While flagship missions such as CHAMP and GOCE have shown us with accelerometer measurements that the thermospheric density in Low Earth Orbit (LEO) can increase by more than 200% during enhanced geomagnetic activity, current empirical models, such as those of the MSISE and Jacchia families, as well as the Drag Temperature Model, fail to reproduce this behavior, limiting the ability to perform orbit prediction and space situational awareness. Several methods have been employed to address this dilemma. One is the High-Accuracy Satellite Drag Model (HASDM), which uses its Dynamic Calibration Atmosphere to employ differential correction across 75 spherical calibration satellites to generate correction parameters to the density that are related to 10.7 cm solar radio flux and ap (Storz et al. 2005). Doornbos et al. 2008 has implemented a method that estimates height-dependent scale factors to the densities from empirical models with respect to densities directly derived from two- line element sets (TLEs). HASDM’s reliance on Space Surveillance Network observations limit its accessibility and detail, and Doornbos’ methods are limited by the fact that TLEs are mean elements; densities derived from them are subject to errors due to smoothing over an entire orbit. In addition, the method of deriving densities from TLEs was initially done only to provide inputs to the SGP4 orbital propagator, which was initially developed without consideration of solar radiation pressure on the trajectory of modeled spacecraft. We present a method to generate new model densities during geomagnetic storms by using an in-house orbital propagator, the Spacecraft Orbital Characterization Kit (SpOCK). This method estimates and applies scale factors to F10.7 and a p to minimize orbit propagation errors with TLEs. The method is tested on a variety of satellites, including CHAMP, GOCE, and the CubeSats of the QB50 and FLOCK constellations. This method proposes to grant insight into storm-time thermospheric density enhancement by modeling the effects of storms on the drag of numerous LEO spacecraft, increasing our understanding of thermospheric dynamics and granting us improved tools for space traffic management and thermospheric research.

Empirical models of the thermospheric neutral density are routinely used by mission planners and systems engineers to perform orbit maintenance, collision avoidance, and estimate time and location of re-entry for spacecraft. These models have characteristic errors in neutral density below 10% during geomagnetic quiet time, but perform worse during intense geomagnetic activity, being unable to reproduce the significant increases in the neutral density that are observed during geomagnetic storms. Underestimation of the density during these conditions translates to errors in orbit propagation that reduce the accuracy of any resulting orbit predictions. These drawbacks directly translate into safety risks for astronauts and orbiting spacecraft, but also limit our understanding of the physics of neutral density enhancements. Numerous CubeSats with publicly available ephemeris in the form of two-line element (TLEs) sets orbit in this region. We present the Multifaceted Optimization Algorithm (MOA), a method to estimate the neutral density by minimizing the error between a modeled trajectory and a set of TLEs. Specifically, the algorithm estimates corrections to the inputs of the NRLMSISE-00 empirical density model, and applies those corrections along-track the SWARM spacecraft orbits. This results in orbit-averaged empirical densities below 10% error in magnitude, compared to errors in excess of 25\% for uncalibrated NLRMSISE-00.