Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 18
Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7051-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7051-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 18
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

Viewed

Total article views: 1,316 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
987 307 22 1,316 12 7
  • HTML: 987
  • PDF: 307
  • XML: 22
  • Total: 1,316
  • BibTeX: 12
  • EndNote: 7
Views and downloads (calculated since 19 Apr 2022)
Cumulative views and downloads (calculated since 19 Apr 2022)

Viewed (geographical distribution)

Total article views: 1,316 (including HTML, PDF, and XML) Thereof 1,239 with geography defined and 77 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Nov 2023
Download
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.
Read more