Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8921
Authors: Pavón-Carrasco, F. J.* 
Torta, J. M.* 
Catalán, M.* 
Talarn, A.* 
Ishihara, T.* 
Title: Improving total field geomagnetic secular variation modeling from a new set 3 of cross-over marine data
Journal: Physics of the Earth and Planetary Interiors 
Series/Report no.: /216 (2013)
Publisher: Elsevier Science Limited
Issue Date: Mar-2013
DOI: 10.1016/j.pepi.2013.01.002
URL: http://earthref.org/ERDA/1728/
Keywords: Geomagnetism
Secular variation
Regional modeling
North Atlantic Ocean
Spherical cap harmonic analysis
Geomagnetic marine data
Subject Classification04. Solid Earth::04.05. Geomagnetism::04.05.02. Geomagnetic field variations and reversals 
04. Solid Earth::04.05. Geomagnetism::04.05.03. Global and regional models 
04. Solid Earth::04.05. Geomagnetism::04.05.04. Magnetic anomalies 
04. Solid Earth::04.05. Geomagnetism::04.05.05. Main geomagnetic field 
04. Solid Earth::04.05. Geomagnetism::04.05.08. Instruments and techniques 
Abstract: A new set of cross-over marine data has been used to generate a regional model for the secular variation of the total geomagnetic field, showing the potential of the suggested approach for gaining a better knowledge of the field over oceanic regions. The model, which is valid for the Northern Atlantic region during the temporal interval 1960–2000, was obtained using spherical cap harmonic analysis (SCHA) in space and penalized splines in time. The maximum spatial expansion is equivalent to degree 9 in ordinary spherical harmonic analysis. Annual mean intensity data from different geomagnetic observatories have been used to improve the spatial and temporal resolution of the original dataset. Results indicate that the regional model improves, in terms of the root mean square error, the prediction given by the 11th generation of IGRF and CM4 global models, especially for the geomagnetic observatories considered. We also provide the uncertainty of the model coefficients and the secular variation prediction given by a bootstrap algorithm. The model is available in the EarthRef. org Digital Archive at http://earthref.org/ERDA/1728/.
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