An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910379436670976 |
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| author | Weyn, Jonathan A. Kumar, Divya Berman, Jeremy Kazmi, Najeeb Klocek, Sylwester Luferenko, Pete Thambiratnam, Kit |
| author_facet | Weyn, Jonathan A. Kumar, Divya Berman, Jeremy Kazmi, Najeeb Klocek, Sylwester Luferenko, Pete Thambiratnam, Kit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting Weyn, Jonathan A. Kumar, Divya Berman, Jeremy Kazmi, Najeeb Klocek, Sylwester Luferenko, Pete Thambiratnam, Kit Atmospheric and Oceanic Physics Machine Learning 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. |
| title | An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2403.15598 |