2021 journal article

Atlantic Hurricane Activity Prediction: A Machine Learning Approach

ATMOSPHERE, 12(4).

By: T. Asthana n, H. Krim n, X. Sun n‚ÄČ, S. Roheda n & L. Xie n

author keywords: hurricanes; tropical cyclones; fusion networks; weather forecast
TL;DR: A machine learning model capable of making good preseason-prediction of Atlantic hurricane activity is presented, which has the potential to make skillful predictions up to 18 months in advance. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
14. Life Below Water (OpenAlex)
Source: Web Of Science
Added: May 10, 2021

Long-term hurricane predictions have been of acute interest in order to protect the community from the loss of lives, and environmental damage. Such predictions help by providing an early warning guidance for any proper precaution and planning. In this paper, we present a machine learning model capable of making good preseason-prediction of Atlantic hurricane activity. The development of this model entails a judicious and non-linear fusion of various data modalities such as sea-level pressure (SLP), sea surface temperature (SST), and wind. A Convolutional Neural Network (CNN) was utilized as a feature extractor for each data modality. This is followed by a feature level fusion to achieve a proper inference. This highly non-linear model was further shown to have the potential to make skillful predictions up to 18 months in advance.