Atlantic Hurricane Activity Prediction: A Machine Learning Approach
In one sentence. CNN feature fusion of sea-level pressure, SST and wind predicts preseason Atlantic hurricane activity with skill up to 18 months ahead.
Abstract
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.
Labels
- Topic
- Extremes & hazards, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- CNN / U-Net
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3390/atmos12040455 OpenAlex:W3152101540
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.