Forecasting of the Thermosphere via Assimilating Electron Density and Temperature Data

The paper presents experiments of driving a physics-based thermosphere model by assimilating electron density (Ne) and temperature (Tn) data using the ensemble adjustment Kalman filter (EAKF) technique. This study not only helps to gauge the accuracy of the assimilation, to explain the inherent model bias, and to understand the limitations of the framework, but it also establishes EAKF as a viable technique to forecast the highly dynamical thermosphere using realistic data assimilation scenarios. The results from the perfect model scenarios show that data assimilation changes and, more often than not, improves the model state. Data from Swarm-A, Swarm-C, CHAMP, and GRACE-A are used to validate the resulting analysis states. Independent validation results show that the Ne-guided thermosphere state does not outperform the model state without data assimilation along the considered satellite orbits. This may be due to the limited number of bonafide Ne profiles available for the thermosphere specification tasks in the experiments. More importantly, the results show that the Ne-guided thermosphere state does not deteriorate much in performance during geomagnetic storm time. The results reveal a few challenges of using Ne profiles in a hypothetical operational data assimilation exercise. In terms of estimating the mass density along the orbits of both CHAMP and GRACE-A satellites, the experiment with assimilating Tn shows more promise over Ne. The results show that the improvement gained in the overall forecasted thermosphere state is better during solar minimum compared to that of solar maximum. These results also provide insights into the biases inherent in the physics-based model. The systematic biases that the paper highlight could be an indication that the specification of plasma-neutral interactions in the model needs further adjustments.