Hyperbolic Decay of the Dst Index during the Recovery Phase of Intense Geomagnetic Storms
Autores
Aguado Molina, Jesús; Cid Tortuero, Consuelo; Saiz Villanueva, María Elena; Cerrato Montalbán, YolandaIdentificadores
Enlace permanente (URI): http://hdl.handle.net/10017/29122DOI: 10.1029/2009JA014658
ISSN: 0148-0227
Fecha de publicación
2010-07-21Patrocinadores
Ministerio de Ciencia e Innovación
Junta de Comunidades de Castilla La Mancha
Cita bibliográfica
Journal of Geophysical Research, 2010, n. 115, p. 1-7
Palabras clave
Space Physics
Geomagnetic storms
Proyectos
info:eu-repo/grantAgreement/MICINN//AYA2009-08662/ES/Meteorologia Espacial/
info:eu-repo/grantAgreement/JCCM//PPII10-0183-7802
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
http://dx.doi.org/10.1029/2009JA014658Derechos
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0
© American Geophysical Union, 2010
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
What one commonly considers for reproducing the recovery phase of magnetosphere,as seen by the Dst index, is exponential function. However, the magnetosphererecovers faster in the first hours than in the late recovery phase. The early steepnessfollowed by the late smoothness in the magnetospheric response is a feature that leadsto the proposal of a hyperbolic decay function to reproduce the recovery phase instead ofthe exponential function. A superposed epoch analysis of recovery phases of intensestorms from 1963 to 2003 was performed, categorizing the storms by their intensity intofive subsets. The hyperbolic decay function reproduces experimental data better than whatthe exponential function does for any subset of storms, which indicates a nonlinearcoupling between dDst/dt and Dst. Moreover, this kind of mathematical function, wherethe degree of reduction of the Dst index depends on time, allows for explaining differentlifetimes of the physical mechanisms involved in the recovery phase and provides newinsights for the modeling of the Dst index.
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