  In this paper, we analyze the applicability of the principal component
analysis (PCA) as a tool to extract the Sq variation of the geomagnetic field.
We tested different geomagnetic field components and used data measured at
different levels of the solar and geomagnetic activity and during different
months. Geomagnetic field variations obtained with PCA were classified as SqPCA
using two types of reference series: SqIQD series calculated using
geomagnetically quiet days and simulations of the ionospheric field with
models. The results for the X and Y and Z components are essentially different.
The Sq variation is always filtered to the first PCA mode for the Y and Z
components. Thus, PCA can automatically extract the Sq variation from the
observations of the Y and Z components of the geomagnetic field. For the X
component, the automatic extraction of the Sq variation is not possible, and a
complimentary analysis, like a comparison to a reference series, is always
needed. We tested two types of reference series: the mean SqIQD and the outputs
of the CM5 and DIFI3 models. Our results show that both the data-based and
model-based reference series can be used but the DIFI3 model performs better.
We also recommend estimating the similarity of the series not with the
correlation analysis but using metrics that account for possible local
stretching/compressing of the compared series, for example, the dynamic time
warping (DTW) distance.
