modeling systems: coupling frameworks, data interoperability and standards for multiscale prediction
4.10. Applied cases and international experience in coastal management through operational modeling: environmental impact assessment, spatial planning and marine risk mitigation

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 5

Advanced remote sensing and data processing techniques for analyzing and monitoring complex ocean systems

5.1. Foundations of ocean remote sensing: physical principles, sensor types and satellite platforms
5.2. Advanced satellite image processing: radiometric, atmospheric and geometric correction of ocean data
5.3. Spectral and multispectral analysis for characterizing ocean parameters: chlorophyll, sea surface temperature and turbidity
5.4. Synthetic aperture radar (SAR) for monitoring currents, waves and sea surface structure
5.5. Integrating remote sensing data with numerical ocean dynamics models: data assimilation and validation of results
5.6. Development and analysis of time series to study complex ocean phenomena and their spatial variability
5.7. Machine learning and artificial intelligence for classifying and segmenting marine images
5.8. Real-time monitoring of extreme ocean events with remote sensing systems and in situ sensors
5.9. Advanced interpretation and visualization of multidimensional data for marine management decisions
5.10. Current and future trends in ocean remote sensing: hyperspectral satellites, autonomous sensor networks and integrated modeling

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 6

Numerical modeling of extreme ocean phenomena, multiscale prediction and remote sensing for the sustainable management of marine ecosystems

6.1. Advanced foundations of ocean dynamics: Navier-Stokes equations applied to geophysical fluids and hydrodynamic simplifications for marine environments
6.2. Numerical hydrodynamic models: computational grids, spatial and temporal discretization, finite methods and finite volumes, stability and convergence
6.3. Simulation and analysis of extreme ocean phenomena: storm surges, extratropical storms, tsunamis and extreme waves, with real cases and validation against observations
6.4. Multiscale prediction in oceanography: integration of regional and global models, ocean-atmosphere coupling and dynamic and statistical downscaling for coastal and marine scenarios
6.5. Advanced remote sensing technologies: altimetry satellites, radiometers, scatterometers, hyperspectral sensors and LIDAR systems for continuous monitoring of ocean variables
6.6. Remote data processing: calibration, atmospheric correction, multispectral analysis and data fusion for accurate estimation of critical ocean parameters
6.7. Sustainable management of marine ecosystems through modeling: integrating physical, chemical and biological variables to assess climate and human impacts on biodiversity and fisheries
6.8. Development of early warning systems based on numerical modeling and remote sensing, validation protocols and effective communication for coastal risk mitigation
6.9. Specialized software and scientific platforms: expert use of ROMS, HYCOM, Delft3D, SWAN and geospatial visualization tools
6.10. Applied case studies: marine spatial planning, coastal land-use planning, adaptive management in the face of climate change and restoration of vulnerable habitats

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 7

Advanced numerical modeling and multiscale prediction systems for the sustainable management of ocean ecosystems

7.1. Mathematical foundations of numerical modeling in oceanography: partial differential equations, discrete methods and numerical stability
7.2. Geophysical fluid dynamics applied to oceanography: scale analysis, Earth's rotation, the Coriolis effect and tidal forces
7.3. Advanced hydrodynamic models: Navier-Stokes, boundary layer models and turbulence parameterization in coastal and ocean environments
7.4. Implementation of sophisticated numerical schemes: finite differences, finite volumes and finite elements for simulating currents and waves
7.5. Integration of observational ocean data: assimilation of satellite, buoy and autonomous sensor data to improve model accuracy
7.6. Multiscale prediction systems: coupling regional and global models for the dynamic characterization of marine ecosystems
7.7. Coupled biogeochemical modeling: simulating biological processes and their interaction with ocean physics in the vertical and horizontal structure
7.8. Computational optimization and parallelization: high-resolution simulations and reduced computing times using HPC
7.9. Practical applications for sustainable management: environmental impact assessment, climate risk mitigation and planning of marine protected areas
7.10. Development and validation of operational early warning systems: integrating numerical modeling with real-time monitoring to protect ecosystems

