  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 24h 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 3000 storms since 1979, sampled at a
6 hour 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.
