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.

我们提出了一个实用的多模式集成天气预报系统，该系统使用混合数据驱动的天气预测模型与欧洲中期天气预报中心（ECMWF）海洋模型相结合，在 4 周的前期预测全球天气，分辨率为 1 度。对于 2 米温度的预测，我们的集成模型相比于原始的 ECMWF 扩展范围集成模型，在平均性能上提高了 4-17％（根据预测提前期而定）。然而，在应用统计偏差修正之后，ECMWF 集成模型在 4 周时相比较好，优势约为 3％。对于其他地表参数，我们的集成模型与 ECMWF 集成模型的性能差距也在几个百分点以内。我们证明了使用基于数据驱动的天气预测模型的多模式集成方法可以实现接近最新技术水平的次季节到季节性天气预报。