dc.contributor.author | Vorobeva, Ekaterina | |
dc.contributor.author | Eggen, Mari Dahl | |
dc.contributor.author | Midtfjord, Alise Danielle | |
dc.contributor.author | Benth, Fred Espen | |
dc.contributor.author | Hupe, Patrick | |
dc.contributor.author | Brissaud, Quentin | |
dc.contributor.author | Orsolini, Yvan Joseph Georges Emile G. | |
dc.contributor.author | Näsholm, Sven Peter | |
dc.date.accessioned | 2024-05-06T09:03:29Z | |
dc.date.available | 2024-05-06T09:03:29Z | |
dc.date.created | 2024-05-02T22:51:20Z | |
dc.date.issued | 2024 | |
dc.identifier.issn | 0035-9009 | |
dc.identifier.uri | https://hdl.handle.net/11250/3129162 | |
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 −1 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Wiley | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Estimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning | en_US |
dc.title.alternative | Estimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Quarterly Journal of the Royal Meteorological Society | en_US |
dc.identifier.doi | 10.1002/qj.4731 | |
dc.identifier.cristin | 2266114 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |