An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting

• Main Authors: Weyn, Jonathan A, Kumar, Divya, Berman, Jeremy, Kazmi, Najeeb, Klocek, Sylwester, Luferenko, Pete, Thambiratnam, Kit
• Format: Journal Article
• Language: English
• Published: 22.03.2024
• Subjects: Computer Science - Learning; Physics - Atmospheric and Oceanic Physics
• DOI: 10.48550/arxiv.2403.15598

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.