Rachel Stutz, Orion Space Solutions; Jeff Steward, Orion Space Solutions; Connor Johnstone, Orion Space Solutions; Tyler O’Connell, Orion Space Solutions; Junk Wilson, Orion Space Solutions; John Noto, Orion Space Solutions; Marcin Pilinski, Laboratory for Atmospheric and Space Physics; David Vallado, COMSPOC; Shaylah Mutschler, Space Environment Technologies
Keywords: Dragster, neutral density, data assimilation, ensemble, thermosphere
Abstract:
We present a quantitative comparison of three methods to modeling the neutral density for low earth orbit (LEO) altitudes including the novel data assimilative approach represented by Dragster. The most basic approach uses neutral density model output driven by solar and lower altitude forcing features but otherwise uninformed by observations. This method is used by the Space Weather Prediction Center (SWPC) in the Whole Atmosphere Model (WAM) and is the “free to the public” service from NOAA intended to facilitate safety of flight for conjunction assessment for civilian and commercial spacecraft operators. The second method uses spacecraft positions as a derived observation of neutral density, and then calculates de-biasing coefficients for neutral density to improve performance of orbital propagation calculations in the same time window. This method is used by the US Space Force in the High Accuracy Satellite Drag Model (HASDM) and provides significant performance improvement over the first approach. Finally, we present Dragster 2.0, a unique commercial application to find maximum likelihood, minimum variance estimates of thermospheric neutral density along with quantified uncertainty. It uses satellite ephemerides in an ensemble Kalman filter based architecture running over 1000 members of the background density model and performing assimilation of the derived effective density observations in each member.
Dragster 2.0 features a newly rearchitected framework appropriate for operational use based on Rust and REST API microservices. The ensemble Kalman filter assimilates effective density derived from satellite state vectors with a background neutral density model. This allows calculation of mean and spread statistics from the ensemble distribution of any variables that are part of the model state. These include both corrections on neutral density values and inputs to the background model such as solar forcing terms (F10.7 and Kp/Ap). The final model states and uncertainties are stored in a single PostgreSQL database, allowing for easy querying for both application of Dragster corrections to neutral density models and validation of Dragster’s performance across many test cases. Presented here are results from both a physics-based (TIE-GCM) and an empirical (NRL MSIS) atmospheric model as backgrounds to Dragster, calculating the ensemble spread for both model states. These distributions provide quantified confidence intervals for neutral density values in the LEO region as well as solar forcing terms during the time of model runs. Dragster outputs a grid of multiplicative factors that can be applied as corrections to a background neutral density grid. The improved density estimates and their uncertainties can be used as a background for satellite propagation, leading to improved space domain awareness (SDA). Including these calculated confidence intervals for neutral density in propagation algorithms also has the potential to improve the accuracy of conjunction assessments for space traffic management (STM). The resulting solar forcing terms and their uncertainties can be used in other assimilative forecast schemes to improve modeling and understanding the space environment. In addition, the ability to use the cloud for high performance computing (HPC) is beneficial for research and development as well as profiling in operations. We present our experience with Parallel Works, a vendor who specializes in cloud-based HPC workflows. This platform has enabled our scientists and engineers to scale up and test the deployment of our TIE-GCM container solution on the cloud. This allows us to test the impact of a large number of TIE-GCM ensemble members (1000+) on Dragster performance to determine the optimum cost versus performance. Also presented are preliminary results of visualizing neutral densities from TIE-GCM and MSIS, their Dragster-updated values, and the difference between these density grids in the CesiumJS framework along with concurrent satellite positions and orbits. The results of the three sources of neutral density (WAM, HASDM, and Dragster) are compared for SDA and STM use cases. The outputs of the three orbital propagation calculations are visualized and the results of multiple ensemble members with perturbed solar forcing parameters and background density values are compared. This study informs cost and value trade off decision makers considering each of the three neutral density methods for their SDA and STM requirements. This study should also inform government policy makers considering how to provide public services required for safety of flight while still preserving a competitive commercial market for commercial STM applications that foster innovation. The final goal is a comprehensive system that can help scientists and operational decisions makers understand the “what now” (current neutral density conditions and satellite orbits), “what next” (error-quantified forecast orbits influenced by density corrections), and “what if” (scenario exploration such as investigating solar storms) of the space domain.
Date of Conference: September 17-20, 2024
Track: Atmospherics/Space Weather