The rapid increase in space based ionospheric measurements combined with the development of Global Assimilative Ionospheric Model (GAIM) make the real-time monitoring and short to medium term forecast of the ionospheric weather conditions possible. As for the meteorological models, the ionospheric data assimilation methods provide a physical principle based integration of diverse sources of ionospheric measurements with a theoretical model of ionospheric dynamics. For ionospheric data assimilation in particular, the estimation of the main ionospheric driving forces such as thermospheric densities and wind, electrical field and solar radiation intensity is as important as the estimation of the electron and ion densities for ionospheric forecast. Two complementary data assimilation approaches are implemented for the USC/JPL GAIM. The first consists of a variation of the Kalman filter recursive estimation approach. The second is a 4-dimensional variational data assimilation approach (4DVAR). These approaches are used to optimally estimate both the electron and ion densities, as well as, the main ionospheric drivers. Our model is developed to assimilate a variety of ionospheric measurements such as ground and space based Total Electron Content (TEC) measurements from GPS, space based UV measurements, in-situ measurements of electron density and, ground based ionosonde measurements. In this presentation, we shall discuss the methodology we employed in our model and our experience in transitioning our model into a reliable operational ionospheric monitoring and forecast system. An important part of our experience comes from our systematic and extensive validation efforts using a variety of independent ionospheric measurements. We shall also present findings from our model validation efforts.