Course on Observing Extreme Phenomena
Why this course?
The Observing Extreme Phenomena
course
This course provides you with the tools and knowledge necessary to understand, analyze, and anticipate large-scale climatic and geophysical events. Learn to interpret data from various sources, including satellites, radar, and numerical models, and to assess the impact of these phenomena on the environment and society. This program is designed for risk management professionals, meteorologists, geophysicists, and anyone interested in the science of natural disasters.
Differential Advantages
- Multidisciplinary Analysis: Integrates knowledge from meteorology, geophysics, oceanography, and remote sensing.
- Cutting-Edge Tools: Uses specialized software for data processing and visualization.
- Real-World Case Studies: Analyzes recent extreme events and learns from the lessons learned.
- Renowned Experts: Receive instruction from leading professionals in natural disaster research.
- Practical Application: Develops decision-making skills for emergency situations.
- Modality: Online
- Level: Cursos
- Hours: 150 H
- Start date: 25-07-2026
Availability: 1 in stock
Who is it aimed at?
- Meteorologists and climatologists seeking to deepen their knowledge of predictive analysis and modeling of extreme events.
- Risk and insurance managers who need a better understanding of the frequency and intensity of phenomena for decision-making.
- Civil protection and emergency professionals who require tools for preparation and response to critical situations.
- Researchers and academics interested in expanding their knowledge of the impact of climate change on the increase in extreme events.
- Environmental science students and related fields seeking specialized training in the observation and analysis of extreme phenomena.
Training flexibility
Adapted to professionals and students: accessible online learning materials, discussion forums and personalized tutoring to answer questions.
Objectives and competencies
Document and analyze anomalous patterns:
“Identify significant deviations from planned routes, unusual speeds, or erratic behavior of other vessels using AIS and radar.”
Predicting and mitigating catastrophic impacts:
“Implement emergency procedures (abandonment, fire, flooding) and coordinate the response with internal and external teams (SAR, port authorities).”
Develop early warning protocols:
Integrate meteorological, oceanographic and maritime traffic data to anticipate risk situations, effectively communicating with stakeholders and escalating according to severity.
Understanding and modeling chaotic dynamics:
“Identify emergent patterns, sensitivity to initial conditions, and predict short-term behavior in nonlinear systems.”
Assessing the resilience of critical infrastructures:
Identify vulnerabilities to natural and anthropogenic threats, proposing effective mitigation measures and contingency plans.
Quantifying uncertainty in rare events:
Employ advanced statistical models and Monte Carlo simulation techniques to estimate probabilities and assess the impact of rare events on decision-making.
Curriculum - Modules
- Comprehensive Maritime Incident Management: protocols, roles, and chain of command for coordinated response
- Operational Planning and Execution: briefing, routes, weather windows, and go/no-go criteria
- Rapid Risk Assessment: criticality matrix, scene control, and decision-making under pressure
- Operational Communication: VHF/GMDSS, standardized reports, and inter-agency liaison
- Tactical Mobility and Safe Boarding: RHIB maneuvers, approach, mooring, and recovery
- Equipment and Technologies: PPE, signaling, satellite tracking, and field data logging
- Immediate Care of the Affected: primary assessment, hypothermia, trauma, and stabilization for evacuation
- Adverse Environmental Conditions: swell, Visibility, flows, and operational mitigation
Simulation and training: critical scenarios, use of VR/AR, and exercises with performance metrics
Documentation and continuous improvement: lessons learned, indicators (MTTA/MTTR), and SOP updates
- Introduction to predictive analytics: concepts, types, and applications in the climate context.
- Fundamentals of climate modeling: statistical, deterministic, and mixed models.
- Climate data sources: global and local databases, remote sensing, historical data.
- Exploratory data analysis techniques: visualization, principal component analysis, clustering.
- Regression models for predicting climate variables: linear, nonlinear, and time series.
- Classification models for identifying climate risks: decision trees, random forests, support vector machines.
- Evaluation and validation of predictive models: performance metrics, cross-validation, sensitivity analysis.
- Future climate scenarios: IPCC projections, models of regionalized climate change.
- Identification and assessment of climate risks: vulnerability, exposure, impact, and probability.
- Climate crisis management: contingency plans, adaptation, resilience, and communication.
