An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting
This work addresses operational weather forecasting for meteorologists and climate scientists, offering a competitive but incremental approach to sub-seasonal predictions.
The authors tackled sub-seasonal weather forecasting by developing an operations-ready multi-model ensemble system using hybrid data-driven models, achieving average improvements of 4-17% over the ECMWF ensemble for 2-meter temperature predictions, though ECMWF was slightly better after bias correction at 4 weeks.
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.