arXiv AI By Kien Le Trung, Tung Kieu, Dinh Duc Nha Nguyen, Cuong Do, Bin Yang, Phi Long Nguyen

AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version

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arXiv Machine Learning
Jul 24

Toward Mechanistic Interpretability of an AI Foundation Model Fine-Tuned for Atmospheric Chemistry

arXiv:2607. 20778v1 Announce Type: new Abstract: Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models.

By Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm, Makoto M. Kelp
arXiv Machine Learning
Sep 21

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...

By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard
arXiv Machine Learning
Oct 8

Artificial intelligence pathways from weather to climate

arXiv:2610.09770v1 Announce Type: cross Abstract: Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models acro...

By Tom Beucler, J. David Neelin, Hui Su, Shivanshi Asthana, Chris Bretherton, Will Chapman, Costa Christopoulos, Spencer K. Clark, Aditya Grover, Ignacio Lopez-Gomez, Tapio Schneider, Adam Subel, Oliver Watt-Meyer
arXiv Statistics ML
Oct 26

Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System

Hybrid forecasting systems that combine machine learning with numerical weather prediction are explored using a neural‑network representation of model error within the Integrated Forecasting System. The study introduces a targeted neural‑network 4D‑Var approach that estimates only network parameters and a batched extension that processes assimilation windows in random order, averaging parameter updates across multiple windows. Experiments show that while sequential online learning improves forecasts during training, it fails to generalise, whereas the batched formulation consistently enhances out‑of‑sample forecast skill and improves convergence with larger batch sizes.

By Alban Farchi, Marcin Chrust, Massimo Bonavita, Marc Bocquet, Wei Pan, Patrick Laloyaux