‘
- Introduction to Critical Events: Definition, Typology, and Impact
- Threat Identification: Risk, Vulnerability, and Probability Analysis
- Legal and Regulatory Framework: Applicable Laws, Regulations, and Standards
- Contingency Plans: Development, Implementation, and Testing
- Crisis Communication: Strategies, Protocols, and Media Relations
- Human Resource Management: Roles and Responsibilities in the Response
- Logistics and Supplies: Storage, Transportation, and Distribution in Emergencies
- Inter-institutional Coordination: Collaboration with Authorities, NGOs, and Businesses
- Post-Event Recovery: Damage Assessment, Restoration, and Lessons Learned
- Technologies for Critical Event Management: Early Warning Systems, Management Software, and Tools analysis
‘
- Introduction to predictive analytics: concepts, types, and applications in risk management.
- Statistical foundations: probability, distributions, linear regression, and time series.
- Data mining: classification, clustering, and anomaly detection techniques.
- Machine learning: supervised and unsupervised algorithms for risk prediction.
- Data sources: identification, collection, and preprocessing of relevant data.
- Predictive analytics tools: software and platforms for data modeling and visualization.
- Predictive modeling: construction, validation, and evaluation of risk models.
- Early warning systems: design and implementation of model-based early warning systems Predictive analytics.
- Risk mitigation: strategies and actions to reduce the probability and impact of identified risks.
- Ethics and responsibility in predictive analytics and risk management.
‘
- Introduction to Predictive Analytics: Concepts, Types, and Applications in Crisis Management
- Statistical Foundations: Probability, Distributions, and Hypothesis Testing
- Data Collection and Cleaning: Sources, Formats, and Preprocessing
- Regression Models: Linear, Logistic, and Polynomial for Prediction
- Time Series: Trend Analysis, Seasonality, and Cyclical Components
- Classification Algorithms: Decision Trees, SVM, and Neural Networks
- Model Evaluation: Performance Metrics, Cross-Validation, and Fitting
- Software Tools: R, Python, and Predictive Analytics Platforms
- Data Visualization: Dashboards, Interactive Charts, and Effective Communication
- Ethics and responsibility in the use of predictive analytics in crisis management
‘
- System Architecture and Components: Structural design, materials, and subsystems (mechanical, electrical, electronic, and fluid) with selection and assembly criteria for marine environments
- Fundamentals and Principles of Operation: Physical and engineering foundations (thermodynamics, fluid mechanics, electricity, control, and materials) that explain performance and operating limits
- Safety and Environmental (SHE): Risk analysis, PPE, LOTO, hazardous atmospheres, spill and waste management, and emergency response plans
- Applicable Regulations and Standards: IMO/ISO/IEC requirements and local regulations;
- Conformance criteria, certification, and best practices for operation and maintenance
- Inspection, testing, and diagnostics: Visual/dimensional inspection, functional testing, data analysis, and predictive techniques (vibration, thermography, fluid analysis) to identify root causes
- Preventive and predictive maintenance: Hourly/cycle/seasonal plans, lubrication, adjustments, calibrations, consumable replacement, post-service verification, and operational reliability
- Instrumentation, tools, and metrology: Measuring and testing equipment, diagnostic software, calibration and traceability; selection criteria, safe use, and storage
- Onboard integration and interfaces: Mechanical, electrical, fluid, and data compatibility; Sealing and watertightness, EMC/EMI, corrosion protection, and interoperability testing.
Quality, acceptance testing, and commissioning: process and materials control, FAT/SAT, bench and sea trials, go/no-go criteria, and evidence documentation.
Technical documentation and integrated practice: logs, checklists, reports, and a complete case study (safety → diagnosis → intervention → verification → report) applicable to any system.
Plan de estudio - Módulos
- Comprehensive Maritime Incident Management: protocols, roles, and chain of command for coordinated response
- Operational Planning and Execution: briefing, routes, weather windows, and go/no-go criteria
- Rapid Risk Assessment: criticality matrix, scene control, and decision-making under pressure
- Operational Communication: VHF/GMDSS, standardized reports, and inter-agency liaison
- Tactical Mobility and Safe Boarding: RHIB maneuvers, approach, mooring, and recovery
- Equipment and Technologies: PPE, signaling, satellite tracking, and field data logging
- Immediate Care of the Affected: primary assessment, hypothermia, trauma, and stabilization for evacuation
- Adverse Environmental Conditions: swell, Visibility, flows, and operational mitigation
Simulation and training: critical scenarios, use of VR/AR, and exercises with performance metrics
Documentation and continuous improvement: lessons learned, indicators (MTTA/MTTR), and SOP updates
- Introduction to predictive analytics: concepts, types, and applications in the climate context.
