Atlantic Hurricane Activity Prediction: A Machine Learning Approach

Atmosphere

Hamid Krim¹  Tanmay Asthana¹  Siddharth Roheda¹  Lian Xie²  Xia Sun² 

[1] Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-8208, USA;Department of Marine, Earth, and Atmospheric Sciences North Carolina State University, Raleigh, NC 27695-8208, USA;

关键词: hurricanes;    tropical cyclones;    fusion networks;    weather forecast;   

DOI  :  10.3390/atmos12040455

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown