Global estimation of thermospheric neutral density (TND) on various altitudes is important for geodetic and space weather applications. This is typically provided by models, however, the quality of these models is limited due to their imperfect structure and the sensitivity of their parameters to the calibration period. Here, we present an ensemble Kalman filter (EnKF)-based calibration and data assimilation (C/DA) technique that updates the model’s states and simultaneously calibrates its key parameters. Its application is demonstrated using the TND estimates from on-board accelerometer measurements, e.g., those of the Gravity Recovery and Climate Experiment (GRACE) mission (at $$\sim 410$$ ∼ 410 km altitude), as observation, and the frequently used empirical model NRLMSISE-00. The C/DA is applied here to re-calibrate the model parameters including those controlling the influence of solar radiation and geomagnetic activity as well as those related to the calculation of exospheric temperature. The resulting model, called here ‘C/DA-NRLMSISE-00’, is then used to now-cast TNDs and individual neutral mass compositions for 3 h, where the model with calibrated parameters is run again during the assimilation period. C/DA-NRLMSISE-00 is also used to forecast the next 21 h, where no new observations are introduced. These forecasts are unique because they are available globally and on various altitudes (300–600 km). To introduce the impact of the thermosphere on estimating ionospheric parameters, the coupled physics-based model TIE-GCM is run by replacing the O2, O1, He and neutral temperature estimates of the C/DA-NRLMSISE-00. Then, the non-assimilated outputs of electron density (Ne) and total electron content (TEC) are validated against independent measurements. Assessing the forecasts of TNDs with those along the Swarm-A ($$\sim 467$$ ∼ 467 km), -B ($$\sim 521$$ ∼ 521 km), and -C ($$\sim 467$$ ∼ 467 km) orbits shows that the root-mean-square error (RMSE) is considerably reduced by 51, 57 and 54%, respectively. We find improvement of 30.92% for forecasting Ne and 26.48% for TEC compared to the radio occulation and global ionosphere maps (GIM), respectively. The presented C/DA approach is recommended for the short-term global multi-level thermosphere and enhanced ionosphere forecasting applications.

• Investigates the impact of solar activity on forecasting through assimilation of COSMIC-N e into a physics-based upper atmosphere model • The agreement between hourly forecasted N e and data is better during solar minimum than solar maximum • The assimilation reduces RMSE of N e estimates much more significantly during the high solar activity period • The assimilation of COSMIC-N e into TIE-GCM significantly influences the neutral dynamics of the thermosphere

Measuring the electron density in the ionosphere is an important step to improve our understanding of the solar-terrestrial environment impact on communication, surveillance, and navigation systems. Several methods can be applied to estimate the three-dimensional (3D) electron density in the ionosphere (Bust & Mitchell, 2008). In this context, the computerized ionospheric tomography (CIT) gained attention in the last three decades since it provides accurate observations of the ionospheric electron density over large areas (Austen et al., 1988;Norberg et al., 2018). The main products are 3D spatial fields of the electron density, which evolve with time in a series of snapshots. Over the last 20 years, several achievements have been obtained by CIT techniques. It has been demonstrated that these images can be used to describe the overall ionospheric plasma structure and its temporal evolution in the atmosphere. They have been successfully used to represent important ionospheric dynamics, such as traveling ionospheric disturbances (Bolmgren et al., 2020;Chen et al., 2016), geomagnetic storm signatures

The first systematic comparison between Swarm-C accelerometer-derived thermospheric density and both empirical and physics-based model results using multiple model performance metrics is presented. This comparison is performed at the satellite's high temporal 10-s resolution, which provides a meaningful evaluation of the models' fidelity for orbit prediction and other space weather forecasting applications. The comparison against the physical model is influenced by the specification of the lower atmospheric forcing, the high-latitude ionospheric plasma convection, and solar activity. Some insights into the model response to thermosphere-driving mechanisms are obtained through a machine learning exercise. The results of this analysis show that the short-timescale variations observed by Swarm-C during periods of high solar and geomagnetic activity were better captured by the physics-based model than the empirical models. It is concluded that Swarm-C data agree well with the climatologies inherent within the models and are, therefore, a useful data set for further model validation and scientific research. Plain Language SummaryThe first systematic comparison of the thermospheric densities between Swarm-C accelerometer-derived densities is presented. The data are compared to two latest versions of a physics-based general circulation model and arguably the most widely used empirical models using multiple model performance metrics. The comparison at the satellite's temporal resolution provides a useful evaluation of the models' fidelity for orbit prediction and pertinent space weather forecasting applications. The results of this study show that the short-timescale variations observed by Swarm-C during periods of high solar and geomagnetic activities were better captured by the physics-based model than the empirical model. The results show the complex interconnectedness of solar activity and geomagnetic activity on model performance in terms of estimating density. The study also uses a machine learning exercise to demonstrate characteristics inherent to each model run. Swarm-C data are consistent with the models. It is concluded that Swarm-C data are suitable for further model validation and scientific research.