The Jet Propulsion Laboratory/University of Southern California Global Assimilation Ionospheric Model (JPL/USC GAIM) uses two data assimilation techniques to optimally combine ionospheric measurements with the physics model: a sparse, traditional Kalman filter to estimate the three-dimensional density state, and a four-dimensional variational approach (4DVAR) to estimate ionospheric drivers such as the equatorial E × B drift or neutral winds. In this paper we study a specific implementation of the JPL/USC GAIM Kalman filter (single ion, low-resolution, and input data from 200 ground GPS sites) and validate its global accuracy over 137 days by comparisons to independent GPS slant total electron content (TEC) observations (“missing site” tests) and independent JASON vertical TEC observations. The assimilation accuracy is robust with a slant TEC spatial prediction RMS error of 4 TECU (Total Electron Content Unit, 1 × 1016 e-/m2) on average and a vertical TEC JASON RMS error of 7 TECU. Removing what appears to be a positive ≈4.4 TECU bias from the JASON observations, we obtain an improved performance of 5.3 TECU over the oceans. Comparisons with a single, thin shell global ionospheric map model and the International Reference Ionosphere and Bent ionospheric models are also provided.