Articles | Volume 4, issue 2
Research article
04 Apr 2023
Research article |  | 04 Apr 2023

Improved extended-range prediction of persistent stratospheric perturbations using machine learning

Raphaël de Fondeville, Zheng Wu, Enikő Székely, Guillaume Obozinski, and Daniela I. V. Domeisen

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Cited articles

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Short summary
We propose a fully data-driven, interpretable, and computationally scalable framework to characterize sudden stratospheric warmings (SSWs), extract statistically significant precursors, and produce machine learning (ML) forecasts. By successfully leveraging the long-lasting impact of SSWs, the ML predictions outperform sub-seasonal numerical forecasts for lead times beyond 25 d. Post-processing numerical predictions using their ML counterparts yields a performance increase of up to 20 %.