Water: Storm

1. Global Deterministic Storm Surge Prediction System (GDSPS)

Description:

The GDSPS produces water level forecasts using a modified version of the NEMO ocean model. It provides 240-hour forecasts twice daily on a 1/12° resolution grid (approximately 3–9 km). The model is forced by 10-meter winds, sea level pressure, ice concentration, ice velocity, and surface currents from the Global Deterministic Prediction System (GDPS). Additionally, the model's three-dimensional ocean temperature and salinity fields are nudged to values provided by the Global Ice-Ocean Prediction System (GIOPS) and the GDPS. During post-processing, storm surge elevation (ETAS) is derived from total water level (SSH) by harmonic analysis.

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Available Variables:

  • Total Water Level (SSH): Sea surface height relative to a reference level.

  • Storm Surge Elevation (ETAS): Elevation of the storm surge component, derived through harmonic analysis.

Potential AI/ML Applications:

  • Developing predictive models for global storm surge events.

  • Assessing the impact of storm surges on coastal infrastructure.

  • Enhancing early warning systems for coastal flooding.


2. Regional Ensemble Storm Surge Prediction System (RESPS)

Description:

The RESPS produces storm surge forecasts using the DalCoast ocean model, a system based on the depth-integrated, barotropic, and linearized form of the Princeton Ocean Model. It is forced by 10-meter winds and sea level pressure from the Global Ensemble Prediction System (GEPS). This ensemble approach accounts for uncertainties in atmospheric conditions, providing a range of possible storm surge scenarios.

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Available Variables:

  • Ensemble Storm Surge Elevations: Multiple realizations of storm surge heights, reflecting different possible outcomes based on varying atmospheric inputs.

Potential AI/ML Applications:

  • Quantifying uncertainty in storm surge forecasts for specific regions.

  • Improving risk assessments for coastal areas under threat from storm surges.

  • Supporting decision-making in emergency management through probabilistic surge predictions.

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