dc.date.accessioned | 2024-05-08T20:05:22Z | |
dc.date.available | 2024-05-08T20:05:22Z | |
dc.date.created | 2024-05-02T22:51:20Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Vorobeva, Ekaterina Eggen, Mari Dahl Midtfjord, Alise Danielle Benth, Fred Espen Hupe, Patrick Brissaud, Quentin Orsolini, Yvan Joseph Georges Emile G. Näsholm, Sven Peter . Estimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning. Quarterly Journal of the Royal Meteorological Society. 2024 | |
dc.identifier.uri | http://hdl.handle.net/10852/110749 | |
dc.description.abstract | There are sparse opportunities for direct measurement of upper stratospheric winds, yet improving their representation in subseasonal-to-seasonal prediction models can have significant benefits. There is solid evidence from previous research that global atmospheric infrasound waves are sensitive to stratospheric dynamics. However, there is a lack of results providing a direct mapping between infrasound recordings and polar-cap upper stratospheric winds. The global International Monitoring System (IMS), which monitors compliance with the Comprehensive Nuclear-Test-Ban Treaty, includes ground-based stations that can be used to characterize the infrasound soundscape continuously. In this study, multi-station IMS infrasound data were utilized along with a machine-learning supported stochastic model, Delay-SDE-net, to demonstrate how a near-real-time estimate of the polar-cap averaged zonal wind at 1-hPa pressure level can be found from infrasound data. The infrasound was filtered to a temporal low-frequency regime dominated by microbaroms, which are ambient-noise infrasonic waves continuously radiated into the atmosphere from nonlinear interaction between counter-propagating ocean surface waves. Delay-SDE-net was trained on 5 years (2014–2018) of infrasound data from three stations and the ERA5 reanalysis 1-hPa polar-cap averaged zonal wind. Using infrasound in 2019–2020 for validation, we demonstrate a prediction of the polar-cap averaged zonal wind, with an error standard deviation of around 12 m·s compared with ERA5. These findings highlight the potential of using infrasound data for near-real-time measurements of upper stratospheric dynamics. A long-term goal is to improve high-top atmospheric model accuracy, which can have significant implications for weather and climate prediction. | |
dc.description.abstract | Estimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning | |
dc.language | EN | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Estimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning | |
dc.title.alternative | ENEngelskEnglishEstimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning | |
dc.type | Journal article | |
dc.creator.author | Vorobeva, Ekaterina | |
dc.creator.author | Eggen, Mari Dahl | |
dc.creator.author | Midtfjord, Alise Danielle | |
dc.creator.author | Benth, Fred Espen | |
dc.creator.author | Hupe, Patrick | |
dc.creator.author | Brissaud, Quentin | |
dc.creator.author | Orsolini, Yvan Joseph Georges Emile G. | |
dc.creator.author | Näsholm, Sven Peter | |
cristin.unitcode | 185,15,13,0 | |
cristin.unitname | Matematisk institutt | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 2266114 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Quarterly Journal of the Royal Meteorological Society&rft.volume=&rft.spage=&rft.date=2024 | |
dc.identifier.jtitle | Quarterly Journal of the Royal Meteorological Society | |
dc.identifier.doi | https://doi.org/10.1002/qj.4731 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0035-9009 | |
dc.type.version | PublishedVersion | |
dc.relation.project | NFR/274377 | |
dc.relation.project | NFR/223252 | |
dc.relation.project | NILU/118005 | |