- Fundamentals of climate modeling: statistical, deterministic, and mixed models.
- Climate data sources: global and local databases, remote sensing, historical data.
- Exploratory data analysis techniques: visualization, principal component analysis, clustering.
- Regression models for predicting climate variables: linear, nonlinear, and time series.
- Classification models for identifying climate risks: decision trees, random forests, support vector machines.
- Evaluation and validation of predictive models: performance metrics, cross-validation, sensitivity analysis.
- Future climate scenarios: IPCC projections, models of regionalized climate change.
- Identification and assessment of climate risks: vulnerability, exposure, impact, and probability.
- Climate crisis management: contingency plans, adaptation, resilience, and communication.
‘
- Introduction to Critical Events: Definition, Typology, and Impact
- Threat Identification: Risk, Vulnerability, and Probability Analysis
- Legal and Regulatory Framework: Applicable Laws, Regulations, and Standards
- Contingency Plans: Development, Implementation, and Testing
- Crisis Communication: Strategies, Protocols, and Media Relations
- Human Resource Management: Roles and Responsibilities in the Response
- Logistics and Supplies: Storage, Transportation, and Distribution in Emergencies
- Inter-institutional Coordination: Collaboration with Authorities, NGOs, and Businesses
- Post-Event Recovery: Damage Assessment, Restoration, and Lessons Learned
- Technologies for Critical Event Management: Early Warning Systems, Management Software, and Tools analysis
‘
- Introduction to predictive analytics: concepts, types, and applications in risk management.
- Statistical foundations: probability, distributions, linear regression, and time series.
- Data mining: classification, clustering, and anomaly detection techniques.
- Machine learning: supervised and unsupervised algorithms for risk prediction.
- Data sources: identification, collection, and preprocessing of relevant data.
- Predictive analytics tools: software and platforms for data modeling and visualization.
- Predictive modeling: construction, validation, and evaluation of risk models.
- Early warning systems: design and implementation of model-based early warning systems Predictive analytics.
- Risk mitigation: strategies and actions to reduce the probability and impact of identified risks.
- Ethics and responsibility in predictive analytics and risk management.
‘
- Introduction to Predictive Analytics: Concepts, Types, and Applications in Crisis Management
- Statistical Foundations: Probability, Distributions, and Hypothesis Testing
- Data Collection and Cleaning: Sources, Formats, and Preprocessing
- Regression Models: Linear, Logistic, and Polynomial for Prediction
- Time Series: Trend Analysis, Seasonality, and Cyclical Components
- Classification Algorithms: Decision Trees, SVM, and Neural Networks
- Model Evaluation: Performance Metrics, Cross-Validation, and Fitting
- Software Tools: R, Python, and Predictive Analytics Platforms
- Data Visualization: Dashboards, Interactive Charts, and Effective Communication
- Ethics and responsibility in the use of predictive analytics in crisis management
‘
- System Architecture and Components: Structural design, materials, and subsystems (mechanical, electrical, electronic, and fluid) with selection and assembly criteria for marine environments
- Fundamentals and Principles of Operation: Physical and engineering foundations (thermodynamics, fluid mechanics, electricity, control, and materials) that explain performance and operating limits
- Safety and Environmental (SHE): Risk analysis, PPE, LOTO, hazardous atmospheres, spill and waste management, and emergency response plans
- Applicable Regulations and Standards: IMO/ISO/IEC requirements and local regulations;
- Conformance criteria, certification, and best practices for operation and maintenance
- Inspection, testing, and diagnostics: Visual/dimensional inspection, functional testing, data analysis, and predictive techniques (vibration, thermography, fluid analysis) to identify root causes
- Preventive and predictive maintenance: Hourly/cycle/seasonal plans, lubrication, adjustments, calibrations, consumable replacement, post-service verification, and operational reliability
- Instrumentation, tools, and metrology: Measuring and testing equipment, diagnostic software, calibration and traceability; selection criteria, safe use, and storage
- Onboard integration and interfaces: Mechanical, electrical, fluid, and data compatibility; Sealing and watertightness, EMC/EMI, corrosion protection, and interoperability testing.
Quality, acceptance testing, and commissioning: process and materials control, FAT/SAT, bench and sea trials, go/no-go criteria, and evidence documentation.
Technical documentation and integrated practice: logs, checklists, reports, and a complete case study (safety → diagnosis → intervention → verification → report) applicable to any system.
