Error Assessment of Thermospheric Mass Density Retrieval With POD Products Using Different Strategies During Solar Minimum

With the proliferation of low Earth orbit (LEO) satellites carrying GNSS receivers on‐board commercial operators such as Spire, Starlink, OneWeb, and Amazon, an abundance of high‐cadence tracking data could become available to the scientific community. While GNSS measurements from geodetic‐grade receivers on satellites like SWARM, CHAMP, GRACE, and GOCE have been extensively used for atmospheric density retrieval, limited research has explored the potential of less accurate data from commercial operators. This study focuses on two methods to estimate atmospheric densities from precision orbit determination (POD) products—precise positions and velocities—utilizing synthetic data sets. The first method, termed “POD accelerometry” treats the POD products as measurements to a reduced‐dynamic POD scheme with the goal of estimating densities using stochastic parameters. The second method known as the energy dissipation rate (EDR) approach derives densities from changes in orbital energy. The relative contributions of various error sources—dynamics model uncertainties, and POD noise—to the estimated densities are studied for a limited set of orbital regimes and space weather activity, and possible error mitigation strategies are suggested. The performance of the two methods and their sensitivities to these various error sources are compared for circular orbits in the altitude regime 300–800 km during solar minimum F10.7=72.5 $\left({F}_{10.7}=72.5\right)$. EDR and POD accelerometry have comparable performances for high drag, low POD noise environments, whereas the latter performs considerably better in low drag 25 ${ >} 25$ cm) environments, with densities retrieved at higher cadences for the orbital regimes considered in this work during solar minimum. Plain Language Summary Low Earth orbit (LEO) Satellites orbit the Earth within its tenuous upper atmosphere. The uncertainties in the modeling of the atmospheric density lead to considerable errors in the prediction of satellite orbits that can have drastic consequences in the increasingly overcrowded LEO regime. Therefore, real‐time estimates of the upper atmospheric density are very beneficial in improving the predictive capability of atmospheric models. This research focuses on using tracking data from satellites equipped with GNSS receivers to estimate atmospheric densities in LEO. Specifically, the study analyzes two