EGU2020-3423
https://doi.org/10.5194/egusphere-egu2020-3423
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Separation of the daily quiet variation from the geomagnetic field observations with the principal component analysis

Anna Morozova1, Rania Rebbah1, and M. Alexandra Pais1,2
Anna Morozova et al.
  • 1Center for Earth and Space Research of the University of Coimbra, University of Coimbra, Coimbra, Portugal
  • 2Physics Department of the Science and Technology Faculty, University of Coimbra, Coimbra, Portugal

Geomagnetic field (GMF) variations from external sources are classified as regular diurnal or occurring during periods of disturbances. The most significant regular variations are the quiet solar daily variation (Sq) and the disturbance daily variation (SD). These variations have well recognized daily cycles and need to be accounted for before the analysis of the disturbed field. Preliminary analysis of the GMF variations shows that the principal component analysis (PCA) is a useful tool for extraction of regular variations of GMF; however the requirements to the data set length, geomagnetic activity level etc. need to be established.

Here we present preliminary results of the PCA-based Sq extraction procedure based on the analysis of the Coimbra Geomagnetic Observatory (COI) measurements of the geomagnetic field components H, X, Y and Z between 2007 and 2015. The PCA-based Sq curves are compared with the standard ones obtained using 5 IQD per month. PCA was applied to data sets of different length: either 1 month-long data set for one of 2007-2015 years or data series for the same month but from different years (2007-2015) combined together. For most of the analyzed years the first PCA mode (PC1) was identified as SD variation and the second mode (PC2) was identified as Sq variation.

How to cite: Morozova, A., Rebbah, R., and Pais, M. A.: Separation of the daily quiet variation from the geomagnetic field observations with the principal component analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3423, https://doi.org/10.5194/egusphere-egu2020-3423, 2020

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