Rogue Waves: What risks ?
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Rogue Waves: What risks ?. Michel Olagnon IFREMER Brest, France. Outline. Statistics: What is a rogue wave ? Definition Examples Occurrence probabilities Reliability: What are the associated risks ? Small ships Large ships Offshore platforms

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Michel Olagnon IFREMER Brest, France

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Michel olagnon ifremer brest france

Rogue Waves: What risks ?

Michel Olagnon


Brest, France

Michel olagnon ifremer brest france


  • Statistics: What is a rogue wave ?

    • Definition

    • Examples

    • Occurrence probabilities

  • Reliability: What are the associated risks ?

    • Small ships

    • Large ships

    • Offshore platforms

  • Humanity: How should we deal with those risks ?

    • The offshore industry method

    • The tsunami analogy

    • Perspectives

What is a rogue wave

What is a rogue wave ?

What is a rogue wave1

What is a rogue wave ?

What is a rogue wave2

What is a rogue wave ?

What is a rogue wave3

What is a rogue wave ?

A wave of unexpected

severity given the

prevailing sea conditions at the time it occurs

What is a rogue wave4

What is a rogue wave ?

Some say a wave that is more than twice the significant wave height, but that may not be a reliable definition.

What is a rogue wave5

What is a rogue wave ?

  • If significant wave height was constant and equal to 10 meters, one would encounter a wave of:

    • 18.5 m every 3 hours

    • 21.5 m every day

    • 25.0 m every month

    • 27.5 m every year

    • 30.0 m every 20 years

    • 31.5 m every 100 years

    • 33.1 m every 1000 years

    • 34.8 m every 10000 years

  • Twice the significant wave height is thus by no means abnormal

What is a rogue wave6

What is a rogue wave ?

A good excuse when crew failed to install port hole storm covers ?

What is a rogue wave7

What is a rogue wave ?

A 15° roll of the camera angle ?

What is a rogue wave8

What is a rogue wave ?

Just something that happens ?

What is a rogue wave9

What is a rogue wave ?

The famous “Draupner wave”

Reconstructed water surface elevations over a 1000 m span, from T-30s (blue) to T (red) for the New Year Wave.

Where does it come from

Where does it come from ?

Georg tells us (Lindgren, 1970) that if it comes from the normal gaussian process, it is a wave that looks in retrospect like the autocorrelation function of the water surface elevation signal.

Sverre (Haver, 2000) states that it is a freak wave if it represents an outlier when seen in view of the population of events generated by a piecewise stationary and homogeneous second order model of the sea surface process, otherwise “only” rogue.

Miguel and Al (Onorato & Osborne, 2005) tell us that according to the Schrödinger equation, it sucks energy from its neighbors and thus it is a freak invader from an outer statistical population.

It is nice to be able to recognize a freak or rogue wave in the statistics after it occurred

It is nice to be able to recognize a Freak or Rogue Wave in the statistics after it occurred...

...For various reasons, a much nicer ability would be that of being successful when speculating that approaching waves are not rogue waves, or even that they are.

‘‘   When a woman at a party asks me what I do, I invariably say «I ’m just a speculator.» The encounter ’s over. The only worse conversation stopper is «I ’m just a statistician.»  ’’

Victor Niederhoffer, The Education of a Speculator, Wiley, 1997

Where does research stand with regards to rogue waves recent studies

Where does research stand with regards to rogue waves : recent studies.

A wave is coming.

In order to predict its rogueness, should we use quasi-deterministically the non-linear Schrödinger equation or merely rely on the statistics derived from, for instance, Slepian processes ?

Discriminating questions

Discriminating questions:

1. Do we have more high waves than our conventional long-term statistical models predict ?

2. When we do have high waves, do other characteristics of the whole storm, of the sea state, or of the few previous waves look different from those of other storms, sea states, or sets of a few consecutive waves ?

3. Especially, do characteristics related to theoretical deterministic constructions of rogue waves exhibit statistical evidence of predictive power ?



20 years of data available from Frigg QP platform in the North Sea



1979-1989: mostly 3-hourly measurements, many time-series available.

1991-1999: mostly 20-minute statistics, only reduced parameters



Hmax and H1/3 retrieved preferably from the time-series when available (7%), from the statistics elsewhen.

