Neutral densities increase up to 800% during geomagnetic storms. Satellite two-line element sets (TLEs) show increased orbital decay during geomagnetic storms from increased drag

Brandt, Daniel A.

Bussy-Virat, Charles D.

Ridley, Aaron J.

English

DigitalCommons@USU

Daniel A. Brandt1, Charles D. Bussy-Virat2, Aaron J. Ridley3• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Department of Climate and Space Science and Engineering, University of Michigan – Ann Arbor • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •  A TLE-based Algorithm for Correcting Empirical Model Densities during Geomagnetic Stormsbranddan@umich.edu1, cbv@umich.edu2, ridley@umich.edu3INTRODUCTIONWeakness of Empirical Atmospheric Models• Models like NLRMSISE-00 poorly reproduce the storm-time density increase3.• Poor model performance reduces accuracy of orbital propagators (Figure 1) like UofM’s Spacecraft Orbital Characterization Kit (SpOCK)4.Model Correction• High-Accuracy Satellite Drag Model (HASDM) used by USAF to correct Jacchia-19705.• HASDM Dynamics Calibration Atmosphere (DCA) uses Space Surveillance Network data of  >75 orbiting spheres to estimate corrections to F10.7 and ap5.• Doornbos et al. 2008 used TLEs to estimate corrections but assumed TLE-derived densities were sufficient6.• We propose a method that estimates corrections by minimizing orbit error between SpOCK orbits and TLEs.• Develop an algorithm capable of estimating corrections to empirical model densities during geomagnetic storms.• Validate the corrected densities returned by the algorithm in comparison to Level 2 densities measured by the SWARM spacecrafts.• Demonstrate the algorithm’s self-consistency across a wide variety of modeled spacecraft orbits during different storms.• Demonstrate the efficacy of using orbit error minimization to back out corrected densities from empirical atmospheric models.Thermosphere Dynamics• Neutral densities increase up to 800% during geomagnetic storms1.• Satellite two-line element sets (TLEs) show increased orbital decay during geomagnetic storms from increased drag2.Figure 1: SpOCK inaccurately modeling orbital decay of the Columbia CubeSat during a geomagnetic storm.METHODOLOGY RESULTSMultifaceted Optimization Algorithm:Corrects NRLMSISE-00 model densities:1. Area Optimization Algorithm (AROPT):• Loop over preceding quiet time and adjust cross-sectional area until orbit error is minimized.• Obtain the mean of the optimized area distribution (assumes NLRMISISE-00 underpredicts effects from storms)2. F10.7 Optimization Algorithm (FOPT):• Repeat the loop, adjusting F10.7 until orbit error is minimized; retrieve the F10.7 correction for each interval.3. ap Optimization Algorithm (APOPT):• Hold constant the most recent pre-storm F10.7 adjustment.• Loop through the storm, adjusting apuntil orbit error is minimized; retrieve the ap correction for each interval 4. Applying Corrections• Linearly interpolate median corrections across all satellites.• Apply corrections to F10.7 and Ap inputs to NRLMSISE-00 along the orbits of validation spacecraft.• Compare the resulting densities to in-situ measurements.Scenario:• Time: 2017-05-23 and 2017-06-02 • Calibration Targets: 10 Flock 3P satellites • Validation Satellites: SWARM-A and -B. • Density correction allowing for reductions up to 10% error in magnitude, on par with results obtained by Doornbos et al. 2008.• Peak density width improved over NLRMSISE-00.• Corrected densities more accurate than NLRMSISE-00 during initial onset.• Corrected densities slightly too high right after storm main phase.• Must validate the algorithm in other storms of varying intensities and using a variety of other calibration targets.• Update MOA to incorporate F10.7 and ap predictions for density forecasting.1. Kim, K.-H., et al., Atmospheric drag effects on the KOMPSAT-1 satellite during geomagnetic superstorms, Earth Planets Space, 58, e25-28, 2006.2. Bowman, B. R. and Storz, M. F., High accuracy satellite drag model (HASDM) review, Space Environment Technologies – JB2008, AA3 03-625, 2003.3. Picone, J. M.; Hedin, A. E.; Drob, D. P.; Aikin, A. C. (2002-12-01). "NRLMSISE-00 empirical model of the atmosphere: Statistical comparisons and scientific issues". Journal of Geophysical Research: Space Physics, 107 (A12): 14684. Bussy-Virat, C. D., et al., The Spacecraft Orbital Characterization Kit and its Applications to the CYGNSS Mission, 2018 Space Flight Mechanics Meeting, AIAA SciTech Forum, AIAA 2018-1973, 2018.5. Storz, M. F., et al., High accuracy satellite drag model (HASDM), Adv. Sp. Res., 36, 12, pp. 2497-2505, 2005.6. Doornbos et al., Use of two-line element set data for the thermosphere neutral density model calibration, Adv. Sp. Res., 41, pp. 1115-1122, 2008..This research is support by NRL Grant Number N00173-18-1-G006 and a 2019 Michigan Space Grant Consortium Fellowship (Grant Number NNX15AJ20H).Figure 2: The strongly positive correlation between rate of deorbit and geomagnetic activity indicated by Dst for 20 identical Flock 2K CubeSats.OBJECTIVESDISCUSSION AND FUTURE WORKREFERENCESACKNOWLEDGEMENTSFigure 5: A flowchart of the MOA algorithm. FOPT corrections are held constant during the storm, while APOPT corrections vary.Figure 6: A flowchart of the bracketing processes used by AROPT to find the optimized area.Figure 7: AROPT optimizing the area of the Flock 3P 1 satellite.Figure 8: AROPT’s resulting optimized areas for Flock 3P 1 during quiet time.Figure 5: Orbits of the Flock 3P CubeSats (cyan) and of SWARM (red) in the June 2017 storm.Figure 9: Flock 3P 1 optimized areas during quiet time assembled into a histogram.Figure 10: Overlapping histograms of optimized area distributions for all Flock 3P satellites (left) and their corresponding optimized areas over time (right)..Figure 11: Linearly-interpolated F10.7 and Ap corrections for all satellites.Figure 12: Rates of orbit decay for all satellites.Figure 13: Corrected densities along-track SWARM-A.Figure 14: Corrected densities along-track SWARM-B.Figure 4: An image of one of the Flock 3P satellites.

A TLE-based Algorithm for Correcting Empirical Model Densities during Geomagnetic Storms

https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=4686&amp;context=smallsat

A TLE-based Algorithm for Correcting Empirical Model Densities during Geomagnetic Storms

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