This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A research team led by Professor Li Xiaofeng from the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) has developed a model to forecast internal solitary waves originating from multiple sources in the Sulu Sea. The model combines physical equations and satellite observations to predict wave arrival times and crest positions across this complex marine environment.
The study was published in IEEE Transactions on Geoscience and Remote Sensing. Internal solitary waves travel along interfaces between ocean layers of different densities. They can transport energy over long distances, generate strong currents and enhance ocean mixing.
Predicting when and where these waves will arrive is important for understanding ocean dynamics and supporting offshore engineering and navigation. Forecasting these waves in the Sulu Sea is challenging because they originate in several regions and follow complex paths shaped by islands, straits and seafloor topography. Forecasts based solely on satellite observations may lack continuity and physical consistency.
Meanwhile, traditional empirical models built around a single generation source cannot adequately represent waves originating from multiple regions. To address these challenges, the researchers combined the eikonal equation with multiyear satellite imagery and tidal data to identify potential wave generation regions. They then refined a source configuration comprising five point sources and one line source in the southwestern and northeastern Sulu Sea.
This configuration better represents the observed propagation characteristics of internal solitary waves. The forecasting model integrates two complementary components. One component is a physical equation that describes the overall wave propagation pattern.
The other component is satellite observations that provide local corrections to account for differences between modeled and observed waves. Together, these components enable the model to predict wave arrival times and crest positions without the need for previously observed wave crests as input. In the independent test set, the full model reduced the propagation-time root-mean-square error by 70.4%, from 4.66 to 1.38 hours, compared with the single-source model.
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