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Titel Statistical analysis on spatial correlation of ionospheric day-to-day variability by using GPS and Incoherent Scatter Radar observations
VerfasserIn X. Yue, W. Wan, L. Liu, T. Mao
Medientyp Artikel
Sprache Englisch
ISSN 0992-7689
Digitales Dokument URL
Erschienen In: Annales Geophysicae ; 25, no. 8 ; Nr. 25, no. 8 (2007-08-29), S.1815-1825
Datensatznummer 250015898
Publikation (Nr.) Volltext-Dokument vorhandencopernicus.org/angeo-25-1815-2007.pdf
 
Zusammenfassung
In this paper, the spatial correlations of ionospheric day-to-day variability are investigated by statistical analysis on GPS and Incoherent Scatter Radar observations. The meridional correlations show significant (>0.8) correlations in the latitudinal blocks of about 6 degrees size on average. Relative larger correlations of TEC's day-to-day variabilities can be found between magnetic conjugate points, which may be due to the geomagnetic conjugacy of several factors for the ionospheric day-to-day variability. The correlation coefficients between geomagnetic conjugate points have an obvious decrease around the sunrise and sunset time at the upper latitude (60°) and their values are bigger between the winter and summer hemisphere than between the spring and autumn hemisphere. The time delay of sunrise (sunset) between magnetic conjugate points with a high dip latitude is a probable reason. Obvious latitude and local time variations of meridional correlation distance, latitude variations of zonal correlation distance, and altitude and local time variations of vertical correlation distance are detected. Furthermore, there are evident seasonal variations of meridional correlation distance at higher latitudes in the Northern Hemisphere and local time variations of zonal correlation distance at higher latitudes in the Southern Hemisphere. These variations can generally be interpreted by the variations of controlling factors, which may have different spatial scales. The influences of the occurrence of ionospheric storms could not be ignored. Further modeling and data analysis are needed to address this problem. We suggest that our results are useful in the specific modeling/forecasting of ionospheric variability and the constructing of a background covariance matrix in ionospheric data assimilation.
 
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