Master’s degree1,500 h · Online · 11 modules
Master's in Marine Storm Prediction Models
This master's trains you in the most advanced tools and techniques for analysing and forecasting extreme weather at sea. You will learn to interpret oceanographic and atmospheric data, develop high-resolution predictive models and assess the impact of storms on coastal infrastructure and maritime activities, becoming an expert in risk management and decision-making in the face of marine weather threats.
Duration1,500 h
ModeOnline
Start1 December
Modules11
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About the programme
What is the Master's in Marine Storm Prediction Models?
This master's prepares you to forecast marine storms and turn forecasts into timely warnings. You will learn the atmospheric and ocean dynamics behind storm formation and intensification, how climate modes such as ENSO, NAO and MJO influence storm activity, how satellites, radar, buoys and LiDAR observe the ocean and atmosphere, how models such as WRF, ROMS and SWAN are configured and coupled, how 3D-Var, 4D-Var and Kalman filters assimilate observations, how stochastic and machine learning methods quantify uncertainty, and how early warning systems and operational forecasting services are designed.
What sets this master's apart: an end-to-end approach from atmospheric physics to the warning that reaches mariners, ports and civil protection; hands-on work with numerical models, data assimilation and probabilistic ensembles; strong coverage of remote sensing and multi-source data fusion; dedicated treatment of waves, storm surges and their impact on coastal infrastructure; and a Final Project in which you develop an integrated prediction and early warning system for marine storms.
Programme typeMaster’s degree
ModeOnline
Duration1,500 hours
Modules11
Start1 December
Programme highlights
- Storm dynamics: frontal systems, extratropical and tropical cyclones and air-sea interaction.
- Numerical models: WRF, ROMS, SWAN and coupled atmosphere-ocean modelling.
- Remote sensing: satellites, SAR, scatterometers, altimeters, radar, LiDAR and buoys.
- Data assimilation and uncertainty: 3D-Var, 4D-Var, Kalman filters, ensembles and Monte Carlo.
- Machine learning: neural networks and pattern detection for early warning.
- Final Project: an integrated prediction and early warning system for marine storms.
Syllabus
Syllabus of the Master's in Marine Storm Prediction Models
11 modules. Select a module to see its content.
Module 1
Advanced analysis of climate patterns and weather systems for marine storm prediction
- 1.1. Advanced atmospheric dynamics: physical, dynamic and thermodynamic processes in the formation of marine storms
- 1.2. Numerical modelling of the atmosphere: fundamentals, microphysics parameterisation and convection schemes at synoptic scale and mesoscale
- 1.3. Spectral analysis of climate patterns: identifying relevant modes of atmospheric and oceanic oscillation (ENSO, NAO, MJO)
- 1.4. Frontal systems and extratropical cyclones: characterisation, evolution and their role in generating marine storms
- 1.5. Ocean-atmosphere interaction: impact of sea surface temperature (SST) and humidity on the intensification of weather systems
- 1.6. Using remote sensing and real-time satellite data to monitor marine weather systems
- 1.7. Integrating weather radar and ocean buoy data to validate and calibrate predictive models
- 1.8. Statistical systems and machine learning for analysing climate time series and seasonal prediction of extreme events
- 1.9. Configuring and running pre-operational early warning systems based on dynamic and statistical storm models
- 1.10. Case studies: retrospective analysis of historical marine storms and assessment of forecast accuracy
At this stage
136 hestimated workload11 wkweeks at your paceModule 2
Advanced remote sensing and numerical modelling for anticipating extreme coastal events
- 2.1. Fundamentals of remote sensing for coastal meteorology: physical principles, sensor types and satellite platforms (optical, infrared, microwave and synthetic aperture radar, SAR)
- 2.2. Advanced satellite image processing: radiometric calibration, atmospheric correction, multispectral fusion and multi-temporal analysis for early storm detection
- 2.3. Interpreting remote sensing data in real time: combining satellite products with in situ data and modelling critical atmospheric and oceanographic variables
- 2.4. Hydrometeorological numerical modelling in coastal areas: predictive models (WRF, ROMS, SWAN), parameterisation of physical processes and atmosphere-ocean coupling
- 2.5. Data assimilation to improve forecast accuracy: variational methods and Kalman filters applied to marine storms
- 2.6. Simulating and forecasting extreme waves and storm surges: advanced use of spectral models and validation with historical records and observation logs
- 2.7. Developing early warning systems based on artificial intelligence and machine learning for automatic identification of precursor patterns
- 2.8. Operational monitoring and tracking: GIS geospatial platforms for dynamic visualisation and real-time storm risk analysis
- 2.9. Practical cases of numerical modelling in vulnerable coastal regions: retrospective analysis and assessment of forecast accuracy
