• Physics 18, s114
Researchers have developed an improved technique for making wave-height predictions that mitigate gaps in data coverage and encompass rare, dangerously high waves.
Landing an aircraft on a floating platform, transferring cargo between ships, and other maritime operations require forecasting wave motion at least a few minutes into the future. The task is doubly challenging: First, in rough conditions, the waves themselves obscure the seascape behind them, frustrating the measurements that inform the forecasts. Second, because wave behavior can be nonlinear, errors in the dataset compound quickly. Xinshu Zhang of Shanghai Jiao Tong University and his colleagues have developed an improved method that tackles both problems [1]. Their computational technique uses radar data that ships routinely collect. Deploying it will require no additional hardware, they say.
Shipborne radar systems are mounted high up for the best possible view, but they can never obtain comprehensive coverage. A typical system has a central blind spot extending tens or hundreds of meters from the ship, and what it does capture can be spotty because of the roughness of the sea. Zhang and colleagues filled these gaps using an averaging algorithm, which continually interpolates data from three sequential radar images. They fed this more complete dataset into a predictive model that accounts for nonlinearities up to the third order in the interactions between waves. When waves are high and steep, such effects can produce occasional “rogue” waves whose amplitudes are much greater than would be the case otherwise.
The team tested the technique on simulated wave fields and achieved wave-height errors of less than 10%. Errors were twice as large when they considered only second-order nonlinearity and 3 times as large when wave interference was taken to be linear—as is the case in most current models. Next, Zhang and colleagues plan to test their method at sea.
–Marric Stephens
Marric Stephens is a Corresponding Editor for Physics Magazine based in Bristol, UK.
References
- J. Yao et al., “Nonlinear wave reconstruction and prediction by a shipborne radar with a dynamic averaging algorithm,” Phys. Rev. Fluids 10, 094801 (2025).
