[1] We present a system that processes phase and group delay time series from a network of dual-frequency GPS receivers and produces a dynamic ionospheric model that is consistent with all the input data. The system is intended for monitoring the ionosphere over a fixed geographical area with dimensions of the order of several thousand kilometers. The inversion technique utilized in this system stems from the inversion technique previously developed by our group within the Coordinate Registration Enhancement by Dynamic Optimization (CREDO) project (a software package for inverting the vertical sounding, backscatter sounding, and satellite total electron content (TEC) data for over-the-horizon radar). The core of this technique is Tikhonov's methodology for solving ill-posed problems. We extended the method to multidimensional nonlinear inverse problems and developed techniques for fast numerical solution. The resulting solution for the ionospheric distribution of electron density is guaranteed to be smooth in space and time and to agree with all input data within errors of measurement. The input data consist of time series of absolute TEC and relative TEC (directly calculated from the raw dual-frequency group delays and phase delays, respectively). The system automatically estimates the measurement noise and receiver-transmitter biases. We test the system using archived data from dual-frequency GPS receivers in the southern California Scripps Orbit and Permanent Array Center (SOPAC) network and data from a vertical sounder.Citation: Fridman, S. V., L. J. Nickisch, M. Aiello, and M. Hausman (2006), Real-time reconstruction of the threedimensional ionosphere using data from a network of GPS receivers, Radio Sci., 41, RS5S12,

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.

 A novel technique is presented for assimilating fragmentary sensor data to produce a global-scale space weather nowcast.  The technique iteratively transforms ("morphs") an underlying global climatology model into agreement with available sensor data.  The technique senses the inherent multi-scale diurnal periodicity of geosystems to restore missing information over no-data regions.

Abstract. An ionospheric reconstruction method is developed that is capable of simultaneously utilizing information from all possible sources of ionospheric measurement. We describe the method and demonstrate its performance on ionospheric data collected by an over-the-horizon radar (vertical and backscatter ionograms) and total electron content data collected by receivers operated in the Caribbean by the Applied Research Laboratories of the University of Texas at Austin. For each data set the method produces a smooth three-dimensional ionospheric model that is consistent with all the measurements. The method is based on the Newton-Kontorovich method for nonlinear operator equations and Tikhonov's regularization technique for ill-posed problems. We demonstrate here the ability of our technique to reconstruct ionospheric spatial variations from global-scale to medium-scale irregularities or traveling ionospheric disturbances. Overall results of this study demonstrate that the technique suggested makes it possible to unite data from various ionospheric instruments to provide monitoring of the ionosphere over a large geographical area in a wide range of spatial scales.

[1] Over-the-horizon radar (OTHR) uses ionospheric reflection to propagate HF transmissions to long range ($500-5000 km). The ionosphere acts as a dynamic "mirror" that varies diurnally, seasonally, and with the solar cycle. Geolocation of targets observed by OTHR (Coordinate Registration (CR)) requires accurate real-time ionospheric modeling and HF propagation calculations to convert radar-measured target signal delays and beam steers to geographical position. We merged our backscatter ionogram (BI) leading edge inversion algorithm CREDO with our more advanced ionospheric data assimilation capability, GPS Ionospheric Inversion (GPSII). The combined algorithm produces a dynamic model of electron density for a fixed geographical region. The model is consistent with BI leading edge data, vertical sounding data, as well as with absolute and relative total electron content (TEC) data from a number of GPS/LEO receivers. Incorporation of additional ionospheric data beyond conventional OTHR vertical and oblique backscatter soundings is expected to enhance the fidelity of real-time ionosphere models, resulting in improved OTHR Coordinate Registration metric accuracy. Initial tests of the OTHR CR supported by the new ionospheric inversion algorithm indicate noticeable improvement of CR accuracy in comparison with legacy techniques.Citation: Fridman, S. V., L. J. Nickisch, and M. Hausman (2012), Inversion of backscatter ionograms and TEC data for over-the-horizon radar, Radio Sci., 47, RS0L10,