- 2.10. International regulations and protocols for managing early warnings of extreme weather: integration with local policy and emergency response planning
At this stage
136 hestimated workload11 wkweeks at your paceModule 3
Weather systems, observation and forecasting tools for marine storms
- 3.1. Theoretical fundamentals of atmospheric dynamics: general circulation, pressure, temperature and humidity in marine environments
- 3.2. Identifying and characterising weather systems: tropical and extratropical cyclones, fronts and local disturbances
- 3.3. Advanced satellite and weather radar analysis: interpreting infrared, visible and microwave imagery
- 3.4. Numerical weather prediction models: fundamentals, governing equations, parameterisations and time scales relevant to marine storms
- 3.5. Processing and validating in situ and remote data: ocean buoys, coastal weather stations and marine sensors
- 3.6. Ocean-atmosphere interaction dynamics: energy transfer, wind effects on the sea surface and storm feedbacks
- 3.7. Spectral and statistical analysis of climate variables: time series, trends, extremes and recurrent patterns associated with severe events
- 3.8. Methods for early detection of convective and mesoscale events in marine environments
- 3.9. Correlating global climate indices (ENSO, NAO, AO) with the occurrence and evolution of marine storms
- 3.10. Practical use of software for modelling and advanced visualisation of weather patterns: WRF, HYSPLIT and meteorological GIS
At this stage
136 hestimated workload11 wkweeks at your paceModule 4
Stochastic wave and surge modelling, real-time data assimilation and integrated coastal warning systems
- 4.1. Fundamentals of stochastic modelling of waves and swell: random process theory and its relationship with coastal dynamics
- 4.2. Advanced mathematical models for probabilistic wave simulation: energy spectra, distribution functions and spatio-temporal correlations
- 4.3. Real-time data assimilation: fusing satellite observations, ocean buoys and coastal radar to improve forecasts
- 4.4. Implementing Kalman filters and Bayesian methods for continuous updating of predictive models
- 4.5. Computational optimisation of stochastic systems for efficiency in critical operational environments
- 4.6. Design and architecture of integrated coastal early warning systems: components, interfaces and communication protocols
- 4.7. Linking hydrodynamic models with warning systems: automatic generation of warnings based on probabilistic risk thresholds
- 4.8. Historical marine storm case studies: validating and tuning predictive models with real data
- 4.9. Assessing uncertainty in wave and surge forecasts: metrics, sensitivity analysis and mitigation techniques
- 4.10. Developing real-time visualisation and monitoring platforms: GIS, dashboards and web tools for effective decision-making
At this stage
136 hestimated workload11 wkweeks at your paceModule 5
Atmospheric and ocean dynamics for predictive storm simulation and impact on coastal infrastructure
- 5.1. Fundamentals of atmospheric dynamics: convective structures and processes, vorticity, divergence and transport of energy and momentum in marine storms
- 5.2. Numerical atmospheric circulation models: turbulence parameterisation and cloud microphysics schemes in coastal systems
- 5.3. Atmosphere-ocean interaction: exchange of heat, moisture and momentum in the marine boundary layer
- 5.4. Dynamics of fronts and low-pressure systems: formation, evolution and movement in coastal regions
- 5.5. Hydrodynamic modelling of marine storms: simulating extreme winds, waves and storm surges
- 5.6. Coupled atmosphere-ocean models for predictive forecasting at critical time and space scales
- 5.7. Oceanographic variables: coastal currents, thermoclines, stratification and their influence on storm intensification
- 5.8. Advanced procedures for assimilating satellite and marine station data into predictive models
- 5.9. Assessing the dynamic impact of marine storms on coastal infrastructure: hydrodynamic and structural force analysis
- 5.10. Early warning and mitigation systems: designing, calibrating and validating predictive tools for coastal planning decisions
At this stage
136 hestimated workload11 wkweeks at your paceModule 6
Integrated modelling of climate patterns, remote sensing and stochastic methods for early storm prediction
- 6.1. Theoretical foundations of climate modelling: atmospheric and ocean dynamics, air-sea interaction and time scales
- 6.2. Advanced principles of remote sensing: active and passive sensors, satellite platforms and multispectral image processing
- 6.3. Remote observing systems: weather radar, LiDAR and radiometry for monitoring marine storms
- 6.4. Stochastic modelling of atmospheric phenomena: random processes, Markov chains and Monte Carlo simulation
- 6.5. Multi-source integration: fusing satellite, ocean buoy and weather station data for climatological modelling
- 6.6. Prediction algorithms: deep neural networks, regression models and time series for anticipating extreme events
- 6.7. Optimising and calibrating models with machine learning and cross-validation
- 6.8. Early warning systems: design and implementation, critical thresholds and communication protocols
- 6.9. Assessing uncertainty in climate predictions: sensitivity analysis and error propagation
