A data assimilation model of the ionosphere

Physics-based data assimilation models of the ionosphere were developed at Utah State University as part of a DoD Multidisciplinary University Research Initiative (MURI) program. The USU effort was called Global Assimilation of Ionospheric Measurements (GAIM). One of the USU data assimilation models has been selected for operational use at the Air Force Weather Agency (AFWA) in Omaha, Nebraska. This model is a Gauss-Markov Kalman Filter (GMKF) model, and it uses a physics-based model of the ionosphere and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. The physics-based model is the Ionosphere Forecast Model (IFM), which is global and covers the E-region, F-region, and topside from 90 to 1400 km. It takes account of five ion species (NO⁺, O₂⁺, N₂⁺, O⁺, H⁺). However, the main output of the model is a 3-dimensional electron density distribution at user specified times. In addition, auxiliary parameters are also provided, including N_mE, h_mE, N_mF₂, h_mF₂, N_e(800 km), slant and vertical TEC. The Gauss-Markov Kalman Model assimilates bottom-side N_e profiles from a variable number of ionosondes, slant TEC from a variable number of ground GPS/TEC stations, in situ N_e from four DMSP satellites, and line-of-sight UV emissions measured by satellites. Quality control algorithms for all of the data types are provided as an integral part of the model and the model takes account of latent data (up to 3 hours). With the GMKF model the ionospheric densities obtained from the IFM constitute a background ionospheric density field on which perturbations are superimposed based on the available data sources and their errors. The density perturbations and the associated errors evolve over time via a statistical Gauss-Markov process. The GMKF model can also be applied to just a region (e.g., North America or Europe) with a simple change to the setup file. The configuration of the GMKF model and relevant applications will be presented.