Pol Mesalles-Ripoll, SpaceNav; Zachary Waldron, SpaceNav; Alex Sanchez, SpaceNav; Roman Rositani, SpaceNav; Matt Duncan, SpaceNav
Keywords: drag, space weather, space situational awareness, atmosphere, density, model, orbit determination, orbit prediction
Abstract:
As low-Earth orbit (LEO) becomes increasingly crowded, the risk of collision continues to grow. Effective mitigation of this risk requires more reliable and accurate predicted trajectories of resident space objects, which in turn requires improved specification and forecasting capabilities of the Earth space environment via upper atmospheric models. The U.S. Space Force uses its own dynamically-calibrated High-Accuracy Satellite Drag Model (HASDM) for orbit predictions and conjunction assessment, but the real-time and predicted densities from HASDM are not publicly available. For satellite owner/operators generating orbit predictions, there are several semi-empirical models such as the MSIS, DTM, and Jacchia-based series of models. These are often computationally fast and accurate for climatological uses, but their ability to accurately project into the future is closely tied to the fidelity of their drivers (i.e., space weather indices such as the F10.7 solar flux and the Kp and Ap geomagnetic activity) and similarly limited by their lower temporal and spatial resolutions. Physics-based models offer greater potential for forecasting but lack the accuracy of semi-empirical models in near real-time scenarios. With no clear consensus on which atmosphere model performs best for which scenario, in this paper we assess the accuracy of each model’s predictive capabilities using their respective drivers for a variety of spacecraft operating in different altitude regimes and inclinations: NASA’s GPM (~430 km, 65°), JPL’s OCO-2 (~700 km, 98°), and PlanetiQ’s GNOMES-4 and GNOMES-5 (~550 and ~580 km, 97°).
We focus our analysis on the following models: NRLMSISE-00, NRLMSIS 2.1, JB2008, DMT2020, and WAM-IPE.
The Naval Research Laboratory (NRL) Mass Spectrometer and Incoherent Scatter (MSIS) model series are empirical models built up from statistical fits to climatological data from satellites, rockets, and radars across several decades of data. NRLMSIS 2.1 is the newest release in the series with earlier versions including NRLMSISE‐00, which is one of the most widely used atmospheric models in LEO operations. MSIS models provide a climatological average of observed behavior for temperature, constituent densities, and neutral mass density from the ground into the lower exosphere to an altitude of ∼1400 km. MSIS models are driven by the F10.7 and Ap indices.
Jacchia–Bowman 2008 (JB2008) is a semi-empirical thermospheric density model developed as an improved revision of JB2006, which itself uses the earlier developed Jacchia 1970 as the basis for the diffusion equations. JB2008 is built up from satellite drag, densities derived from orbital decay data, and radar data from 1978 through 2007. The effects of solar activity are driven by the F10.7, S10.7, M10.7, and Y10.7 indices, while geomagnetic activity is captured via the Ap index during quiet times and the Dst index during storm-time.
The semi-empirical Drag Temperature Model 2020 (DTM2020) was developed within the H2020 Space Weather Atmosphere Models and Indices (SWAMI) project. DTM2020 is built up from empirical data from optical spectrometers, satellite drag, accelerometers, and energy dissipation rate-inferred densities from 1969 through 2019. DTM2020 has two modes: operational (driven by F10.7 and Kp indices) and research (driven by F30 and hourly Hpo indices).
WAM-IPE is a physics-based model developed by NOAA’s Space Weather Prediction Center (SWPC) which couples two components: the Whole Atmosphere Model (WAM) and Ionosphere-Plasmasphere-Electrodynamics model (IPE). WAM is a first-principles upper atmospheric general circulation model extending from Earth’s surface to approximately 600 km. IPE is is a physics-based ionosphere and plasmasphere model extending up to approximately 10000 km. WAM-IPE’s solar activity is driven by the solar EUV irradiance model for aeronomic calculations (EUVAC), which is driven by F10.7. The Weimer empirical model is used to calculate the high-latitude electric field as driven by solar wind data at 1 AU.
For this study, we build on top of SpaceNav’s previously presented framework for predictive orbit accuracy evaluations. By processing GNSS tracking data for each satellite through our orbit determination (OD) pipeline over the span of multiple months—where both calm and storm conditions were observed—we generate a large dataset of definitive ephemeris data. Using the last definitive state at the end of each OD arc, we propagate predictive ephemerides using the solved-for drag coefficient (CD) and the most up-to-date space weather predictions available at the time (using data from the Canadian Penticton observatory, NOAA SWPC, the U.S. Air Force, and Space Environment Technologies).
Differencing the predictive orbits against the reference definitive trajectory, we generate a set of position differences (ephemeris overlaps) for the analysis period. These differences are presented in relative time from the epoch of each OD and can be aggregated together to obtain cumulative distribution functions of the position error at each propagation time (e.g., 6, 12, 24, 48, and up to 72 h, representing the time range where operators typically commit to risk mitigation maneuvers). Statistics on the prediction errors are generated and analyzed both in terms of the norm of the position differences as well as the componentwise differences in a satellite-centric frame like RIC.
Finally, we review the contribution to the errors caused by space weather forecasts alone by propagating the orbits with both predictive and definitive space weather drivers. Previous work has shown that errors in the F10.7 are one of the largest contributors to orbit prediction accuracy; comparing the position difference statistics for each model using the two space weather datasets, we want to revisit these results with what is now an expanded set of atmosphere models.
Date of Conference: September 16-19, 2025
Track: Atmospherics/Space Weather