Ionospheric weather disturbances can have detrimental effects on a variety of civilian and military systems and operations. They can affect over-the-horizon (OTH) radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the FAA's Wide-Area Augmentation System (WAAS). In an effort to mitigate and/or forecast the adverse effects of the ionosphere on these systems, we developed two physics-based data assimilation models of the ionosphere under a program called Global Assimilation of Ionospheric Measurements (GAIM). One of the 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+), but the main output of the model is a 3-dimensional electron density distribution at user specified times. The second data assimilation model uses a physics-based ionosphere-plasmasphere-polar wind model and a Kalman filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. This full physics model, which is global and covers the altitude range of from 90 to 30,000 km, is more sophisticated than the Gauss-Markov model, and hence, should provide more reliable specifications in data poor regions and during severe weather disturbances. Both of these GAIM models assimilate bottom-side Ne profiles from a variable number of ionosondes, slant TEC from a variable number of ground GPS/TEC stations, in situ Ne 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 GAIM models and these models take account of latent data (up to 3 hours). The characteristics and uses of these models will be discussed.