 |
Center for Nonlinear Studies |
We present an overview of a relatively simple physics-based ionospheric model and a data assimilation system that corrects model forecast using slant TEC measurements from a network of ground-based GPS receivers. The numerical global 3-D model utilizes magnetic (p-q-l) coordinates and empirical models for neutral composition, winds, and ExB drift. The model computes the spatial distribution and temporal evolution of H+, O+, He+, O2+, NO+, N2+, N+ and electrons. It solves momentum and mass conservation equations for all seven ion species and electrons and the energy conservation equation for the three major ions (H+, O+, and He+) and electrons. Two data assimilation frameworks have been implemented in order to assimilate GPS data into the model and to correct model results: a sub-optimal sequential Kalman Filter and the Ensemble Square Root Kalman Filter. An ionospheric nowcasting system consisting of the physical model and the sequential Kalman Filter has been deployed operationally and generates quantitative estimates of the ionospheric state (concentrations, velocities, and temperatures of ions and electrons) continuously with a 6-hour delay from real time. Results of validation against various correlative data are presented. A second system, based on the Square Root Kalman Filter has been deployed as a research tool. Results of Observing System Simulation Experiments (OSSE) are shown, demonstrating the assimilation system's ability to restore unknown ExB drift velocities at the equator and neutral winds. Practical applications of the developed systems are discussed, the primary one being calculations and delivery of ionospheric corrections for GPS and GLONASS users.
Host: Hosted by the Information Science and Technology Institute (ISTI)