Personal‐computer‐based system for real‐time reconstruction of the three‐dimensional ionosphere using data from diverse sources

Abstract

We present new capabilities of our system for monitoring the ionosphere over a fixed geographical area with dimensions of the order of several thousand kilometers. The system employs a nonlinear representation for electron density that ensures a nonnegative solution. The multidimensional nonlinear inverse problem is efficiently solved using a combination of the Newton‐Kontorovich method and Tikhonov's regularization technique for ill‐posed problems. The system is able to utilize a variety of types of ionospheric data, which are as follows: networks of ground‐ and space‐based (satellite mounted) dual‐frequency GPS receivers provide time series of oblique absolute total electron content (TEC) and/or relative TEC data (directly calculated from the raw dual‐frequency group delays and phase delays, respectively), TEC data from ground‐ or space‐based receivers operating with dual‐frequency beacons mounted on low‐Earth orbit (LEO) satellites, vertical TEC data from orbiting radio altimeters (such as Jason satellite), in situ electron density data from plasma probes on LEO satellites (such as Challenging Minisatellite Payload for Geophysical Research and Application), and electron density profiles from sounders. The resulting solution for the distribution of electron density is guaranteed to be smooth in space and time and to agree with all input data within errors of measurement. Real time performance is attained on a single personal computer with 5 min data refreshment period. Operation of the system is tested on real data with various data types simultaneously present. A new form of the stabilizing functional is developed to ensure reasonable assimilation of the in situ electron density data.

The IONORT-ISP-WC system: Inclusion of an electron collision frequency model for the D-layer

“…A comprehensive specification of the ionosphere in terms of electron density, neutral particles-electrons collision frequency, and geomagnetic field is required in order to carry out an accurate ray-tracing. Therefore, when dealing with near real-time applications of ray-tracing, it is of crucial importance to have a realistic ionospheric modelling through 3-D models of ionospheric electron density, which after assimilating measured data calculate an updated 3-D image of the ionosphere (Angling and Khattatov, 2006;Thompson et al, 2006;Fridman et al, 2006Fridman et al, , 2009Decker and McNamara, 2007;McNamara et al, 2007McNamara et al, , 2008McNamara et al, , 2010McNamara et al, , 2011McNamara et al, , 2013Angling and Jackson-Booth, 2011;Shim et al, 2011). More recently, at the INGV, the IRI-SIRMUP-PROFILES (ISP) model, capable of providing a 3-D electron density profile representation of the ionosphere in quasi real time, was developed.…”

Feasibility study for reconstructing the spatial‐temporal structure of TIDs from high‐resolution backscatter ionograms

“…The application of Tikhonov regularization produces the smoothest ionosphere that is in agreement with the input data to within the data measurement error. More recently we developed the Tikhonov‐based ionospheric data assimilation capability called Global Positioning Satellite Ionospheric Inversion (GPSII) [ Fridman et al , , , ; McNamara et al , ]. GPSII is capable of ingesting data from GPS and low‐Earth orbiting satellite beacons, in situ electron density (e.g., Defense Meteorological Satellite Program satellites), the Jason altimeter, the Doppler orbitography and radiopositioning integrated by satellite system, and vertical incidence sounders to derive a three‐dimensional ionosphere model that is both spatially and temporally smooth but is yet in agreement with all the input data to within the data measurement error.…”

Ionospheric electron density profile estimation using commercial AM broadcast signals

“…However, due to the lack of horizontal ray paths the vertical precision obtained by CIT is not good. Researchers have proposed many methods to solve this problem, such as bringing in radio occultation data by carrying receivers on low Earth orbit (LEO) satellites (Tsai et al, 2001); employing ionosonde data (Garcı́a-Fernández et al, 2003, Ma et al, 2005; absorbing incoherent scatter radar observations (Chartier et al, 2012); and using the vertical ionograms, backscatter ionograms and CHAMP satellite data together (Fridman and Nickisch, 2001, Fridman et al, 2009, Zhao et al, 2010. These methods can improve the vertical precision to a certain degree; however, these methods cannot obtain high-precision imaging because of the sparseness of ionosondes.…”