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

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Cited articles

Bishop, C. M.: Pattern Recognition and Machine Learning, chap. 1, 32–33 pp., edited by: Jordan, M., Kleinberg, J., and Schölkopf, B., Springer International Publishing, ISBN 0387310738, 2006. a
Bosler, P. A., Roesler, E. L., Taylor, M. A., and Mundt, M. R.: Stride Search: a general algorithm for storm detection in high-resolution climate data, Geosci. Model Dev., 9, 1383–1398,, 2016. a, b
Bourdin, S., Fromang, S., Dulac, W., Cattiaux, J., and Chauvin, F.: Intercomparison of four algorithms for detecting tropical cyclones using ERA5, Geosci. Model Dev., 15, 6759–6786,, 2022. a, b
Chan, J. C. L.: Comment on “Changes in tropical cyclone number, duration, and intensity in a warming environment”, Science, 311, 1713,, 2006. a
Ebert-Uphoff, I. and Hilburn, K.: Evaluation, tuning, and interpretation of neural networks for working with images in meteorological applications, B. Am. Meteorol. Soc., 101, E2149–E2170,, 2020. a
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.