Articles | Volume 7, issue 3
https://doi.org/10.5194/wcd-7-1307-2026
https://doi.org/10.5194/wcd-7-1307-2026
Research article
 | 
24 Jul 2026
Research article |  | 24 Jul 2026

Intraseasonal prediction of monthly storminess in the North Sea with the ACE2 atmospheric emulator and Random Forests

Proshonni Aziz, Birgit Hünicke, and Eduardo Zorita

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Proshonni Aziz on behalf of the Authors (11 Jun 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (11 Jun 2026) by Amy Butler
RR by Anonymous Referee #2 (27 Jun 2026)
RR by Anonymous Referee #1 (01 Jul 2026)
ED: Publish subject to revisions (further review by editor and referees) (02 Jul 2026) by Amy Butler
AR by Proshonni Aziz on behalf of the Authors (15 Jul 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (19 Jul 2026) by Amy Butler
AR by Proshonni Aziz on behalf of the Authors (21 Jul 2026)  Manuscript 
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Short summary
By analysing data from 1940 to 2024, we found that upper atmosphere conditions in early winter directly influence the number of North Sea storms occurring weeks or months later. We used a climate model and machine learning to improve these forecasts. Results show that December patterns can predict January storminess with high accuracy, with effects persisting for up to 60 d. Stratospheric data and machine learning together improve winter storm prediction for this region.
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