A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields

Modeling typhoon-induced storm surges requires 10-m wind and sea level pressure fields as forcings, commonly obtained using parametric models or a fully dynamical simulation by numerical weather prediction (NWP) models. The parametric models are generally less accurate than the full-physics models of the NWP, but they are often preferred owing to their computational efficiency facilitating rapid uncertainty quantification. Here, we propose using a deep learning method based on generative adversarial networks (GAN) to translate the parametric model outputs into a more realistic atmospheric forcings structure resembling the NWP model results. Additionally, we introduce lead-lag parameters to incorporate a forecasting feature in our model. Thirty-four historical typhoon events from 1981 to 2012 are selected to train the GAN, followed by storm surge simulations for the four most recent events. The proposed method efficiently transforms the parametric model into realistic forcing fields by a standard desktop computer within a few seconds. The results show that the storm surge model accuracy with forcings generated by GAN is comparable to that of the NWP model and outperforms the parametric model. Our novel GAN model offers an alternative for rapid storm forecasting and can potentially combine varied data, such as those from satellite images, to improve the forecasts further.

Automated summary

In this study, a deep learning method based on generative adversarial networks (GAN) was used to translate parametric model outputs into realistic atmospheric forcing fields resembling numerical weather prediction (NWP) model results. Lead-lag parameters were also introduced to incorporate a forecasting feature in the model. The results showed that the storm surge model accuracy with forcings generated by GAN was comparable to that of the NWP model and outperformed the parametric model. This novel GAN model offers an alternative for rapid storm forecasting and has the potential to improve forecasts further by combining diverse data sources.