Statistical analysis on spatial correlation of ionospheric day-to-day variability by using GPS and Incoherent Scatter Radar observations

Abstract

Abstract. 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.

Tomography of the ionospheric electron density with geostatistical inversion

“…Thus, its principal behavior agrees with the TEC correlation analyses of Shim et al (2008). Furthermore, we observe the temporal evolution of the horizontal covariance reaching its peak at 12:00 on day of year (DOY) 22 (2011) coinciding with the local time variations of the TEC correlation distances described in Yue et al (2007).…”

“…Bust et al (2004) suggested the separation of the spatial covariance model into horizontal and vertical components to take the geometric anisotropy of the ionosphere, i.e., directionally dependent correlation lengths, into account. Yue et al (2007) and Shim et al (2008) confirmed this approach with the analysis of GPS and incoherent scatter radar observations revealing different correlation lengths in latitude, longitude and height direction. Furthermore, the investigations of Blanch et al (2003) and Pèrez (2005) show that the exponential covariance model might be appropriate to describe spatial dependencies of the ionosphere.…”

“…Based on the investigations of Shim et al (2008), we choose to set the initial guess for the parameters θ 2 and θ 3 to about 1.5. The parameter θ 4 controls the vertical correlation length and is set to about 300 km in agreement with the analyses of Yue et al (2007). Figure 2 illustrates the estimated electron density covariance models for the three different latitudes 45 • N, 50 • N and 55 • N at the Greenwich meridian at a height of 300 km.…”

Ionospheric electron density observed by FORMOSAT-3/COSMIC over the European region and validated by ionosonde data

“…However, our approach is based on the research of the ionospheric spatial correlation (e.g. Klobuchar and Kunches 2000;Yue et al 2007). It is well-known that the correlation distance of the ionosphere depends on direction and can be regarded as anisotropic.…”

Observing System Simulation Experiment Study on Imaging the Ionosphere by Assimilating Observations From Ground GNSS, LEO-Based Radio Occultation and Ocean Reflection, and Cross Link

“…We developed a global ionospheric data assimilation model based on some previous studies [32], [33], [35], [37]. The key parameters of the model, including spatial and temporal resolutions, background and observation covariances, and background models, can be customized by the user.…”