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Mechanistic Interpretability of Atmospheric Rivers in GraphCast
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arXiv AI
Oct 7
Mechanistic Interpretability of Atmospheric Rivers in GraphCast
arXiv:2610.07583v1 Announce Type: cross Abstract: While AI weather models now rival operational forecasts, how they represent the atmosphere internally remains an open question: feature attribution r...
arXiv Machine Learning
Aug 21
SAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth Events
arXiv:2608. 20117v1 Announce Type: new Abstract: The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning.
arXiv Machine Learning
6d ago
GNN-VIEW: Visual Inspection and Explanation of Graph-based Earth-System Models
GNN‑VIEW is an interactive, browser‑based tool designed to aid the development and interpretation of graph‑based Earth‑system models. It visualizes the graph’s information pathways to help developers debug the structure before training, and after training it applies attribution‑based explainability to show which input features influence specific forecasts. The authors demonstrate its utility in building an ocean forecasting graph and in diagnosing poor precipitation predictions in a weather model during Hurricane Laura.
arXiv Machine Learning
Oct 22
Tropical Cyclone Attributes in Sparse Autoencoder Features of an AI Weather Model
The paper investigates how tropical cyclone attributes are represented within the sparse autoencoder (SAE) features of the AI weather model GraphCast. By analyzing 2,115 six‑hourly timesteps from 205 cyclones, the authors map SAE activations to cyclone position, intensity, and pressure structure across multiple layers and decompositions, finding that each attribute is encoded in multiple features and that deeper layers better predict future position and pressure structure. The study demonstrates that the organization of physical‑attribute relations in SAEs is reproducible and can aid in interpreting cyclone‑related information in GraphCast.
arXiv AI
Jul 28
Context-Aware Concept Distillation for Trustworthy Flood Prediction
arXiv:2607. 23237v1 Announce Type: cross Abstract: Effective flood risk management relies on accurate forecasting, yet the "black box" nature of stateof-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions.
arXiv Machine Learning
Aug 26
Topology enables learning-based hydrodynamic prediction of the global river system
arXiv:2602.22293v2 Announce Type: replace Abstract: Accurate river prediction is essential for water, food and energy security, yet remains challenging across entire river networks. Machine learning...