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

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

doi:https://doi.org/10.5194/wcd-4-287-2023

Articles | Volume 4, issue 2

https://doi.org/10.5194/wcd-4-287-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wcd-4-287-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Data sets

The ERA-Interim reanalysis: configuration and performance of the data assimilation system (https://apps.ecmwf.int/datasets/data/interim-full-daily) D. P. Dee, S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S.~B. Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.-N. Thépaut, and F. Vitart https://doi.org/10.1002/qj.828

The subseasonal to seasonal (S2S) prediction project database (https://apps.ecmwf.int/datasets/data/s2s) F. Vitart, C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Déqué, L. Ferranti, E. Fucile, M. Fuentes, H. Hendon, J. Hodgson, H. S. Kang, A. Kumar, H. Lin, G. Liu, X. Liu, P. Malguzzi, I. Mallas, M. Manoussakis, D. Mastrangelo, C. MacLachlan, P. McLean, A. Minami, R. Mladek, T. Nakazawa, S. Najm, Y. Nie, M. Rixen, A. W. Robertson, P. Ruti, C. Sun, Y. Takaya, M. Tolstykh, F. Venuti, D. Waliser, S. Woolnough, T. Wu, D. J. Won, H. Xiao, R. Zaripov, and L. Zhang https://doi.org/10.1175/BAMS-D-16-0017.1

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 %.