Assimilation of sparse continuous near-earth weather measurements by NECTAR model morphing

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dc.contributor.authorGalkin, I. A.
dc.contributor.authorReinisch, B. W.
dc.contributor.authorVesnin, A. M.
dc.contributor.authorBilitza, D.
dc.contributor.authorFridman, S.
dc.contributor.authorHabarulema, J. B.
dc.contributor.authorVeliz, Oscar
dc.date.accessioned2021-06-15T10:32:40Z
dc.date.available2021-06-15T10:32:40Z
dc.date.issued2020-11
dc.description.abstractNon-linear Error Compensation Technique with Associative Restoration (NECTAR) is a novel approach to the assimilation of fragmentary sensor data to produce a global nowcast of the near-Earth space weather. NECTAR restores missing information by iteratively transforming (“morphing”) an underlying global climatology model into agreement with currently available sensor data. The morphing procedure benefits from analysis of the inherent multiscale diurnal periodicity of the geosystems by processing 24-hr time histories of the differences between measured and climate-expected values at each sensor site. The 24-hr deviation time series are used to compute and then globally interpolate the diurnal deviation harmonics. NECTAR therefore views the geosystem in terms of its periodic planetary-scale basis to associate observed fragments of the activity with the grand-scale weather processes of the matching variability scales. Such approach strengthens the restorative capability of the assimilation, specifically when only a limited number of observatories is available for the weather nowcast. Scenarios where the NECTAR concept works best are common in planetary-scale near-Earth weather applications, especially where sensor instrumentation is complex, expensive, and therefore scarce. To conduct the assimilation process, NECTAR employs a Hopfield feedback recurrent neural network commonly used in the associative memory architectures. Associative memories mimic human capability to restore full information from its initial fragments. When applied to the sparse spatial data, such a neural network becomes a nonlinear multiscale interpolator of missing information. Early tests of the NECTAR morphing reveal its enhanced capability to predict system dynamics over no-data regions (spatial interpolation).es_ES
dc.description.peer-reviewPor pareses_ES
dc.formatapplication/pdfes_ES
dc.identifier.citationGalkin, I. A., Reinisch, B. W., Vesnin, A. M., Bilitza, D., Fridman, S., Habarulema, J. B., & Veliz, O. (2020). Assimilation of sparse continuous near-earth weather measurements by NECTAR model morphing.==$Space Weather, 18$==(11). https://doi.org/10.1029/2020SW002463es_ES
dc.identifier.doihttps://doi.org/10.1029/2020SW002463es_ES
dc.identifier.govdocindex-oti2018
dc.identifier.journalSpace Weatheres_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12816/4948
dc.language.isoenges_ES
dc.publisherAmerican Geophysical Uniones_ES
dc.relation.ispartofurn:issn:1542-7390
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/es_ES
dc.subjectData assimilationes_ES
dc.subjectDiurnal harmonic analysises_ES
dc.subjectHopfield networkses_ES
dc.subjectModel morphinges_ES
dc.subjectSpatial predictiones_ES
dc.subjectWeather nowcastes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.05.01es_ES
dc.titleAssimilation of sparse continuous near-earth weather measurements by NECTAR model morphinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES

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