An ensemble of data-driven weather prediction models for operational
  sub-seasonal forecasting
Jonathan A. Weyn Divya Kumar Jeremy Berman Najeeb Kazmi Sylwester Klocek Pete Luferenko Kit Thambiratnam
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.