An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting
<|reference_start|>An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting: We present an operations-ready multi-model ensemble weather forecasting system which uses hybrid data-driven weather prediction models coupled with the European Centre for Medium-range Weather Forecasts (ECMWF) ocean model to predict global weather at 1-degree resolution for 4 weeks of lead time. For predictions of 2-meter temperature, our ensemble on average outperforms the raw ECMWF extended-range ensemble by 4-17%, depending on the lead time. However, after applying statistical bias corrections, the ECMWF ensemble is about 3% better at 4 weeks. For other surface parameters, our ensemble is also within a few percentage points of ECMWF's ensemble. We demonstrate that it is possible to achieve near-state-of-the-art subseasonal-to-seasonal forecasts using a multi-model ensembling approach with data-driven weather prediction models.<|reference_end|>
@article{weyn2024an,
    title={An ensemble of data-driven weather prediction models for operational
  sub-seasonal forecasting},
    author={Jonathan A. Weyn, Divya Kumar, Jeremy Berman, Najeeb Kazmi, Sylwester
  Klocek, Pete Luferenko, Kit Thambiratnam},
    journal={arXiv preprint arXiv:2403.15598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15598},
    primaryClass={physics.ao-ph cs.LG}
}