Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7051-2022
https://doi.org/10.5194/gmd-15-7051-2022
Development and technical paper
 | 
16 Sep 2022
Development and technical paper |  | 16 Sep 2022

Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset

Sébastien Gardoll and Olivier Boucher

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Latest update: 13 Jul 2024
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Short summary
Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs (ERA5 and MERRA-2 labeled by HURDAT2) according to the presence or absence of TCs. We tested the impact of interpolation and of "mixing and matching" the training and test sets on the performance of the CNN.