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 8

Multiscale ocean process dynamics and integrated modeling for environmental management and the prediction of critical events

8.1. Foundations of ocean dynamics: physical properties of water, equations of motion and conservation of mass and energy in marine fluids
8.2. Multiscale processes in physical oceanography: from microscale turbulence to the global thermohaline circulation
8.3. Coastal-ocean interaction: coastal phenomena, upwelling and their impact on the marine ecosystem
8.4. Integrated numerical modeling: discretization techniques, numerical schemes and computational methods applied to ocean fluids
8.5. Advanced hydrodynamic models: 3D, coupled and mesoscale models for simulating currents, tides and waves
8.6. Data assimilation in oceanography: integrating in situ and satellite observations to improve predictive models
8.7. Prediction and monitoring of critical events: modeling cyclones, tsunamis, red tides and pollutant spills
8.8. Modeling-based environmental management tools: applications in coastal planning, impact mitigation and conservation of marine habitats
8.9. Statistical and machine learning methods for multiscale analysis in physical oceanography
8.10. Practical cases and computer simulations: design, execution and validation of projects addressing real problems in oceanography and marine management

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 9

Advanced numerical modeling and integrated ocean prediction systems for the sustainable management of marine ecosystems

9.1. Theoretical foundations of numerical modeling in oceanography: Navier-Stokes equations, hydrostatic and non-hydrostatic approximations and principles of geophysical fluid dynamics
9.2. Advanced hydrodynamic models: implementation and parameterization of ROMS, MITgcm and FVCOM and their adaptation to coastal areas and the open sea
9.3. Physical processes in the water column: simulation of turbulent mixing, thermal stratification, thermocline dynamics and wind- and tide-driven circulation
9.4. Ocean-atmosphere coupling: techniques for integrating meteorological and ocean models through information exchange and dynamic feedback
9.5. Modeling extreme scenarios: waves, storms and storm surges, and integration of prediction models for coastal risk assessment
9.6. Integrated ocean prediction systems: architecture, ocean databases, assimilation of in situ and remote data and real-time operation
9.7. Numerical methods and computational strategies for optimizing models: discretization, solving partial differential equations, parallelization and HPC in oceanography
9.8. Model validation and verification: statistical analysis, comparison with experimental data, resampling techniques and performance metrics for marine physical modeling
9.9. Applications in the sustainable management of marine ecosystems: current modeling for tracking pollutants, nutrient dispersion and prediction of critical habitats for wildlife
9.10. Practical cases of integrating prediction systems into decision-making: coastal management plans, sustainable maritime spatial planning and response to pollution and extreme climate events

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 10

Scientific programming, high-performance computing and ocean data management

10.1. Python programming for oceanography
10.2. NetCDF formats and CF conventions
10.3. High-performance computing
10.4. Parallelization of models
10.5. Scientific visualization
10.6. Version control and reproducibility
10.7. Management of large data volumes
10.8. Cloud computing
10.9. Machine learning in oceanography
10.10. Computational project

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 11

Final Project

11.1. Advanced theoretical foundations of physical oceanography: fluid dynamics, ocean thermodynamics and momentum transfer
11.2. Mathematical and numerical modeling of ocean phenomena: Navier-Stokes equations, longshore drift and turbulence modeling
11.3. Integration of satellite and in situ remote sensing data: image processing, SAR sensors, altimetry and ocean fluorescence
11.4. Development and calibration of 3D hydrodynamic models for simulating tides, currents and extreme waves
11.5. Implementation of numerical data assimilation algorithms to improve prediction accuracy in coastal and ocean systems
11.6. Assessment and modeling of extreme phenomena: tsunamis, severe storms, storm surges and upwelling events
11.7. Advanced statistical analysis for probabilistic prediction and risk assessment in sensitive marine ecosystems
11.8. Development of integrated GIS platforms for real-time visualization and management of ocean data and forecasts
11.9. Applications in sustainable management: adaptive plans for the conservation and management of marine protected areas facing climate threats
11.10. Case studies and simulations focused on public policy, maritime spatial planning and environmental impact mitigation

At this stage

136 hestimated workload

11 wkweeks at your pace

Plan your pace

Duration and format of the Master's in Physical Oceanography and Marine Modeling

It is 1,500 hours. Tell us how many you can study each week and we will tell you when you will really finish.

