Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet
<|reference_start|>Data Assimilation with Machine Learning Surrogate Models: A Case Study with FourCastNet: Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations. We empirically demonstrate and theoretically justify that, despite the long-time instability of the surrogates and the sparsity of the observations, filtering estimates can remain accurate in the long-time horizon. As a case study, we integrate FourCastNet, a state-of-the-art weather surrogate model, within a variational data assimilation framework using partial, noisy ERA5 data. Our results show that filtering estimates remain accurate over a year-long assimilation window and provide effective initial conditions for forecasting tasks, including extreme event prediction.<|reference_end|>
@article{adrian2024data,
    title={Data Assimilation with Machine Learning Surrogate Models: A Case Study
  with FourCastNet},
    author={Melissa Adrian, Daniel Sanz-Alonso, Rebecca Willett},
    journal={arXiv preprint arXiv:2405.13180},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13180},
    primaryClass={eess.SP cs.LG nlin.CD physics.ao-ph stat.AP}
}