- Introduction to Predictive Analytics: Basic concepts, types of models, and applications in natural disasters.
- Data Collection and Preprocessing: Data sources (remote sensing, historical data, social networks), cleaning, transformation, and normalization.
- Predictive Statistical Models: Linear regression, time series, survival analysis, and their application to predicting natural events.
- Machine Learning for Prediction: Classification and regression algorithms (decision trees, Random Forest, SVM, Neural Networks) applied to disaster prediction.
- Model Evaluation and Validation: Evaluation metrics (accuracy, recall, F1-score, RMSE), cross-validation, and model selection.
- Early Warning Systems
Early Warning: Design and implementation of early warning systems based on predictive models, thresholds, and risk levels.
Data Visualization and Risk Communication: Creation of risk maps, interactive dashboards, and tools to communicate information to the public and authorities.
Simulation and Scenario Modeling: Simulation models to predict the impact of different disaster scenarios (floods, earthquakes, hurricanes).
Integration with Geographic Information Systems (GIS): Use of GIS for the spatial management and analysis of data related to natural disasters.
Ethics and Responsibility in Predictive Analysis: Ethical considerations when using predictive models in risk contexts, biases, and transparency.
- Introduction to Predictive Analytics: Key Concepts and Applications in Catastrophic Risks
- Descriptive Statistics: Measures of Central Tendency, Dispersion, and Data Visualization
- Probability and Distributions: Modeling Uncertainty in Catastrophic Events
- Linear and Nonlinear Regression: Predicting Potential Losses and Damages
- Time Series: Analyzing and Forecasting Temporal Patterns in Disasters
- Classification Models: Identifying Areas of High Risk and Vulnerability
- Scenario Analysis: Simulating Extreme Events and Assessing Impacts
- Data Mining: Discovering Hidden Patterns in Large Datasets
- Machine Learning: Algorithms for Risk Prediction and Optimization strategies
- Ethics and responsibility in the use of predictive models in risk management
‘
- Introduction to Crisis Management: Definition, Typology, and Life Cycle
- Risk Analysis: Identification, Assessment, and Prioritization of Threats
- Predictive Modeling: Time Series, Regression, and Machine Learning
- Information Sources: Historical Data, Sensors, and Social Networks
- Early Warning Systems: Thresholds, Triggers, and Notifications
- Contingency Plans: Strategies, Resources, and Responsibilities
- Crisis Communication: Protocols, Channels, and Key Messages
- Drills and Exercises: Design, Execution, and Evaluation
- Resource Management: Allocation, Mobilization, and Optimization
- Post-Event Analysis: Lessons Learned and continuous improvement
‘
- Introduction to predictive analytics: concepts, types, and applications in critical events
- Fundamentals of statistics and probability: distributions, hypothesis testing, confidence intervals
- Predictive analytics tools: software, programming languages (R, Python), libraries
- Data collection and preprocessing: cleaning, transformation, integration, and reduction
- Regression models: linear, logistic, polynomial, and their application in risk prediction
- Time series models: ARIMA, Prophet, and their use in anomaly and trend detection
- Supervised machine learning: classification and regression for critical event prediction
- Unsupervised machine learning: clustering and reduction of Dimensionality for pattern identification
Model evaluation and validation: performance metrics, overfitting and underfitting, parameter tuning
Communicating and visualizing results: creating reports and dashboards for decision-making
‘
Career opportunities
- Specialized Meteorologist/Climatologist: Prediction and analysis of extreme weather events for government entities, insurance companies, or consulting firms.
- Scientific Researcher: Participation in research projects on climate change and extreme weather events at universities or research centers.
- Risk Analyst: Assessment of the impact of extreme weather events on infrastructure, populations, and ecosystems for insurance companies, reinsurance companies, or risk consultancies.
- Emergency Manager: Coordination of the response to natural disasters in government agencies, NGOs, or private companies.
- Environmental Consultant: Advising companies and governments on adaptation and mitigation measures to address the effects of climate change and extreme weather events.
- Science Journalist/Communicator: Dissemination of information on extreme weather events and climate change in the media. or science communication agencies.
- Environmental Educator: Design and implementation of educational programs on climate change and extreme weather events for schools, universities, or environmental education centers.
- Predictive Model Developer: Creation and improvement of computer models to predict the occurrence and impact of extreme weather events.
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Admission requirements
Academic/professional profile:
Degree/Bachelor's degree in Nautical Science/Maritime Transport, Naval/Marine Engineering, or a related field; or proven professional experience in bridge/operations.
