Coupled AI and physics model improves typhoon-wave height forecasting
Typhoons pose significant threats, with sudden, dangerous waves that endanger ships, offshore platforms and coastal infrastructure in the northwest Pacific Ocean. Current typhoon forecasting methods sometimes underestimate the largest waves during typhoons, but researchers in China have designed a new model that may offer improvements. Their new study, published in Ocean Engineering, describes how the model combines physics guidance with data-driven learning to improve typhoon forecasting.
Predicting typhoon wave dynamics
Significant wave height (SWH) is used as a standard measure of sea-state severity and is central to marine warnings, but this parameter is often underestimated in the commonly used Simulating WAves Nearshore (SWAN) model. This leads to compounding errors that throw off typhoon forecasts. The underestimation partially results from the model's dependence on wind data, which can be challenging to collect during the extreme conditions induced by typhoons. Furthermore, standard corrections can be difficult when waves grow and fade quickly.
Researchers have attempted to improve methods but have faced challenges. The authors of the new study write, "To reduce wave-model biases under tropical cyclones, a range of post-processing and correction strategies have been explored. Data assimilation can improve wave state estimates when observations are available, but it is computationally expensive and its effectiveness is often limited during extreme events by data sparsity and the strong sensitivity of wave dynamics to forcing uncertainties. Statistical interpolation or empirical correction methods are relatively lightweight, yet they may struggle to represent the nonlinear and rapidly evolving error structure of typhoon-driven waves."
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