Water: Wave
1. Global Deterministic Wave Prediction System (GDWPS)
Description:
The GDWPS utilizes the third-generation spectral wave model, WaveWatch III® (WW3), to produce wave forecasts up to 240 hours into the future. The model is driven by 10-meter wind data and ice concentration inputs from the Global Deterministic Prediction System (GDPS). Ice concentration data is used to modulate wave growth, attenuating it in areas with 25% to 75% ice coverage and suppressing it entirely where ice concentration exceeds 75%.
Available Variables:
Significant Wave Height: Combined height of wind waves and swell.
Mean Wave Period: Average period of the combined wind waves and swell.
Mean Wave Direction: Average direction from which the combined wind waves and swell are coming.
Peak Wave Period: Period corresponding to the peak energy of the wave spectrum.
Potential AI/ML Applications:
Developing predictive models for maritime navigation safety.
Enhancing coastal management through improved wave forecasting.
Supporting offshore operations by predicting hazardous wave conditions.
2. Global Ensemble Wave Prediction System (GEWPS)
Description:
The GEWPS employs the WaveWatch III® (WW3) model to generate probabilistic wave forecasts extending up to 16 days into the future. It comprises 20 ensemble members and a control member, all forced by 10-meter wind data from the Global Ensemble Prediction System (GEPS).
Available Variables:
Probability of Exceedance: Likelihood of wave parameters exceeding predefined thresholds.
Ensemble Mean Significant Wave Height: Average significant wave height across ensemble members.
Ensemble Spread: Measure of uncertainty among ensemble forecasts.
Potential AI/ML Applications:
Quantifying uncertainty in wave forecasts for risk assessment.
Improving decision-making in maritime operations through probabilistic forecasting.
Training AI models to predict extreme wave events by analyzing ensemble outputs.
3. Regional Deterministic Wave Prediction System (RDWPS)
Description:
The RDWPS provides high-resolution wave forecasts for specific regional domains. It utilizes the WaveWatch III® model, tailored to capture finer-scale wave dynamics influenced by regional geographical features and localized atmospheric conditions.
Available Variables:
Significant Wave Height: Height of the highest third of waves.
Mean Wave Period: Average time interval between waves.
Mean Wave Direction: Predominant direction of wave propagation.
Wave Energy Spectrum: Distribution of wave energy across different frequencies and directions.
Potential AI/ML Applications:
Enhancing localized wave forecasts for coastal communities.
Supporting harbor operations by predicting wave conditions affecting port accessibility.
Improving recreational marine activity planning through detailed regional wave predictions.
4. Regional Ensemble Wave Prediction System (REWPS)
Description:
The REWPS offers probabilistic wave forecasts for regional areas, combining multiple model runs to assess forecast uncertainty. It is designed to provide detailed ensemble-based wave predictions, aiding in risk assessment and decision-making for regional maritime activities.
Available Variables:
Probability of Exceedance: Chance of wave parameters surpassing certain thresholds.
Ensemble Mean Significant Wave Height: Average wave height prediction across ensemble members.
Ensemble Spread: Degree of variability among ensemble forecasts, indicating confidence levels.
Potential AI/ML Applications:
Assessing regional maritime risks by analyzing probabilistic wave forecasts.
Developing AI models to predict coastal flooding events influenced by wave activity.
Enhancing search and rescue operations through improved understanding of wave forecast uncertainties.
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