Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4495-2021
https://doi.org/10.5194/gmd-14-4495-2021
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, Karthik Kashinath, Prabhat, and Da Yang

Related authors

Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022,https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021,https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1
Travis A. O'Brien, Mark D. Risser, Burlen Loring, Abdelrahman A. Elbashandy, Harinarayan Krishnan, Jeffrey Johnson, Christina M. Patricola-DiRosario, John P. O'Brien, Ankur Mahesh, Prabhat, Sarahí Arriaga Ramirez, Alan M. Rhoades, Alexander Charn, Héctor Inda Díaz, and William D. Collins
Geosci. Model Dev., 13, 6131–6148, https://doi.org/10.5194/gmd-13-6131-2020,https://doi.org/10.5194/gmd-13-6131-2020, 2020
Short summary

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, https://doi.org/10.1371/journal.pone.0130140, 2015. a
Download
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.
Share