Articles | Volume 14, issue 7
Geosci. Model Dev., 14, 4495–4508, 2021
Geosci. Model Dev., 14, 4495–4508, 2021

Methods for assessment of models 22 Jul 2021

Methods for assessment of models | 22 Jul 2021

Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation

Benjamin A. Toms et al.

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Cited articles

Abhik, S. and Hendon, H. H.: Influence of the QBO on the MJO during coupled model multiweek forecasts, Geophys. Res. Lett., 46, 9213–9221, 2019. a
Adames, Á. F. and Kim, D.: The MJO as a dispersive, convectively coupled moisture wave: Theory and observations, J. Atmos. Sci., 73, 913–941, 2016. a
Adames, Á. F. and Wallace, J. M.: Three-dimensional structure and evolution of the MJO and its relation to the mean flow, J. Atmos. Sci., 71, 2007–2026, 2014. a
Adames, Á. F. and Wallace, J. M.: Three-dimensional structure and evolution of the moisture field in the MJO, J. Atmos. Sci., 72, 3733–3754, 2015. a
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PloS one, 10, e0130140,, 2015. a
Short summary
We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.