AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version
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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.
arXiv:2609.24882v1 Announce Type: new Abstract: Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from hi...
arXiv:2610.33771v1 Announce Type: new Abstract: Earth-system models simplify atmospheric chemistry into condensed mechanisms, because near-explicit mechanisms such as the Master Chemical Mechanism (M...
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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.