# Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

arXiv: https://arxiv.org/abs/1912.01752 · HTML: https://mlsanity.com/paper/1912.01752/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 30 of 44)

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- [Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks](https://mlsanity.com/paper/2105.06386/) (2021)
- [Combining data assimilation and machine learning to estimate parameters of a convective-scale model](https://mlsanity.com/paper/2109.02953/) (2021)
- [Differentiable modeling to unify machine learning and physical models and advance Geosciences](https://mlsanity.com/paper/2301.04027/) (2023)
- [Interpretable Machine Learning for Weather and Climate Prediction: A Survey](https://mlsanity.com/paper/2403.18864/) (2024)
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- [A Rigorous Study Of The Deep Taylor Decomposition](https://mlsanity.com/paper/2211.08425/) (2022)
- [Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI](https://mlsanity.com/paper/2310.09091/) (2023)
- [Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations](https://mlsanity.com/paper/2310.16015/) (2023)
- [Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences](https://mlsanity.com/paper/2410.19856/) (2024)
- [Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability](https://mlsanity.com/paper/2210.09958/) (2022)
- [Online Test of a Neural Network Deep Convection Parameterization in ARP-GEM1](https://mlsanity.com/paper/2410.21920/) (2024)
- [Interpreting Neural Policies with Disentangled Tree Representations](https://mlsanity.com/paper/2210.06650/) (2022)
- [Towards Physically Consistent Deep Learning For Climate Model Parameterizations](https://mlsanity.com/paper/2406.03920/) (2024)
- [Applications of Explainable artificial intelligence in Earth system science](https://mlsanity.com/paper/2406.11882/) (2024)

## Abstract (arXiv)

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 learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backwards optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research.