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

• URL: http://arxiv.org/abs/2403.15598v1
• Date: Fri, 22 Mar 2024 20:01:53 GMT
• Title: An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting
• Authors: Jonathan A. Weyn, Divya Kumar, Jeremy Berman, Najeeb Kazmi, Sylwester Klocek, Pete Luferenko, Kit Thambiratnam,
• Abstract summary: We present an operations-ready multi-model ensemble weather forecasting system. 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.
• Score: 0.08106028186803123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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