Freak waves North Sea more elusive than thought

Freak waves in the North Sea are proving more difficult to fathom than thought. German researchers discovered that these giant waves appear more frequently and unexpectedly than previously believed. This also has implications for the Wadden Sea.

The research, part of the Freak Waves II project, focuses on getting to the bottom of these extreme natural phenomena and developing better predictions. The study shows that monster waves occur not only under extreme weather conditions, but also in relatively calm weather. This challenges existing prediction models and makes the risks to shipping even less predictable.
Freak waves are surprisingly high compared to surrounding waves. They are defined as waves with a height more than double that of the waves around them. They are unexpected and can occur with enormous force.

New warning models

For ships, offshore wind farms and research platforms, these unexpected waves pose a serious danger. A single monster wave can cause severe damage or even capsize a ship. Better warning systems are crucial to protect crews and installations. Within Freak Waves II, scientists are working on new models that combine meteorological data, wave measurements and machine learning. The aim is to recognise patterns faster and issue warnings earlier.
Researchers at Helmholtz-Zentrum Hereon and BSH will soon test the new prediction models in practice. At the same time, they are working on better warning systems to reduce the impact of these natural forces.

Wadden region vulnerable

The findings are particularly relevant for the Wadden region, a region already known for its treacherous currents and rapidly changing weather conditions. That monster waves are more common here than thought previously, underlines the need for vigilance and better monitoring.
The study in the German Bight shows that strong tidal currents increase the likelihood of extreme waves. During storm Britta in 2006, a 16-metre wave was recorded, resulting in damage to the research platform Fino1.

Thesis uses AI

Back in 2022, Dion Häfner, a researcher at the Niels Bohr Institute in Copenhagen, was awarded a PhD for a thesis on monster waves. Using artificial intelligence (AI), he analysed data from more than a billion waves measured by 158 different buoys around the US coast. All measured wave periods added together represent 700 years of data. Applying AI, Häfner concludes that the most dominant factor in the materialisation of these bizarre waves is the so-called “linear superposition”. This phenomenon occurs when two wave systems intersect and reinforce each other for a short time.
“When two wave systems meet at sea in a way that increases the likelihood of generating high crests followed by deep troughs, the risk of extremely large waves arises,” he says. This is knowledge that has existed for 300 years and which we are now backing up with data,” says Dion Häfner.

Safer shipping

The researchers’ algorithm is good news for the shipping industry, which has some 50,000 cargo ships sailing around the world at any given time. Hopefully, using the algorithm, it will be possible to predict when this “perfect” combination of factors is present to increase the risk of a monster wave that could endanger everyone at sea.
“As shipping companies plan their routes well in advance, they can use our algorithm to get a risk assessment of whether there is a chance of encountering dangerous monster waves along the way. Based on that, they can choose alternative routes,” says Dion Häfner.
Both the algorithm and the study are public, as are the weather and wave data used by the researchers. Therefore, according to Dion Häfner, interested parties, such as government agencies and weather services, can easily start calculating the probability of unreliable waves. And unlike many other models created using artificial intelligence, all intermediate calculations in the researchers’ algorithm are transparent.

Sources: an article by René Quist in the Schuttevaer (subscribers only) and an article in SciTechDaily.
Image: PXhere.

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