Assimilation of gridded neutral and electron densities into TIE-GCM

Atmospheric drag acceleration is the largest non gravitational acceleration acting on low Earth orbiting satellites. Precise models of the drag acceleration are needed for precise orbit determination of satellites that are not equipped with accelerometers. This applies to many Earth observation satellites such as altimeter satellites. The drag acceleration mainly depends on the density of the atmosphere. Both empirical and physical models of the upper atmosphere often fail to provide sufficient estimates of the density. Therefore, we use an ensemble Kalman filter to improve the density estimation of a physical model (TIE-GCM). In previous experiments, we showed that by assimilating accelerometer derived densities from the CHAMP satellite, using a two-step approach, we were able to significantly improve drag predictions for the GRACE satellites. We first calibrated an empirical model using the accelerometer derived densities, evaluated the calibrated model on a regular global grid and then assimilated the gridded densities. The two-step approach enables us to update the state of the atmosphere globally without relying on a correct representation of long-range correlations in the ensemble. Here, we aim to assimilate electron densities in a similar way. The electron densities are computed from a 4D model based on GNSS and satellite altimetry data as well as radio occultation measurements.