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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.