CONTEXTField conditions and systems being assessed
Training
Data Processing
Various types of data must be prepared as input data for modeling, and these data are obtained from many data providers, both national and international institutions.
- Evidence-led
- Traceable assumptions
- Decision-ready outputs
- Methods proportionate to risk
Visual Leaflet
One-Page Visual Summary for Quick Briefing
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- Presentation-ready visual
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Use this leaflet as a concise visual entry point before moving into the more detailed technical explanation.
ANALYSISIntegrated data, methods, and modelling
DECISIONVisual outputs and actionable recommendations
Executive Brief
Clarity before a decision is made
01Evidence-led
02Traceable assumptions
03Decision-ready outputs
04Methods proportionate to risk
Executive Brief
Data Processing
Clarity before a decision is made
Various types of data must be prepared as input data for modeling, and these data are obtained from many data providers, both national and international institutions.
These data must be adjusted to the data format structure and resolution accepted by the model. In addition, boundary condition data require time-series data with specific structures and formats.
Decision Supported
Define the approach, priorities, and actions for data processing using traceable evidence.
Risk Controlled
Generic content, poor fit with working tools, and limited practical transfer.
Success Criteria
Participants can apply methods to relevant data and cases.
Analysis Scope
What is assessed and why it matters
Participant profile and capability needs
This aspect is assessed to clarify its implications for data processing.
Concepts and working principles
This aspect is assessed to clarify its implications for data processing.
Exercises using data and cases
This aspect is assessed to clarify its implications for data processing.
Software and workflow
This aspect is assessed to clarify its implications for data processing.
Result interpretation and quality control
This aspect is assessed to clarify its implications for data processing.
Capability evaluation and follow-up
This aspect is assessed to clarify its implications for data processing.
Data & Methods
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.
Core Deliverables
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.
Decision Value
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.
Delivery Path
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.
Various types of data must be prepared as input data for modeling, and these data are obtained from many data providers, both national and international institutions. All of these data need to be prepared according to the data format required by the model. The types of data that need to be prepared include bathymetric data and surface roughness characteristics, wind data, sea level prediction data as boundary conditions, initial condition data, and other related data.
These data must be adjusted to the data format structure and resolution accepted by the model. In addition, boundary condition data require time-series data with specific structures and formats. The more model scenarios that need to be developed, the more data must be prepared for modeling purposes. The process becomes even more complex when the model being developed is an operational model that is continuously used for prediction purposes.
The ability and expertise to prepare input data for modeling are highly important. Various national and international databases are available in different data formats, and data standardization is required to conduct modeling properly. This training aims to develop the skills needed to prepare model input data, so participants can become proficient in handling various available data formats.
Related Options
Explore related topics and services
Modeling
Many modeling software packages are currently available, both commercial and non-commercial.
Learn more →Data Interpretation
Unlike other types of training, modeling technology training does not focus only on technical skills, such as how to run or use available modeling software.
Learn more →Next Step
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.
Useful initial information
- Location and project phase
- Decision or objective to support
- Primary problems and risks
- Available data
- Expected outputs and schedule