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Spatial Extremes and Shapes of Large Waves: A Study in Wave Dynamics

Explore the behavior of large waves in two dimensions, using spatial measurements and advanced tools like Piterbarg and Slepian. Dive into simulations and the implications for stochastic surfaces. Compare Gaussian theory with Lagrangian models for wave prediction.

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Spatial Extremes and Shapes of Large Waves: A Study in Wave Dynamics

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  1. SPATIAL EXTREMES AND SHAPES OF LARGE WAVES Hervé Socquet-Juglard and Kristian B. Dysthe,Univ. of Bergen, Karsten Trulsen, Univ. of Oslo, Sébastien Fouques, Jingdong Liu, and Harald E. Krogstad, NTNU, Norway

  2. From one to two dimensions • Spatial measurements • Two tools: Piterbarg and Slepian • What do the simulations show? • Is the standard model good enough?

  3. Extreme waves at Ekofisk November 1985 (Hm0~11m) Four simultaneous EMI laser time series 5m 15 Laser 2 Laser 3 Laser 4 10 Laser 5 5 Laser 2-5 [m] 0 -5 -10 750 760 770 780 790 800 810 820 830 840 850 Time (s)

  4. ASAR US BUOY WAM ESA Envisat ASAR imagette

  5. Inverted sea surface elevation from ERS-2 complex imagette (Prof. Susanne Lehner, DLR/RSMAS) Range: 512 pixels, Drange=20m, Azimuth: 256 pixels, Dazimuth=16m

  6. What about extremes for stochastic surfaces? Vladimir I. Piterbarg: Asymptotic Methods in the Theory of Gaussian Processes and Fields, AMS Translations, 1996 (Theorem 14.1).

  7. The 2-D version of Piterbarg’s theorem:

  8. Piterbarg’s theorem settles the benchmark Gaussiancase very nicely for all reasonably sized regions • The distribution depends only on the size of the region relativeto the area of ”one wave” • The distribution converges to the Gumbel form when

  9. Storm ”Gauss”: 100x100km, Tz = 10s, constant Hs, mean wave length = 250m, mean crest length = 600m, duration 6 hours: • In time at a fixed location: • N = 2100 • E(maxCrest) = 1.0Hs • In space at a fixed time: • N = 6.6x104 • E(maxCrest) = 1.3Hs • The total storm • N  2x107 • E(maxCrest)=1.69Hs Is a freak wavetruly exceptional, or is it simply being at the wrong place at the wrong time?

  10. What about this one?

  11. or this ...

  12. Small excerpts from simulated surfaces, 3rd order reconstructions JONSWAP, narrow dir. distribution JONSWAP, wider dir. distribution

  13. THE SLEPIAN MODEL REPRESENTATION (Lindgren,1972): The shape of a Gaussian stochastic surface around a point • Optimal predictor (conditional expectation) in the Gaussian case • Optimal second order predictor in general

  14. Around a high maximum:

  15. Simulated surface around a maximum SMR Prediction

  16. ± 2s in the prediction error distribution Actual profiles

  17. Averaged shapes (Case A)

  18. Simulations Slepian Cuts through maxima (averaged) Case A (short crested)

  19. Simulations Slepian Cuts through maxima (averaged) Case B (long crested)

  20. THE SHAPE OF MAXIMA: • The Slepian model prediction is only useful within a rather small neighbourhood about the maximum • Third order reconstructed waves are significantly steeper • along the wave propagation direction than first order waves • Wave crests are in general a little longer than predicted by Gaussian theory, but appear to be bent in various directions, so that averages orthogonal to the wave propagation is similar

  21. THE ”STANDARD MODEL”: • Eulerian based perturbation expansion • Linear Wave Theory is the leading order term

  22. LAGRANGIAN MODELS: A DIFFERENT VIEW Eulerian: Velocity and pressure fields at fixed locations. Lagrangian: Track the motion of each particle as a function of time. Objective of our study: Investigate geometric and kinematic properties of a Lagrangian sea surface model for applications to radar backscatter. Lagrange,1788; Gerstner, 1802; Lamb, 1932; Miche,1942; Pohle (student of Stoker), 1950; Pierson, 1961,1962; Chang,1969; Gjøsund, Arntsen and Moe, 2002, and many more ...

  23. Variables: Mass conservation: (Integrated, 0 vorticity) Momentum conservation: Boundary conditions at the surface,d = 0, p=0 Boundary condition at the bottom: zt = 0 for d = -h

  24. The First Order Regular (Gerstner) Wave sinusoidal (Profile, but not propagation velocity, coincides up to 3rd order with the 3rd order Stokes wave)

  25. First order solution for a short wave riding on a long wave:

  26. FIRST ORDER LAGRANGIAN MODEL: • Resembles Linear Wave Theory • No Stokes drift • The (Lagrangian) spectrum on the usual dispersion surface • THE SECOND ORDER LAGRANGIAN MODEL • Needs integrated momentum equations as a basis • Uses particle velocity as the basic quantity (since • Stokes drift prevents the use of particle position)

  27. REAL WAVES peaked crests - rounded troughs

  28. Excerpt of a simulation of 3D first order Lagrangian waves

  29. First order solutions

  30. Second order solutions

  31. ANIMATION OF 1-D, SECOND ORDER LAGRANGIAN SOLUTION Note slow (Stokes) drift

  32. CONCLUSIONS • Spatial data need spatial tools • Gaussian theory is still a benchmark • case for random waves • Difficult verification of results and claims • Lagrangian model may show new features • also relevant for extreme waves

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