Real-world rogue wave probabilities

Area of Science:

• Oceanography
• Fluid Dynamics
• Data Science

Background:

• Rogue waves pose significant risks to maritime operations.
• Existing forecasting methods based on simulations and lab experiments have limitations.
• Reliable prediction of rogue waves remains an unsolved challenge.

Purpose of the Study:

• To identify reliable predictors for rogue wave occurrence using extensive observational data.
• To evaluate the effectiveness of traditional rogue wave prediction parameters.
• To understand the underlying mechanisms driving rogue wave formation.

Main Methods:

• Analysis of over one billion ocean surface wave observations.
• Application of data mining and interpretable machine learning techniques.
• Statistical evaluation of various sea state parameters as predictors.

Main Results:

• Traditional parameters like kurtosis and Benjamin-Feir index are weak predictors of rogue wave risk.
• Crest-trough correlation is the dominant predictor across various conditions, explaining significant risk variation.
• For rogue crests, skewness, steepness, and Ursell number are strong predictors, aligning with second-order theory.

Conclusions:

• Linear superposition in bandwidth-limited seas is the primary mechanism for common rogue waves.
• Nonlinear effects play a secondary role in the formation of everyday rogue waves.
• The standard definition of rogue waves based solely on height may lack practical significance.