Exploring Ionospheric Modeling Methods: Towards a Global Ionospheric Monitor

Explosive growth in the availability of ionospheric measurements from ground and space provide a fundamentally new opportunity to image the ionosphere as never before. As examples, ground receiver TEC, occultation TEC, space-based whole-Earth UV disc emission, in-situ measurements of electron density, and ground based ionosonde measurements of both bottom-side profiles and critical parameters are all highly desirable and plentiful. However, these disparate measurements must be blended together carefully, each having its own unique capabilities and challenges. Towards this end, we have developed the Global Assimilative Ionospheric Model (GAIM). Utilizing two separate complementary assimilation approaches, such observations are intelligently combined with prior climatological knowledge to yield the best estimate of the current ionospheric state. The first, a variation of the Kalman filter recursive estimation approach, is optimized to produce the best fit ionosphere with emphasis in regions of higher data density relying on a climatological background to assist in regions of sparse data. The second, a 4-dimensional variational approach (4DVAR) instead adjusts physical driver estimates (thermospheric densities and winds, electric fields, and solar radiation intensity in our case) to smoothly match incoming data and spread its influence in a physically consistent manner. For ionospheric data in particular, estimation of these ionospheric drivers is of paramount importance, as the ionosphere is a heavily dissipative system, and forecast without proper drivers is nearly impossible. In this presentation, we shall address our methods and implementation, share our experiences transitioning this research system into an operational model for the AFRL and others, and show validation results of its operation.