- 6.10. Practical cases of integrated modelling and simulation for accurate storm prediction in different coastal regions
At this stage
136 hestimated workload11 wkweeks at your paceModule 7
Remote sensing and numerical modelling applied to advanced storm prediction
- 7.1. Fundamentals of remote sensing for marine meteorology: physical principles, active and passive sensors, electromagnetic waves and their interaction with the sea surface
- 7.2. Meteorological and oceanographic satellites: classification, geostationary and polar orbits, key instruments (scatterometers, radiometers, altimeters) and their role in early storm detection
- 7.3. Processing and analysing remote data: extracting meteorological and oceanographic variables, atmospheric correction, sensor calibration and multi-sensor data fusion
- 7.4. Numerical modelling of the atmosphere and ocean: mathematical formulation of prediction models, the Navier-Stokes equations, parameterisation of physical processes and atmosphere-ocean coupling
- 7.5. Configuring and running numerical models: numerical schemes, adaptive meshing, initial and boundary conditions and data assimilation
- 7.6. Simulating and forecasting marine storms: identifying and tracking convective systems, dynamic and thermodynamic analysis of marine cyclones and probabilistic assessment of coastal impact
- 7.7. Validating and verifying prediction models: statistical metrics, comparison with in situ and satellite data, sensitivity analysis and parameter tuning
- 7.8. Integrating early warning systems: operational architecture, real-time information flow and automated algorithms for weather warnings and risk assessment
- 7.9. Practical applications in marine emergency management: response protocols, inter-agency coordination and model-informed decisions to reduce damage and save lives
- 7.10. Technological advances and future trends: artificial intelligence and machine learning for better forecasts, UAV platforms and nanosatellite constellations
At this stage
136 hestimated workload11 wkweeks at your paceModule 8
Advanced data assimilation and predictive modelling for integrated storm risk management
- 8.1. Fundamentals of data assimilation: concepts, history and evolution in marine meteorology
- 8.2. Data sources for marine storms: satellites, ocean buoys, coastal radars and weather stations
- 8.3. Numerical weather prediction models: dynamic, statistical and hybrid
- 8.4. Advanced assimilation techniques: 3D-Var, 4D-Var and extended Kalman filters
- 8.5. Implementing assimilation algorithms in high-resolution regional models for coastal environments
- 8.6. Multi-source data integration: interoperability, bias correction and cross-validation
- 8.7. Stochastic predictive modelling for risk scenarios: uncertainty, sensitivity and probabilistic analysis
- 8.8. Optimising models with machine learning and artificial intelligence applied to storm prediction
- 8.9. Integrated risk management: impact assessment, early warnings and response planning based on dynamic forecasts
- 8.10. Case studies and practical application in coastal management: damage mitigation, urban planning and emergency protocols
At this stage
136 hestimated workload11 wkweeks at your paceModule 9
Integrated storm prediction using remote sensing, numerical modelling and data assimilation
- 9.1. Fundamentals of remote sensing for marine storms: physical principles and active and passive satellite sensors
- 9.2. Processing remote sensing data: atmospheric correction algorithms, extraction of ocean parameters and in situ validation
- 9.3. Numerical modelling of atmosphere and ocean: coupled hydrodynamic and atmospheric models for simulating extreme events
- 9.4. Marine storm dynamics: formation, intensification and decay based on thermodynamic and dynamic variables
- 9.5. Multimodal data assimilation: incorporating satellite observations, ocean buoys and coastal radar into predictive models
- 9.6. Machine learning techniques for improving forecasts and early detection of critical patterns
- 9.7. Validating and tuning models with advanced statistical metrics and probabilistic ensembles
- 9.8. Managing uncertainty in meteorological and oceanographic forecasts: stochastic methods and sensitivity analysis
- 9.9. Implementing integrated early warning systems: architecture, protocols and risk communication to end users
- 9.10. International case studies: detailed analysis of recent events with emphasis on mitigation and response strategies
At this stage
136 hestimated workload11 wkweeks at your paceModule 10
Warning communication, risk management and operational forecasting services
- 10.1. Early warning systems
- 10.2. Marine weather warnings
- 10.3. Communicating risk to mariners and authorities
- 10.4. Response protocols for severe weather
- 10.5. Operational forecasting services
- 10.6. Forecast verification
- 10.7. Working with civil protection
- 10.8. Economic impact of storms
- 10.9. Insurance and climate risk
- 10.10. Designing a warning service
At this stage
136 hestimated workload11 wkweeks at your paceModule 11
Final Project
- 11.1. Introduction to the Final Project: objectives, scope and relevance of an integrated prediction and early warning system for marine storms
- 11.2. Scientific foundations: atmospheric and ocean dynamics in marine storms and the physical and chemical processes involved
