71 citations · 83 across the 2 of their papers we have counts for
4 papers
Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks
Elizabeth A. Barnes, Kirsten Mayer, Benjamin Toms +2
The atmosphere is chaotic. This fundamental property of the climate system makes forecasting weather incredibly challenging: it's impossible to expect weather models to ever provid…
Indicator patterns of forced change learned by an artificial neural network
Elizabeth A. Barnes, Benjamin Toms, James W. Hurrell +3
Many problems in climate science require the identification of signals obscured by both the "noise" of internal climate variability and differences across models. Following previou…
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
Benjamin A. Toms, Elizabeth A. Barnes, Imme Ebert-Uphoff
Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks lea…
Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation
Benjamin A. Toms, Karthik Kashinath, Prabhat +1
We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying th…