Effects of inferring unobserved thermospheric and ionospheric state variables by using an Ensemble Kalman Filter on global ionospheric specification and forecasting

In the EnKF, the unobserved state variables are estimated and made dynamically and chemically consistent with the observed state variable, thus improving the performance of the data assimilation system. The impact on ensemble forecast is further examined by initializing the TIEGCM with the assimilation analysis. The main findings are the following: (1) by incorporating ion-neutral coupling into the EnKF, the ionospheric electron density analysis, and forecast can be considerably improved.

(2) Thermospheric composition is the most significant state variable that affects ionospheric analysis and forecast. (3) Thermospheric variables have a much longer impact on ionospheric forecast (>24 h) than ionospheric variables (2 to 3 h). (4) In the TIEGCM, the effect of assimilating electron densities is not completely transmitted to the forecast step unless the densities of ion species are estimated.