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Low Earth orbit (LEO) radio occultation|radio occultations (RO) constellations can provide global electron density profiles (EDPs) to better specify and forecast the ionosphere‐thermosphere (I‐T) system. To inform future RO constellation design, this study uses comprehensive Observing System Simulation Experiments (OSSEs) to assess the ionospheric specification impact of assimilating synthetic EDPs into a coupled I‐T model. These OSSEs use 10 different sets of RO constellation configurations containing 6 or 12 LEO satellites with base orbit parameter combinations of 520 or 800 km altitude, and 24° or 72° inclination. The OSSEs are performed using the Ensemble Adjustment Kalman Filter implemented in the data assimilation (DA) Research Testbed and the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM). A different I‐T model is used for the nature run, the Whole Atmosphere Model‐Ionosphere Plasmasphere Electrodynamics (WAM‐IPE), to simulate the period of interest is the St. Patrick's Day storm on March 13–18, 2015. Errors from models and EDP retrieval are realistically accounted for in this study through distinct I‐T models and by retrieving synthetic EDPs through an extension Abel inversion algorithm. OSSE assessment, using multiple metrics, finds that greater EDP spatial coverage leading to improved specification at altitudes 300 km and above, with the 520 km altitude constellations performing best due to yielding the highest observation counts. A potential performance limit is suggested with two 6‐satellite constellations. Lastly, close examination of Abel inversion error impacts highlights major EDP limitations at altitudes below 200 km and dayside equatorial regions with large horizontal gradients and low electron density magnitudes. 

Abstract The largest obstacle to managing satellites in low Earth orbit (LEO) is accurately forecasting the neutral mass densities that appreciably impact atmospheric drag. Empirical thermospheric models are often used to estimate neutral densities but they struggle to forecast neutral densities during geomagnetic storms when they are highly variable. Physics‐based models are thus increasingly turned to for their ability to describe the dynamical evolution of neutral densities. However, these models require observations to constrain dynamical state variables to be able to forecast mass densities with adequate fidelity. The LEO environment has scarce neutral state observations. Here, we demonstrate, in simulated experiments, a reduction in orbit errors and neutral densities using a physics‐based, data assimilation approach with ionospheric observations. Using a coupled thermosphere‐ionosphere model, the Thermosphere Ionosphere Electrodynamics General Circulation Model, we assimilate Constellation Observing System for Meterology, Ionosphere, and Climate electron density profiles (EDPs) derived from radio occultation (RO) observations. We use the EDPs to directly update neutral states, improving errors for neutral temperature by 70% and neutral winds by 20%. Updated neutral temperature and neutral winds additionally improve helium composition errors by 60% and 40%, respectively. Improved neutral density estimates correspond to a reduction in orbit errors of 1.2 km over 2 days, a 70% reduction over a no‐assimilation control, and a 29 km improvement over 9 days. This study builds on the results of our earlier work to further develop and demonstrate the potential of using a vast and growing RO data source, with a physics‐based model, to overcome our limited number of neutral observations.