Hours per week

4 h30 h

With 12 h per week

months to finish

125 weeks

weeks in total

This is the pace at which most students finish. It fits well with the installment plan.

Faculty

Academic leadership and faculty

The Navalis teaching team: working professionals who teach and supervise the programs.

Dr. Marina AlcoverAcademic Director and Head of Quality Assurance

Eng. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints

Capt. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator

Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory

Dr. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator

Eng. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator

Eng. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line

Eng. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration Coordinator

Objectives

Objectives of the Master's in Physical Oceanography and Marine Modeling

01Develop ocean models to predict the behavior of currents, implementing coupled atmosphere-ocean models, validating them with in situ and satellite observations and calibrating them across spatial and temporal scales.

02Assess the impact of climate change on ocean circulation and marine ecosystems, analyzing data and climate models to anticipate changes in currents, temperature and salinity and their effect on productivity.

03Analyze and forecast extreme ocean events such as storm surges and coastal upwelling, simulating them with numerical models and historical data and communicating risks to stakeholders.

04Support the management of marine resources and operations by modeling currents, tides and waves to optimize navigation routes, port operations and coastal risk mitigation.

05Implement and validate ocean observing systems, configuring and maintaining buoys, profilers and remote sensors and ensuring data quality through calibration and error control.

06Design coastal risk mitigation and adaptation strategies based on ocean models, including early warning systems for storm surges and tsunamis that integrate real-time data and predictive models.

Careers

Career opportunities after the Master's in Physical Oceanography and Marine Modeling

01Operational oceanographer in public agencies or private companies, monitoring the state of the ocean and forecasting its evolution

02Marine modeler, building and calibrating numerical models to simulate ocean processes and extreme events

03Marine environmental consultant, assessing the impact of human activities on the marine environment and proposing mitigation measures

04Ocean data analyst, processing and analyzing large volumes of observational and model data

05Coastal risk and climate change specialist, studying the impact of climate change on oceans and coasts and supporting early warning systems

06Researcher or technical lead in marine renewable energy projects, assessing resources and environmental impacts

Frequently asked questions

Questions about the Master's in Physical Oceanography and Marine Modeling

The dynamics of the ocean and its numerical modeling, applied above all to the prediction of extreme events, operational forecasting and coastal risk and ecosystem management.

The focus is on physical processes: geophysical fluid dynamics, circulation, waves and tides. Coupled biogeochemical modeling and ecosystem applications are included where physics drives them.

The program covers widely used models such as ROMS, ADCIRC, Delft3D, HYCOM, MITgcm, FVCOM and SWAN, along with Python, NetCDF and high-performance computing.

It combines modeling with satellite and in situ observation and orients both toward extreme events, early warning systems and decision support for coastal and marine management.

It is recommended. A dedicated module covers Python for oceanography, data formats, version control, HPC and cloud computing, but basic programming knowledge will help you from the start.

Online, with practical projects using ocean data throughout the modules, and a Final Project in which you build an integrated modeling and remote sensing system for extreme events.

A Navalis Institute master's certificate and a Final Project that documents a complete prediction system, useful as a professional portfolio.

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Online admission

Admission process

No waiting for a call back. If your documents are in order, you can enroll in the same session.

Entry requirements

Profile: a degree in physics, mathematics, marine science, engineering or a related scientific field; knowledge of calculus, physics, programming (Python or MATLAB) and statistics is an advantage.
Language: sufficient command of English to follow the classes and the technical documentation.
Technical resources: a computer able to run scientific software and process data, with a stable internet connection.
Admission: online application, review of your profile and a short interview with the academic team.

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