• Kalman filter;
  • data assimilation;
  • thermosphere composition

[1] Global circulation models (GCMs) for the thermosphere ionosphere system have been in use for more than 20 years. In the beginning the GCMs were run on supercomputers, were expensive to run, and were used mainly to provide insight into the physics of the region and to interpret measurements. Advances in computer technology have made it possible to run GCMs on desktops and to compare their results with real-time or near-real-time measurements. Today's models are capable of reproducing generic geomagnetic storm effects, but modeling specific storms is still a challenge because accurate descriptions of the energy input during storms are not easy to obtain. One way to compensate for the uncertainty in model inputs for a given period is to assimilate measurements into the model results. In this way, meteorologists have been improving their ability to model tropospheric weather for the last few decades. Data assimilation algorithms have seen an explosive growth in the last few years, and the time has come to apply such techniques to the thermospheric storm effects problem. We present results from an ensemble Kalman filter scheme that determines the best estimate of the global height-integrated O/N2 ratio by combining GCM results and uncertainties with measurements and their errors. We describe the differences that result from the application of an ensemble Kalman filter to an externally forced system (neutral chemical composition) versus a system dominated by the initial condition and internal dynamics (tropospheric weather and ocean models). The results demonstrate that an ensemble of 10 members is able to characterize the state covariance matrix with sufficient fidelity to enable the Kalman filter to operate in a stable mode. Some information about the external forcing was extracted from the estimate of the state. The general trend of the forcing was followed by the filter, but departures were present over some periods.