The datasets of daily variations is obtained from the geomagnetic field raw observations at the Coimbra Magnetic Observatory (COI, Portugal). The data set was obtained for the 01.01.2007-31.12.2017 time interval and covers almost the entire solar cycle 24. The raw data were processed using two methods to extract daily variability. The first method uses the so-called “geomagnetically quiet days” to calculate S-type variations as daily means resulting in the data sub-set named “IQD Sq and SD”. The second method uses the principal component analysis (PCA) to extract main variability modes of the original data. The first three modes produced by PCA and explaining up to 98% of the variability of the raw data are in the data sub-set named “PCA modes”. Both methods allow to extract regular geomagnetic field variations related to daily variations (S-type variations) in the ionospheric dynamo region and some magnetospheric currents (e.g., field-aligned currents). The COI location in middle latitudes near the mean latitude of the ionospheric Sq current vortex's focus allows studying its seasonal and decadal variability using the S-type regular variations of the geomagnetic field measured near the ground. The S-type variations for the X and Y components of the geomagnetic field obtained at the COI observatory can also be re-scaled and used to analyze geomagnetic field variations obtained at other European geomagnetic observatories at close latitudes. The S-type variations for the Z component of the geomagnetic field obtained at the COI observatory can be compared to similar variations observed at more continental regions to study the so-called “coastal effect” in the geomagnetic field variations.

Here we present datasets of daily variation obtained from the geomagnetic field raw observations recorded at the Coimbra Magnetic Observatory (COI, Portugal) between 01.01.2007 and 31.12.2017, covering almost the entire solar cycle 24. Two methods were used to extract daily variability from the raw geomagnetic hourly data. The first method uses the so-called "geomagnetically quiet days" to calculate S-type variations as daily means resulting in the data sub-set named "IQD Sq and SD". The second method uses the principal component analysis (PCA) to decompose the original series into main variability modes. The first three modes produced by PCA and explaining up to 98% of the variability of the raw data are in the data sub-set named "PCA modes". Both methods allow to extract regular geomagnetic field variations related to daily variations (S-type variations) in the ionospheric dynamo region and some magnetospheric currents (e.g., field-aligned currents).

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.

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