Assimilating Space‐Based Thermospheric Neutral Density (TND) Data Into the TIE‐GCM Coupled Model During Periods With Low and High Solar Activity

The global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space‐based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space‐based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm‐C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm‐B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE‐GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively. Plain Language Summary The atmosphere has different layers, like the thermosphere and ionosphere, which are important for satellite orbit prediction and communication. The empirical or physics‐based models can be used to understand what's happening in these layers, but they aren't always accurate. In this study, the neutral density estimates along low earth orbit satellites have been integrated with the physics‐based Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM) through the Ensemble Kalman Filter (EnKF) Data Assimilation (DA) method. We found that using this data can help us make better predictions about the thermosphere and ionosphere variables. Our technique could be useful for predicting changes in the atmosphere in the short‐term, which could be important for communication and navigation. Key Points Investigating the impact of covariance localization on the assimilation of Thermospheric Neutral Density data into TIE‐GCM Assimilation the of TND data into TIE‐GCM considerably improves the electro