Assimilating Satellite Orbit Data to Improve Neutral Density Forecasts for Operational Use
Abstract
Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near-Earth space environment. Drastic changes in the upper atmosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to increased position uncertainties, larger covariance ellipses, and errors when predicting collisions in space.
In this paper, we describe ongoing work to validate a commercial nowcast and forecast system, called Dragster, for specifying the neutral atmospheric state related to orbital drag conditions. Dragster is based on ensemble Kalman filtering with both global circulation models (GCM's) as well as empirical models of the thermosphere. The filter is capable of running in real-time and uses assimilative techniques to produce a satellite-drag nowcast. This software also produces 72-hour predictions of the global satellite-drag conditions. Testing of the Dragster software is performed by assimilating publicly available orbital data (two-line elements or TLE's also known as general perturbations orbital analysis) but upgrades are underway to assimilate information from special-perturbations orbit analysis of satellite observations. In this paper, we summarize the model design and assimilative architecture, and present validation results using both drag (TLE and accelerometer data) and remote-sensing observations, and demonstrate assimilation performance with NRLMSIS-00 and a GCM. Validation results will be compared to the performance of non-assimilative models including TIE-GCM, NRLMSISE-00, JB-08, and HASDM. Validation studies are always performed by comparing observations which were not used as part of the assimilation-dataset.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMSA33B3476P
- Keywords:
-
- 0355 Thermosphere: composition and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 3315 Data assimilation;
- ATMOSPHERIC PROCESSESDE: 2447 Modeling and forecasting;
- IONOSPHEREDE: 2736 Magnetosphere/ionosphere interactions;
- MAGNETOSPHERIC PHYSICS