Are Satellites In The Thermosphere

Satellites play important roles in our daily lives, providing navigation, data, and communications solutions, as well equally World observations to monitor weather, climate, and natural resources. All of this information is vital for policymakers, businesses, and consumers. However, increasing demand for the services that satellites provide has also created an increasingly crowded environment in the low–Earth orbit (LEO) region where many of these satellites operate. Unlike automobiles on crowded city streets, satellites lack onboard drivers who can steer around obstacles at a moment's notice. To avoid collisions and plan evasive maneuvers, satellite operators predict orbits and business relationship for accurately known gravitational forces; they must too business relationship for trajectory changes brought about by atmospheric drag on the craft, a far more difficult task.

The potential add-on of tens of thousands of objects to low World orbit will escalate the take a chance of catastrophic, and cascading, collisions.

Approximately 1,800 agile satellites currently operate beneath 1,000 kilometers in altitude [Matrimony of Concerned Scientists, 2005], where air resistance, or drag, is large enough to significantly bear on satellite orbital trajectories. These agile spacecraft share this region with more than than 10,000 inert satellites and pieces of debris.

The structure of very large constellations of commercial LEO satellites began in about 2018 when the private company SpaceX launched its first Starlink satellite prototypes; other companies (e.chiliad., OneWeb, Amazon, Telesat) have followed suit or are preparing their own constellations. Calculation to the congestion is a speedily increasing number of low-cost small satellites, which tin can now be built using largely off-the-shelf components. The potential addition of tens of thousands of objects to LEO will escalate the risk of catastrophic, and cascading, collisions. The resulting exponential increase in orbital debris could make LEO unviable [Kessler et al., 2010], and crossing to higher orbits could get perilous.

In LEO, atmospheric drag is past far the dominant source of error associated with orbit propagation (numerical modeling to predict a satellite's future position and velocity), and it plays a defining role in satellite mission planning, orbit and reentry prediction, and collision avoidance. Accurately tracking and predicting the locations of objects in infinite is of paramount importance to assessing collision gamble, which determines whether executing avoidance maneuvers is necessary. Thus, the projected massive increment in the number of orbiting spacecraft in the near hereafter is driving an increasingly critical need for more than accurate satellite elevate modeling and forecasting.

Quality Models Crave Quality Input

The greatest limitation to improving thermosphere models is the inconsistent quality and sparse distribution of upper atmosphere observations.

The accuracy of orbit prediction relies on the quality of the atmospheric drag forcefulness models and the forecasts they produce. Satellite characteristics (e.1000., size and geometry) influence atmospheric drag, but drag by and large depends on the very depression density of the highly variable upper atmosphere, called the thermosphere. Realizing significant advances in orbit prediction volition require more than authentic specification and forecasting of this infinite environs. The greatest limitation to improving thermosphere models is the inconsistent quality and sparse distribution of upper temper observations.

Uncertainties in atmospheric drag modeling are largely associated with variability of the density of the neutral (as opposed to charged) atoms and molecules in the thermosphere. This variability is driven by changing solar extreme ultraviolet emissions (referred to as solar action), by interactions of the magnetosphere with the solar current of air (referred to as geomagnetic activity), and by upwardly propagating meteorological perturbations like gravity waves and tides that originate at lower altitudes in Earth's atmosphere.

Data about these driving sources is required to feed both empirical and physics-based models of the upper atmosphere, which in plough are used (separately) to calculate satellite drag. Despite progress made over the by couple of decades, large uncertainties still be in estimates of the solar, magnetospheric, and gravity wave free energy input to—and thus in how this energy affects—the thermosphere [e.g., Siscoe et al., 2004; Palmroth et al., 2005; Peterson et al., 2012; Oberheide et al., 2015; Becker and Vadas, 2020].

As the scientific community focuses on improving measurements of the magnitude, spatial distribution, and temporal development of these drivers, efforts are nether way to advance modeling of thermospheric variability with the development and testing of data absorption schemes that combine models and almost-real-fourth dimension observations [eastward.one thousand., Codrescu et al., 2018; Sutton, 2018; Pilinski et al., 2016]. Data assimilation methods have been used in terrestrial conditions analyses and forecasts for decades to meliorate specify meteorological initial conditions in models.

Sparse Data from the Thermosphere

Data absorption methods crave a steady stream of observations with which to update and refine model forecasts. The chief obstruction in data assimilation efforts for thermosphere specification is the scarcity of high-quality measurements of density, temperature, and limerick. Later a hiatus of more fifteen years when practically no data were collected, the distribution of density data since observations started again in 2000 has still been rather sparse (Figure 1). Although these data accept facilitated new enquiry investigating the upper atmosphere, that contribution will stagnate without adequate follow-up information collection missions.

Figure showing distribution of accelerometer density observations of geomagnetic and solar activity by altitude and observation duration — Fig. ane. This stacked histogram shows the distribution of accelerometer density observations of geomagnetic and solar activity past altitude and full observation duration from 2000 to 2018. Observations are from the Challenging Minisatellite Payload (Champ), Gravity Recovery and Climate Experiment (GRACE), Gravity Field and Steady-State Body of water Circulation Explorer (GOCE), and Swarm satellites. CHAMP and GRACE data were obtained from a repository at Delft University of Technology; GOCE and Swarm data were obtained from the European Space Agency'due south Earth Online platform.

