Specifying the equatorial ionosphere using CINDI on C/NOFS, COSMIC, and data interpolating empirical orthogonal functions

Abstract

[1] Data Interpolating Empirical Orthogonal Functions (DINEOFs) are a data-based method for determining a few orthogonal basis functions that optimally reproduce a given data set. This technique is applied to meridional drift measurements performed by the Coupled Ion Neutral Dynamics Investigation (CINDI) onboard the Communication/Navigation Outage Forecasting System (C/NOFS) as well as electron density profiles derived from GPS Radio Occulations (RO) performed by the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). The low densities of the equatorial ionosphere spanning 2009-2010 restricted quality drift measurements by CINDI to altitudes near perigee, limiting the local time coverage of measurements. Full local time descriptions may be obtained as perigee moves through all local times though this requires a minimum 67 day season. To increase the data coverage of the ionosphere, CINDI data are supplemented with COSMIC GPS RO data. DINEOFs are applied to median meridional drift measurements as well as COSMIC measurements spanning 2009-2010 and are used to make a best estimate of the equatorial ionosphere at locations not observed. The scattered distribution of COSMIC profiles as well as the physical relationship between meridional ion drifts and the distribution of density with altitude improves the quality of the reconstructions compared to using CINDI alone. The DINEOF reconstructions demonstrate that the annual anomaly of reduced ionospheric densities in June compared to December measured by COSMIC is coincident with a change in the meridional ion drifts at the geomagnetic equator measured by CINDI.

Ionosphere data assimilation modeling of 2015 St. Patrick's Day geomagnetic storm

“…The eastward electric field then induce the vertical plasma drift and uplift the plasma to a higher altitude and cause the separation of EIA crests in the assimilation model. The effect of the vertical drift in modification of the EIA crests during quiet and storm periods have been reported by several observational, statistical, and theoretical studies [e.g., Rastogi and Klobuchar , ; Chen et al ., ; Lin et al ., ; Stoneback et al ., ]. The feature of strong vertical drift in this assimilation model around Indian sector at 1400 UT on 17 March 2015 has been confirmed by the observational results reported by Joshi et al .…”

A high‐resolution model of the external and induced magnetic field at the Earth's surface in the Northern Hemisphere

“…These types of benefits have led to EOFs being used recently in studies of other aspects of ionospheric electrodynamics, for instance, the electric field and plasma motion [ Matsuo et al , ; Kim et al , ; Cousins et al , , ], field‐aligned current [ Cousins et al , ; Milan et al , ], and particle precipitation and associated conductivity [ Stoneback et al , ; McGranaghan et al , , ]. EOFs have also been applied to the geomagnetic field, as here, by Golovkov et al [], Shore et al [], and the studies examined above [ Sun et al , ; Xu and Kamide , ; Balasis and Egbert , ].…”

EOF analysis and modeling of GPS TEC climatology over North America

“…[] and Stoneback et al . [], indicate that the EOF analysis is a powerful method for use in ionospheric data analysis and empirical modeling.…”

“…Further details of this method can be found in Dvinskikh [1988], which presents an initial application of the technique to ionospheric modeling. Prior studies, as demonstrated in Singer and Taubenheim [1990], Bossy and Rawer [1990], Singer and Dvinskikh [1991], Dvinskikh and Naidenova [1991], Marsh et al [2004], Matsuo et al [2002Matsuo et al [ , 2005, Liu et al [2008], Wan et al [2012], A et al [2012], Zhang et al [2013] and Stoneback et al [2013], indicate that the EOF analysis is a powerful method for use in ionospheric data analysis and empirical modeling. TEC changes with longitude, latitude, local time, season and day number of the year, and the phase of solar cycle, so the hourly and monthly average TEC data derived from the previous step can be decomposed using one of the three possible combinations of basis function E k and principal component coefficient P k :…”