
Ocean Wave Prediction
Ocean waves are primarily generated by surface winds, although in certain regions they may also be influenced by variations in water temperature and salinity.
- Evidence-led
- Traceable assumptions
- Decision-ready outputs
- Methods proportionate to risk
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Clarity before a decision is made
Ocean Wave Prediction
Clarity before a decision is made
Ocean waves are primarily generated by surface winds, although in certain regions they may also be influenced by variations in water temperature and salinity.
Accurate wave forecasting and real-time wave information are essential for a wide range of marine and coastal activities. In addition to continuous operational forecasting, wave prediction can also be performed for specific weather events, such as forecasting wave heights and their potential impacts during periods of strong winds or storms.

Decision Supported
Define the approach, priorities, and actions for ocean wave prediction using traceable evidence.

Risk Controlled
Environmental impact, design failure, operational disruption, uncontrolled cost, and weak assumptions.

Success Criteria
Comparable options, quantified risk, and implementable recommendations.
What is assessed and why it matters

Observations and initial conditions
This aspect is assessed to clarify its implications for ocean wave prediction.

Current, sea-level, and wave prediction
This aspect is assessed to clarify its implications for ocean wave prediction.

Physical, chemical, and biological parameters
This aspect is assessed to clarify its implications for ocean wave prediction.

Uncertainty and forecast horizon
This aspect is assessed to clarify its implications for ocean wave prediction.

Warning thresholds and information users
This aspect is assessed to clarify its implications for ocean wave prediction.

Dissemination, updates, and evaluation
This aspect is assessed to clarify its implications for ocean wave prediction.
A traceable evidence base

Observations
Field surveys, in-situ measurements, laboratory results, historical records, and operating information as required.

Remote sensing & GIS
Satellite imagery, mapping, spatial analysis, temporal change, and integration of multiple data sources.

Modeling & scenarios
Model setup, calibration, validation, existing–planned–extreme scenarios, and sensitivity analysis.

Quality assurance
Metadata, quality controls, assumptions, limitations, data versions, and processing lineage are documented.
Decision-ready information

Initial assessment & data gaps
Objectives, study area, available data, additional needs, initial risks, and recommended level of detail.

Datasets, maps & indicators
Quality-controlled data, thematic maps, time series, indicators, and comparable visualizations.

Scenarios & risk evaluation
Comparison of existing conditions, alternatives, extremes, sensitivities, consequences, and mitigation options.

Report & executive brief
Methods, results, limitations, recommendations, action priorities, and stakeholder presentation materials.
Benefits for decision makers and policy leaders

Reduce uncertainty
Assumptions, data, variability, and limitations are stated so decision risk is not hidden.

Compare options objectively
Alternative locations, designs, operations, or policies are assessed using consistent indicators.

Optimize cost and time
Data needs and analysis depth are proportionate to risk so resources are used efficiently.

Increase stakeholder confidence
Findings and recommendations are transparent for technical, management, regulatory, and partner review.
A clear process from need to recommendation
- 01

Need definition
Objectives, users, location, project phase, problems, constraints, and the decision to support.
- 02

Scope & work plan
Methods, data, surveys, models, schedule, team, deliverables, review gates, and resource estimate.
- 03

Acquisition & quality control
Collection, inspection, harmonization, documentation, and data-sufficiency assessment.
- 04

Analysis & scenario testing
Processing, modeling, validation, option comparison, sensitivity, and risk evaluation.
- 05

Recommendation & handover
Maps, report, executive brief, presentation, supporting data, and follow-up plan.
Full technical basis and contextOpen this section to read the complete source technical narrative.
Ocean waves are primarily generated by surface winds, although in certain regions they may also be influenced by variations in water temperature and salinity. Wave characteristics are controlled by numerous environmental factors, including bathymetry, seabed roughness, coastal morphology, the geometry of the water body (such as straits, bays, or open oceans), offshore structures, and other physical features. These factors give rise to complex wave phenomena, including wave refraction, diffraction, shoaling, reflection, and other transformation processes that significantly influence wave behavior in coastal and offshore environments.
Accurate wave forecasting and real-time wave information are essential for a wide range of marine and coastal activities. In addition to continuous operational forecasting, wave prediction can also be performed for specific weather events, such as forecasting wave heights and their potential impacts during periods of strong winds or storms. Large waves can disrupt numerous human activities, particularly maritime transportation, where navigation safety is a primary concern. Extreme wave events may also cause coastal flooding, damage to coastal infrastructure, shoreline erosion, and morfological changes along the coast. Offshore facilities, including oil and gas platforms, are similarly vulnerable to severe wave conditions that can interrupt operations and compromise personnel safety. Reliable wave forecasting is therefore critical for ensuring the safety, efficiency, and continuity of marine operations.
Advanced numerical modeling provides a fast and effective approach for predicting ocean wave conditions. Operational forecasting systems are developed through several stages. The first stage defines the spatial scale of the application, ranging from local (ports, river mouths, coastal waters), regional (straits, bays, and coastal seas), to global (open ocean). The second stage involves selecting the most appropriate wave modeling modules based on the characteristics of the study area and the dominant wave-generating mechanisms. The final stage establishes an operational forecasting system capable of delivering continuous, accurate, and timely wave predictions.
Hydrodynamic Modeling is used to simulate wave conditions associated with current circulation, while specialized wave models—including Spectral Wave Modeling, Nearshore Spectral Wave Modeling, Parabolic Mild Slope Modeling, Elliptic Mild Slope Modeling, Boussinesq Wave Modeling, and Wave Analysis Tools—are applied to simulate wave generation, propagation, transformation, and nearshore processes according to the physical characteristics of each marine environment. Marine Geographic Information System (Marine GIS) integrates forecasting results with environmental, operational, and spatial datasets, providing a comprehensive platform for visualization, monitoring, and decision support.
Share the need, location, available data, and the decision to be supported.
The CORZ team will review the objective, scope, data availability, risk level, schedule, and required outputs to prepare a proportionate approach.
- Location and project phase
- Decision or objective to support
- Primary problems and risks
- Available data
- Expected outputs and schedule