  Landfall of a tropical cyclone is the event when it moves over the land after
crossing the coast of the ocean. It is important to know the characteristics of
the landfall in terms of location and time, well advance in time to take
preventive measures timely. In this article, we develop a deep learning model
based on the combination of a Convolutional Neural network and a Long
Short-Term memory network to predict the landfall's location and time of a
tropical cyclone in six ocean basins of the world with high accuracy. We have
used high-resolution spacial reanalysis data, ERA5, maintained by European
Center for Medium-Range Weather Forecasting (ECMWF). The model takes any 9
hours, 15 hours, or 21 hours of data, during the progress of a tropical cyclone
and predicts its landfall's location in terms of latitude and longitude and
time in hours. For 21 hours of data, we achieve mean absolute error for
landfall's location prediction in the range of 66.18 - 158.92 kilometers and
for landfall's time prediction in the range of 4.71 - 8.20 hours across all six
ocean basins. The model can be trained in just 30 to 45 minutes (based on ocean
basin) and can predict the landfall's location and time in a few seconds, which
makes it suitable for real time prediction.
