Based on the thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM), a thermospheric-ionospheric data assimilation and forecast system is developed. Using this system, we estimated the oxygen ions, neutral temperature, wind, and composition by assimilating the simulated data from Formosa Satellite 3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) occultation electron density profiles to evaluate their effects on the ionospheric forecast. An ensemble Kalman filter data assimilation scheme and combined state and parameter estimation methods are used to estimate the unobserved parameters in the model. The statistical results show that the neutral and ion compositions are more effective than the neutral temperature and wind for improving the forecast of the ionospheric electron density, whose root mean square errors in the assimilation period decreased by approximately 40%, 30%, and 10% due to the estimations of the neutral composition, oxygen ions, and neutral temperature, respectively. Due to the different physical and chemical processes that these parameters primarily affect, their e-folding times differ greatly from longer than 12 h for neutral composition to approximately 6 h for oxygen ions and 3 h for neutral temperature. The effect of estimating the neutral composition on improving the ionospheric forecast is greater than that of estimating the oxygen ions, which can be also be seen in an actual data assimilation experiment. This indicates that the neutral composition is the most important thermospheric parameter in ionospheric data assimilations and forecasts. © 2018, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.