For storms, missing zero-crossing period information was derived from T1/3 (9.4%) and drawn from the empirical H1/3-Tz distribution when no information at all was available (1.7%).

The final database consists of 265147 statistical records, it is thus equivalent to nearly 9 years of continuous measurements.

Michel olagnon ifremer brest france

EKOFISK, operated by ConocoPhillips

Laser measurements at the time of the ”Varg incident”


North Sea

Storm freakiness

Storm “freakiness”

We (Olagnon & Prevosto, 2005, Olagnon & Magnusson, 2004) tried to investigate the widest time-scale: the whole storm.

Especially, the maximum wave expected in a storm is a more useful forecast to seafarers than the maximum wave in some particular 1- or 3-hour duration sea state of that storm.

It may thus appear natural to relate the maximum wave in a storm to the maximum predicted H1/3 in that whole storm rather than to the prevailing H1/3 at the precise instant of Hmax.

Storm freakiness1

Storm “freakiness”

Storms are defined as durations > 12 hours with H1/3> 5m

Storm freakiness2

Storm “freakiness”

For each of the 187 identified storms, 1000 random simulations were made using the database statistical parameters and a Jonswap wave spectrum with gamma=3. Second order correction was then applied to all computed Hmax values.

Freakiness of a storm is defined as the quantile rank of that storm’s observed Hmax/ H1/3max in the corresponding distribution over the 187 actual storms (empirical) and over the 187000 simulated storms (2nd order theory).

Storm freakiness3

Storm “freakiness”

QQ-plot of Hmax/ H1/3max = blue dots.

H1/3 = green dots

Hmax = red dots

Apart from a very few ones, storms are less “freaky” than 2nd order theory would predict.

Storm freakiness4

Storm “freakiness”

QQ-plot of Hmax/ H1/3max = blue dots.

Mean storm BFI = red dots

Benjamin-Feir instability at the time-scale of a storm can only be very weakly related to its “freakiness”.

Storm freakiness5

Storm “freakiness”

Expectations based on experience rather than theory would be definitely too low: An explanation for so many freak waves reported ?

Medium term the sea state time scale freaky sea states

Medium term: the sea state time scaleFreaky sea states ?

Nerzic & Prevosto (98) proposed a Weibull-Stokes model for the distribution of maximum waves Hmax in a sea state, conditional to H1/3 and Tz of the sea state.

They used a 7% subset of the Frigg database, without any special emphasis on extremes, to derive their model.

We use the full database to study how the model performs with long-term extremes.

Distribution of maximum wave heights

Distribution of maximum wave heights

No underestimation by model !

Again, an appropriate transformation, limited to taking into account standard non-linearities up to second order, is sufficient to explain the observed extremes

Comparison of empirical distribution of Hmax with Nerzic & Prevosto model for H1/3>5 m.

Kurtosis and benjamin feir instability

Kurtosis and Benjamin-Feir instability

“When a similarity connection is achieved between two objects to 20 decimal places, the greater will move to the lesser”

A.E. Van Vogt, The World of Null-A, 1945

Even though conventional Hmax models seem acceptable for long-term distributions, it might be possible to predict when the extremes in the distribution are most likely to occur : at those times, the similarity between the actual world and the theoretical deterministic world of non-linear Schrödinger equation may be such that we can apply the rules of the latter for some limited time-space window. In that latter world, extremes are governed by Benjamin-Feir instability.

Kurtosis and benjamin feir instability1

Kurtosis and Benjamin-Feir instability

Benjamin-Feir instability, i.e. the ratio of steepness to bandwidth, and signal kurtosis are strongly related (Mori & Janssen 2005)...

… but are kurtosis (BFI) excursions away from regular values the cause of freak waves, or a mere consequence of their observation ?

In other words, is kurtosis (BFI) a predictor or only a detector ?

Kurtosis and hmax

Kurtosis and Hmax

Hmax/ H1/3exhibits

a clear relationship to kurtosis...

Kurtosis and hmax1

Kurtosis and Hmax

…but if “kurtosis” is computed with removal of the largest wave’s time-duration, the relationship can no longer be seen.

What value in met offices warnings

Mostly based on Benjamin-Feir instability, and we just saw not conclusive.

What value in Met’Offices warnings ?

Difficult to assess how good the chosen omens are.

Difficult to find volunteers to go into the worst areas of storms and validate the forecasts...