- 11.3. Numerical weather and ocean prediction models: selection, parameterisation and validation for extreme coastal events
- 11.4. Satellite and radar remote sensing: advanced image interpretation, pattern detection algorithms and extraction of critical variables
- 11.5. Designing multidimensional databases for real-time meteorological and oceanographic data
- 11.6. Integrating stochastic techniques and probabilistic models to quantify uncertainty and optimise forecasts
- 11.7. Data fusion methods: assimilation and joint processing of satellite information, numerical models and in situ observations
- 11.8. Developing early warning algorithms: threshold criteria, risk analysis and automated warnings for end users
- 11.9. Designing and implementing the integrated system with graphical interfaces for real-time visualisation and monitoring
- 11.10. Operational validation and calibration of the system using historical cases and experimental events
At this stage
136 hestimated workload11 wkweeks at your pacePlan your pace
Duration and format of the Master's in Marine Storm Prediction Models
It is 1,500 hours. Tell us how many you can study each week and we will tell you when you will really finish.
4 h30 h
With 12 h per week
29
months to finish
125 weeks
weeks in total
This is the pace at which most students finish. It fits well with the instalment plan.
Faculty
Academic leadership and faculty
The Navalis teaching team: working professionals who teach and supervise the programmes.
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 CoordinatorObjectives
Objectives of the Master's in Marine Storm Prediction Models
01Optimise coastal risk management by identifying, assessing and mitigating weather, current and traffic risks with forecasting systems and effective communication.
02Improve the accuracy of early warnings by integrating real-time meteorological and oceanographic data and adjusting alert thresholds dynamically.
03Develop impact mitigation strategies through predefined contingency plans adapted to real situations, prioritising people, assets and the environment.
04Drive innovation in predictive technology by developing machine learning algorithms that improve forecast reliability.
05Lead research on extreme weather by designing innovative predictive models in collaboration with scientific institutions.
06Interpret and communicate complex storm data clearly, adapting the level of detail to each audience and using effective visual resources such as maps, charts and simulations.
Careers
Career opportunities after the Master's in Marine Storm Prediction Models
Frequently asked questions
Questions about the Master's in Marine Storm Prediction Models
Developing and applying predictive models for marine storms, based on a solid understanding of the oceanographic and meteorological processes that generate them.
Both. You use atmospheric models to forecast storms and couple them with wave, surge and ocean models to predict sea conditions and coastal impacts.
You work with numerical models such as WRF, ROMS and SWAN, data assimilation methods, GIS platforms and programming environments such as Python, R or MATLAB.
Yes. Several modules apply neural networks, regression and time series methods to improve forecasts and detect precursor patterns of extreme events.
Yes. You design early warning systems and learn how marine warnings, forecast verification and risk communication to mariners, authorities and civil protection work.
A quantitative degree in physics, mathematics, engineering or marine sciences, or experience in modelling, is recommended; basic statistics and programming help you get the most from the programme.
Fully online, through a virtual campus available 24/7, with live sessions that are recorded and tutorials with the teaching staff.
The master's comprises 1,500 hours of study across 11 modules of about 136 hours each and ends with the Final Project. You can progress at your own pace within the schedule set when you enrol.
Related programmes
More programmes in Climate and oceanography
Online admission
Admission process
No waiting for a call back. If your documents are in order, you can enrol in the same session.
Entry requirements
- Profile: marine meteorologists and oceanographers, coastal engineers and port infrastructure managers, offshore energy and shipping professionals who depend on reliable forecasts, researchers in extreme weather, and environmental consultants and emergency managers. A degree in physics, mathematics, engineering or marine sciences, or proven experience in meteorological or oceanographic modelling, is recommended, together with basic statistics and familiarity with Python, R or MATLAB.
- Language: sufficient command of English to follow the classes and the technical documentation.
- Technical resources: a computer capable of running modelling and data analysis software, a high-speed internet connection, an up-to-date browser and basic system administration skills.
- Admission: online application, review of your profile and a short interview with the academic team.
- 01ApplicationYour details, background and documents.
- 02Instant reviewWe check that everything is complete and in order.
- 03EnrolmentChoose full payment or instalments and reserve your place.
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