Online model error correction with neural networks: application to the Integrated Forecasting System

arXiv:2403.03702v1 Announce Type: cross
Abstract: In recent years, there has been significant progress in the development of fully data-driven global numerical weather prediction models. These machine learning weather prediction models have their strength, notably accuracy and low computational requirements, but also their weakness: they struggle to represent fundamental dynamical balances, and they are far from being suitable for data assimilation experiments. Hybrid modelling emerges as a promising approach to address these limitations. Hybrid models integrate a physics-based core component with …

abstract accuracy application arxiv computational cs.lg data data-driven development error error correction forecasting global low machine machine learning networks neural networks numerical numerical weather prediction prediction prediction models progress requirements stat.ml struggle type weather weather prediction