# Predicting Landfall's Location and Time of a Tropical Cyclone Using Reanalysis Data

arXiv: https://arxiv.org/abs/2103.16108 · HTML: https://mlsanity.com/paper/2103.16108/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge](https://mlsanity.com/paper/1711.07970/) (2017)
- [Predicting Hurricane Trajectories using a Recurrent Neural Network](https://mlsanity.com/paper/1802.02548/) (2018)
- [Hurricane Forecasting: A Novel Multimodal Machine Learning Framework](https://mlsanity.com/paper/2011.06125/) (2020)
- [Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data](https://mlsanity.com/paper/1910.10566/) (2019)

## Built on by (most cited first, 2 of 2)

- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Forecasting formation of a Tropical Cyclone Using Reanalysis Data](https://mlsanity.com/paper/2212.06149/) (2022)

## Abstract (arXiv)

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.