Mechanistic Interpretability of Atmospheric Rivers in GraphCast
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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While AI weather models now rival operational forecasts, how they represent the atmosphere internally remains an open question: feature attribution reveals which input patterns matter, not what the mo...
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.
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.
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.
The paper introduces GraphRiverCast (GRC), a topology-informed foundation model that predicts daily multivariate hydrodynamics across the global river network using sparse gauge data. By leveraging river connectivity and energetics, GRC outperforms both physics-based and traditional machine-learning models, maintaining skill over long lead times and generalizing to ungauged reaches and finer-resolution networks. The approach demonstrates how incorporating system structure can improve predictions in observation-limited Earth systems.
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...