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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/33544
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dc.contributor.authorKondrashov, D-
dc.contributor.authorSudakow, I-
dc.contributor.authorLivina, V-
dc.contributor.authorYang, Q-
dc.date.accessioned2026-07-01T13:12:32Z-
dc.date.available2026-07-01T13:12:32Z-
dc.date.issued2026-02-03-
dc.identifierORCiD: Dmitri Kondrashov https://orcid.org/0000-0002-3471-7275-
dc.identifierORCiD: Ivan Sudakow https://orcid.org/0000-0003-2614-8794-
dc.identifierORCiD: Valerie Livina https://orcid.org/0000-0003-3759-9013-
dc.identifierORCiD: Qingping Yang https://orcid.org/0000-0002-2557-8752-
dc.identifier.citationKondrashov, D. et al. (2026) 'Accurate and robust real-time prediction of September Arctic sea ice', Chaos: An Interdisciplinary Journal of Nonlinear Science, 36 (2), 023110, pp. 1–16. doi: 10.1063/5.0295634.en-US
dc.identifier.issn1054-1500-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/33544-
dc.descriptionData Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. The NSIDC daily data by region is available at https://nsidc.org/sea-ice-today/sea-ice-tools .en-US
dc.description.abstractWe describe the real-time forecasting of September 2024 Arctic sea ice extent using a theory-guided machine learning method based on data-adaptive harmonic decomposition and frequency-based nonlinear stochastic modeling, as part of the Sea Ice Outlook. Compared to standard statistical and machine learning models, this method adeptly accounts for non-linear behavior, effectively incorporates memory effects, and handles a wide range of time scale variations, from synoptic (stochastic-like) weather effects to low-frequency (red-noise like) variability, significantly enhancing the accuracy and reliability of sea ice prediction.en-US
dc.description.sponsorshipThis research was supported by National Science Foundation (NSF) under Grant No. OPP-2438993. The collaborative work undertaken at the Tipping Phenomena in Environmental Dynamical Systems workshop was supported by the London Mathematical Society through Grant Scheme 3 (Grant No. 32304). Part of this research was performed while D.K. and I.S. were visiting the Institute for Mathematical and Statistical Innovation (IMSI), which is supported by the NSF under Grant No. DMS-1929348.en-US
dc.format.extentpp. 1–16-
dc.format.mediumPrint-Electronic-
dc.languageEnglishen-US
dc.language.isoengen-US
dc.publisherAmerican Institute of Physicsen-US
dc.rightsCreative Commons Attribution 4.0 International-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectnonlinear systemsen-US
dc.subjectcryosphereen-US
dc.subjectgeodesyen-US
dc.subjectgeographic locationen-US
dc.subjectmachine learningen-US
dc.subjectsignal processingen-US
dc.subjectharmonic analysisen-US
dc.subjectoperator theoryen-US
dc.subjectautocorrelationen-US
dc.subjectstochastic processesen-US
dc.titleAccurate and robust real-time prediction of September Arctic sea iceen-US
dc.typeArticleen-US
dc.date.dateAccepted2025-12-07-
dc.relation.isPartOfChaos: An Interdisciplinary Journal of Nonlinear Science-
pubs.issue2-
pubs.publication-statusPublished-
pubs.volume36-
dc.identifier.eissn1089-7682-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dcterms.dateAccepted2025-12-07-
dc.rights.holderAuthor(s)-
dc.contributor.orcidKondrashov, Dmitri [0000-0002-3471-7275]-
dc.contributor.orcidSudakow, Ivan [0000-0003-2614-8794]-
dc.contributor.orcidLivina,Valerie [0000-0003-3759-9013]-
dc.contributor.orcidYang, Qingping [0000-0002-2557-8752]-
dc.identifier.number023110-
Appears in Collections:Department of Mechanical and Aerospace Engineering Research Papers

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