Language proficiency:
Recommended functional maritime English (SMCP) for simulations and technical materials.
5. Induction
Updated resume, copy of degree or seaman's book, ID card/passport, letter of motivation.
Technical requirements (for online):
Equipment with camera/microphone, stable connection, ≥ 24” monitor recommended for ECDIS/Radar-ARPA.
Admission process and dates
1. Online
application
(form + documents).
2. Academic review and interview
(profile/objectives/schedule compatibility).
3. Admission decision
(+ scholarship proposal if applicable).
4. Reservation of place
(deposit) and registration.
5. Induction
(access to campus, calendars, simulator guides).
Scholarships and grants
- Identification: Learn to recognize and classify the main extreme weather phenomena.
- Analysis: Master data interpretation techniques and models to predict their evolution.
- Impact: Understand the effects of these events on the environment and communities.
- Mitigation: Explore prevention and adaptation strategies to reduce risks.
- Tools: Use specialized software and platforms for monitoring and early warning.
Testimonials
During training in observing extreme phenomena, my analysis of the extratropical cyclone “Zeta” accurately predicted its unusual trajectory and rapid intensification, anticipating the severe impact on the Canary Islands 24 hours earlier than the main models, allowing local authorities to prepare more effectively.
I mastered marine weather forecasting, using numerical models and satellite data to accurately predict ocean conditions, which significantly improved the safety and efficiency of my company’s maritime operations.
I successfully predicted the trajectory and intensity of a Category 4 hurricane 48 hours in advance, using satellite data and advanced atmospheric models, which allowed local authorities to evacuate coastal populations in time and minimize damage.
During my training in observing extreme phenomena, my analysis of satellite data allowed me to identify an unusual pattern of ocean warming prior to a Category 4 hurricane. This information, shared with the relevant authorities, contributed to an early and effective evacuation, significantly minimizing human losses in the affected region.
Frequently asked questions
Unusual and large-scale climatic, geological, biological or social events.
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Meteorological and climatic phenomena such as heat waves, droughts, floods, hurricanes, tornadoes, snowstorms, etc., as well as other geophysical events such as earthquakes, tsunamis and volcanic eruptions.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna University + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
- Introduction to predictive analytics: concepts, types, and applications in critical events
- Fundamentals of statistics and probability: distributions, hypothesis testing, confidence intervals
- Predictive analytics tools: software, programming languages (R, Python), libraries
- Data collection and preprocessing: cleaning, transformation, integration, and reduction
- Regression models: linear, logistic, polynomial, and their application in risk prediction
- Time series models: ARIMA, Prophet, and their use in anomaly and trend detection
- Supervised machine learning: classification and regression for critical event prediction
- Unsupervised machine learning: clustering and reduction of Dimensionality for pattern identification
Model evaluation and validation: performance metrics, overfitting and underfitting, parameter tuning
Communicating and visualizing results: creating reports and dashboards for decision-making
‘
Request information
- Complete the Application Form
- Attach your CV/Qualifications (if you have them to hand).
- Indicate your preferred cohort (January/May/September) and whether you want the hybrid option with simulator sessions.
Teachers
Eng. Tomás Riera
Full Professor
Eng. Tomás Riera
Full Professor
Naval engineer specializing in the selection, integration, and optimization of propulsion systems for yachts, high-speed craft, and service vessels.
Eng. Sofía Marquina
Full Professor
Eng. Sofía Marquina
Full Professor
Materials engineer specializing in marine composites and corrosion protection systems for ships, yachts, and port structures.
Eng. Javier Bañuls
Full Professor
Eng. Javier Bañuls
Full Professor
Naval electrical engineer with over fifteen years of experience in the design, integration, and operation of power and automation systems.
Dr. Nuria Llobregat
Full Professor
Dr. Nuria Llobregat
Full Professor
Specialist in meteorological and operational decision-making for coastal and offshore navigation, integrating wind, wave, and current models.
Dr. Pau Ferrer
Full Professor
Dr. Pau Ferrer
Full Professor
Naval engineer with a PhD in applied hydrodynamics and over a decade of experience designing high-performance hulls and marine structures.
Cap. Javier Abaroa (MCA)
Full Professor
Cap. Javier Abaroa (MCA)
Full Professor
MCA-certified captain with command of ocean-going vessels, specializing in advanced maneuvering, navigation management, and bridge safety.