Principal component analysis as a tool to extract Sq variation from the geomagnetic field observations: Conditions of applicability - Morozova, A. L. (2023)
We analyzed the applicability of the principal component analysis (PCA) as a tool to extract the Sq variation of the geomagnetic field (GMF) taking into account different geomagnetic field components, data measured at different levels of the solar and geomagnetic activity, data from different months. The validation of the method was performed with geomagnetic data obtained at the Coimbra Magnetic Observatory in Portugal (40° 13' N, 8° 25.3' W, 99 m a.s.l., IAGA code COI). GMF variations obtained with PCA were "classified" as SqPCA using reference series: (1) obtained from the observational data (SqIQD), (2) simulated by ionospheric field models. While our results show that both the data-based and model-based reference series can be used, the DIFI3 model performs better as a reference series for GMF at middle latitudes. We also recommend to estimate the similarity of the series with a metric that account for possible local stretching/compressing of the compared series, for example, the dynamic time warping (DTW) distance. Since the validation of the method was performed on the geomagnetic series obtained at a mid-latitudinal European observatory, we recommend performing additional tests when applying this method to data obtained in other regions/latitudes.•For the Y and Z components of the geomagnetic field PCA can be used to extract Sq variations from the observations without any additional procedures and SqPCA is equals to PC1.•For the X component PCA can be used to extract Sq variation from the observations of the X component, but further analysis, for example, a comparison to a set of reference curves either obtained from the data analysis or generated using models, is always needed to classify PCs of the X component.•We recommend to use data generated by DIFI-class models as reference series and the dtw metric (dynamic time warping distance) to classify SqPCA.