Abstract
The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24 h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3,000 storms since 1979, sampled at a 6 h frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts.
Highlights
Cyclones, hurricanes and typhoons are words designating the same phenomena: a rare and complex event characterized by strong winds surrounding a low pressure area
By incorporating large spatial atmospheric data in a statistical model, using state-of-the-art machine learning methods, we can improve the accuracy while reducing the calculation time
We propose to answer the following questions: Can we develop a statistical forecast model, using state-of-the-art deep learning techniques, that is able to compete with current forecast models at a 24h time lag? How can we take the best advantage of the worldwide historical track database and the reanalysis meteorological fields?
Summary
Hurricanes and typhoons are words designating the same phenomena: a rare and complex event characterized by strong winds surrounding a low pressure area. Dynamical models solve the physical equations governing motions in the atmosphere and they are influenced by physical models -convective schemes (such as Kain-Fritsch or Simplified Arakawa Schubert), cloud microphysics, land surface model, ocean model, sea/land ice model, planetary boundary layer scheme, surface layer scheme, longwave and shortwave radiation schemes, subgrid-scale diffusion- and by their data assimilation methods (such as 4D-VAR). They are computationally demanding and in current practice older model runs are adjusted in order to be considered early methods, i.e. available in real time. By incorporating large spatial atmospheric data in a statistical model, using state-of-the-art machine learning methods, we can improve the accuracy while reducing the calculation time
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
