Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Forecasting the Evolution of North Atlantic Hurricanes: A Deep Learning Approach



Rikhi Bose, Adam L. Pintar, Emil Simiu


Accurate prediction of storm evolution from genesis onwards may be of great importance considering that billions of dollars worth of property damage and numerous casualties are inflicted each year all over the globe. In the present work, two classes of Recurrent Neural Network (RNN) models for predicting storm-eye trajectory have been developed. These models are trained on input features available in or derived from the North Atlantic hurricane database maintained by the National Hurricane Center (NHC). The models utilize historical data to estimate probabilities of storms passing through any location. Furthermore, inputs to the models are such so that the model development methodology is applicable to any oceanic basin in any part of the globe. Model forecasting errors have been analyzed in detail. The error analysis shows that the Many-To-Many class of models are appropriate for long-term forecasting while Many-To-One prediction models perform comparably well for 6 − hr predictions. Application of these models to predicting more than 40 test storms in the North Atlantic basin shows that, at least for forecasts of up to 12 hours, they outperform all data-based storm trajectory prediction models cited in the paper. Apart from providing very fast predictions, the RNN models require less information than models used in current practice. Because the models presented herein emulate the trajectory trends of historical storms, they are currently being used for the simulation of synthetic storm tracks and features and the subsequent estimation of extreme wind speeds with various mean recurrence intervals.
Technical Note (NIST TN) - 2167
Report Number


Hurricane forecasting, HURDAT2, Long Short-Term Memory (LSTM), North Atlantic hurricanes, Recurrent Neural Networks (RNN), Time series forecasting.


Bose, R. , Pintar, A. and Simiu, E. (2021), Forecasting the Evolution of North Atlantic Hurricanes: A Deep Learning Approach, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online],, (Accessed July 21, 2024)


If you have any questions about this publication or are having problems accessing it, please contact

Created June 30, 2021, Updated November 29, 2022