Data assimilation and driver estimation for the Global Ionosphere–Thermosphere Model using the Ensemble Adjustment Kalman Filter

Abstract

This paper proposes a differential inflation scheme and applies this technique to driver estimation for the Global Ionosphere–Thermosphere Model (GITM) using the Ensemble Adjustment Kalman Filter (EAKF), which is a part of the Data Assimilation Research Testbed (DART). Driver estimation using EAKF is first demonstrated on a linear example and then applied to GITM. The Challenging Minisatellite Payload (CHAMP) neutral mass density measurements are assimilated into a biased version of GITM, and the solar flux index, F10.7, is estimated. Although the estimate of F10.7 obtained using DART does not converge to the measured values, the converged values are shown to drive the GITM output close to CHAMP measurements. In order to prevent the ensemble spread from converging to zero, the state and driver estimates are inflated. In particular, the F10.7 estimate is inflated to have a constant variance. It is shown that EAKF with differential inflation reduces the model bias from 73% down to 7% along the CHAMP satellite path when compared to the biased GITM output obtained without using data assimilation. The Gravity Recovery and Climate Experiment (GRACE) density measurements are used to validate the data assimilation performance at locations different from measurement locations. It is shown that the bias at GRACE locations is decreased from 76% down to 52% as compared to not using data assimilation, showing that model estimation of the thermosphere is improved globally.