Rogue Wave Prediction Tool Developed

Rogue waves can measure eight times higher than the surrounding seas and can strike in otherwise relatively calm waters, with virtually no warning. Now a prediction tool developed by MIT engineers may give sailors a two to three minute warning of an incoming rogue wave, hopefully providing them with enough time to shut down essential operations on a ship or offshore platform.

The tool, in the form of an algorithm, sifts through data from surrounding waves to spot clusters of waves that may develop into a rogue wave. Depending on a wave group’s length and height, the algorithm computes a probability that the group will turn into a rogue wave within the next few minutes.

“It’s precise in the sense that it’s telling us very accurately the location and the time that this rare event will happen,” says Themis Sapsis, the American Bureau of Shipping Career Development Assistant Professor of Mechanical Engineering at MIT. “We have a range of possibilities, and we can say that this will be a dangerous wave, and you’d better do something. That’s really all you need.”

Like many complex systems, the open ocean can be represented as a chaotic mix of constantly changing data points. To understand and predict rare events such as rogue waves, scientists have typically taken a leave-no-wave-behind approach, in which they try to simulate every individual wave in a given body of water, to give a high-resolution picture of the sea state, as well as any suspicious, rogue-like activity. This extremely detailed approach is also computationally expensive, as it requires a cluster of computers to solve equations for each and every wave, and their interactions with surrounding waves.

“It’s accurate, but it’s extremely slow — you cannot run these computations on your laptop,” Sapsis says. “There’s no way to predict rogue waves practically. That’s the gap we’re trying to address.”

Sapsis and his team devised a much simpler, faster way to predict rogue waves, given data on the surrounding wave field.

In previous work, the team identified one mechanism by which rogue waves form in unidirectional wave fields. They observed that, while the open ocean consists of many waves, most of which move independently of each other, some waves cluster together in a single wave group, rolling through the ocean together. Certain wave groups, they found, end up “focusing” or exchanging energy in a way that eventually leads to an extreme rogue wave.

“These waves really talk to each other,” Sapsis says. “They interact and exchange energy. It’s not just bad luck. It’s the dynamics that create this phenomenon.”

In their current work, the researchers sought to identify precursors, or patterns in those wave groups that ultimately end up as rogue waves. To do this, they combined ocean wave data available from measurements taken by ocean buoys, with nonlinear analysis of the underlying water wave equations.

Sapsis used the statistical data to quantify the range of wave possibilities, for a given body of water. They then developed a novel approach to analyze the nonlinear dynamics of the system and predict which wave groups will evolve into extreme rogue waves.

His team was able to predict which groups turned rogue, based on two parameters: a wave group’s length and height. The combination of statistics and dynamics helped the team identify the length-scale of a critical wave group, which has the highest likelihood of evolving into a rogue wave. Using this, the team derived a simple algorithm to predict a rogue wave based on incoming data. By tracking the energy of the surrounding wave field over this length-scale, they could immediately calculate the probability of a rogue wave developing.

Sapsis says the team’s algorithm is able to predict rogue waves several minutes before they fully develop. To put the algorithm into practice, he says ships and offshore platforms will have to utilize high-resolution scanning technologies such as LIDAR and radar to measure the surrounding waves.

This research was supported in part by the Office of Naval Research, the Army Research Office and the American Bureau of Shipping. Sapsis and former postdoc Will Cousins have published their results in the Journal of Fluid Mechanics.