What is there to be seen a few waves ahead

What is there to be seen a few waves ahead ?

Instantaneous Benjamin-Feir instability index: nothing.



BFI Index

What is there to be seen a few waves ahead1

What is there to be seen a few waves ahead ?

Irregularity factor

( # of crests / # up zero crossings ): nothing.



Irr. Fact.

What is there to be seen a few waves ahead2

What is there to be seen a few waves ahead ?

Steepness: let’s have a closer look.




What is there to be seen a few waves ahead3

What is there to be seen a few waves ahead ?








Conclusions so far

Conclusions so far

  • Extreme waves are not found more frequently than conventional long-term distribution models predict.

  • When extremes are observed, no abnormal characteristic can be found in non-directional parameters at the time scale of the whole storm, of the sea state or of a set of a few consecutive waves. There is nothing more in rogue waves than what we can see in the statistics.

Associated risks

A small ship usually climbs up the wave...

Associated risks

Risks for small ships

…but may get rolled over or caught from the back.

Risks for small ships

Risks for small ships1

Flooding of the bridge or control room

Risks for small ships

Risks for larger ships

Get green water in addition to white on the foredeck...

Risks for larger ships

Risk for larger ships

…and water weighs a lot !

“for a while until they got it squared away, we launched them sailing backwards…”

Risk for larger ships

Risks for larger ships1

Breaking of the structure due to sagging or hogging, in the trough or on the crest.

Risks for larger ships

Risky areas where not to sail

Risky areas where not to sail ?

Risky areas

Only areas where there are more ships at risk...

Risky areas ?

The wrong place at the wrong time

Except if you are named Hosukai, of course...

The wrong place at the wrong time

Offshore platforms

Cannot avoid bad weather areas.

The deck has to

be high enough

to let the waves

pass by in the



Offshore platforms

How the offshore industry deals with the risk

Reliability targets of 10-4 yearly.

On one hand, 10000 years from

now, the North Sea may well be a

desert, on the other hand, risks

associated with waves are at

least one order of magnitude lower

than those of blast, fire, human

errors, etc.

The idea is to keepthe metocean

risk at that relative level.

How the offshore industry deals with the risk.

How the offshore industry deals with the risk1

Design methods were questioned

for a while, because of the possibility

of some phenomenon different from

the ones that had been used to derive

the theories that led to design values.

Experience and studies have shown

that there was no problem with those

theories onto the 10-4 limit.

To some extent, the shipping industry

uses a similar approach, but less openly.

To the shipowner, the risk of a rogue

wave is an acceptable one, as we would

say for the risk of a car accident when

driving to work.

How the offshore industry deals with the risk.

The tsunami analogy

When you go to Hawaii, there is no sign,

to be seen on the real estate near the

beaches, that they could be washed

away by a tsunami at any moment.

Yet, if a tsunami occurs in Hawaii,

there will be loss of property, but

likely no loss of lives: those subject

to the risk are properly trained, know

the ominous tokens and what to do


Rogue waves can be considered in

the same fashion: they may happen,

one should just train not to be caught

unprepared in that case.

The tsunami analogy

The 3 rules for survival training training and training

What should you watch for ?

Complex, multiple low pressure meteorological systems

Pressure lows traveling at the same speed as the waves they create (“running fetch”)

A sea state easier to handle than could have been expected from the wind’s strength

The time when the storm’s maximum is close ahead

The time when a cold front is close ahead

The 3 Rules for survival: Training, training and training


Design:Rogue waves understanding is now far from being a priority, but they do occur (as statistically expected), and should not be neglected.

Forecast (the priority):No automatic rules, but … it may not be impossible to train super-expert meteorologists to estimate the risks with good chances of success.NOT A MET’OFFICE ACCEPTED PRACTICE HOWEVER !


Freak events do happen

Freak events do happen

The death of Aeschylus was not of his own will; […]. Having come out of the place where he lived in Sicily, he sat under the sun. An eagle carrying a tortoise happened to fly above him. Mistaken by the whiteness of his bald head, it let the tortoise fall on to it, as it would have done to a stone, in order to break it and eat its flesh. The blow took his life away from the poet who first gave the most perfect form to tragedy.

Valerius Maximus, Factorum ac dictorum memorabilium, IX 12, ca. 30 AD


Thank you.

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