This information is even more important for the evolution of operational models constrained by data assimilation. Data absorption and subsequent model verification with independent observations are, past definition, not possible without current information. Sustained, long-term global observations of such key variables as temperature, wind, and the chemical composition in the thermosphere are essential for achieving a improve understanding of its complex dynamics and chemistry, for evaluating and improving models, and for developing robust forecasting capabilities.

Filling the Information Gaps

Loftier-resolution measurements of air density take been inferred from accelerometer information since 2000. These data were collected by the High german Gnaw (Challenging Minisatellite Payload) satellite and and then by NASA and Deutsches Zentrum für Luft- und Raumfahrt's GRACE (Gravity Recovery and Climate Experiment) satellite and the European Space Agency'due south GOCE (Gravity Field and Steady-Country Ocean Apportionment Explorer) and Swarm satellites. Except for Swarm, atmospheric density monitoring was not a mission objective, so these valuable density data sets we currently take are information of opportunity. The information sets made relatively detailed verification of thermosphere models possible for the commencement time, which in turn contributed significantly to the models' improvement.

Figure 1 shows that we have few measurements of density nether high and very high solar activity conditions. We also have very few measurements from days when geomagnetic storm conditions were moderate to extreme because of the relative rareness of these short-duration (one–3 days, typically) storm events. Fewer than 10 extreme geomagnetic storms have been measured with accelerometers since 2000, and it is vital that we maintain and raise monitoring capability now and in the future to augment our thin database.

The temperature and composition of the lower thermosphere directly and profoundly touch the unabridged low–Earth orbit surround, yet the processes by which they exercise and so are poorly constrained.

Figure two displays how thin the density data distribution is for fifty-fifty the best-observed storm in the database. At the lowest altitudes, below almost 250 kilometers, there are no records in GOCE data of air densities nether weather condition of very high solar activity and simply a few under high solar activity, and these information provide very express local solar time coverage because they were collected in only the half dozen:00–viii:00 a.m. and 6:00–viii:00 p.m. sectors (dawn–dusk).

Another major obstruction to predicting drag on satellites in LEO is the scarcity of temperature, density, and chemical composition measurements in the lower thermosphere, betwixt 100 and 200 kilometers in altitude. In this region, which could exist called the "ignorosphere" given the lack of observations, the atmosphere transitions from existence a homogeneous mixture consisting primarily of molecular nitrogen to a diffusively separated gas mixture dominated past atomic oxygen. The temperature and limerick of the lower thermosphere directly and profoundly affect the entire LEO environment, withal the processes by which they do so are poorly constrained in models or by observations, fifty-fifty as seasonal averages [Emmert et al., 2020]. This is also the region where geomagnetic activity injects massive amounts of energy, some other poorly constrained variable in models, into the atmosphere and drives global thermospheric variations during tempest times.

Figure showing distribution of accelerometer density data with respect to altitude, local time, and measurement duration — Fig. 2. The distribution of density data with respect to altitude, local time, and measurement duration (variable shading of colors) collected by three satellites during a well-observed geomagnetic storm on 5 April 2010 is shown hither. (i kilometer × 1 minute bins). Data sources are the same as in Figure ane.

To achieve necessary progress in upper temper modeling that enables authentic elevate predictions and space traffic direction in an increasingly crowded infinite environment, sustained observations of the thermosphere are much needed. Ideally, an international observation system, along the lines of the World Meteorological Organization (WMO) for conditions forecasts, should exist mounted to coordinate efforts globally. WMO serves equally a good case because the organization has promoted gratis and unrestricted exchange of data since 1873, and this organization has created a global standardized network to support conditions services.

This endeavour should be complemented by scientific discipline missions focusing on specific regions like the lower thermosphere-ionosphere (due east.g., the Daedalus mission) or on topics like the changing period of solar energy into the magnetosphere (e.g., the Dione mission).

Acknowledgments

We thank thermosphere modelers John Emmert (U.S. Naval Research Laboratory, Washington, D.C.) and Eric Sutton (Space Conditions Technology, Inquiry, and Education Center, University of Colorado Boulder) for their insight and contributions to this article.

References

Becker, E., and S. Fifty. Vadas (2020), Explicit global simulation of gravity waves in the thermosphere, J. Geophys. Res. Space Phys., 125, e2020JA028034, https://doi.org/10.1029/2020JA028034.

Codrescu, S. M., Yard. 5. Codrescu, and M. Fedrizzi (2018), An ensemble Kalman filter for the thermosphere-ionosphere, Space Weather, sixteen, 57–68, https://doi.org/ten.1002/2017SW001752.

Emmert, J. T., et al. (2020), NRLMSIS ii.0: A whole-temper empirical model of temperature and neutral species densities, World Infinite Sci., seven, e2020EA001321, https://doi.org/10.1029/2020EA001321.

Kessler, D. J., et al. (2010), The Kessler syndrome: Implications to futurity space operations, Adv. Astron. Sci., 137, 47–62.

Oberheide, J., et al. (2015), The geospace response to variable inputs from the lower atmosphere: A review of the progress fabricated by Task Group four of CAWSES-2, Prog. Globe Planet. Sci., 2, 2, https://doi.org/x.1186/s40645-014-0031-4.

Palmroth, M., et al. (2005), Assessment of ionospheric Joule heating by GUMICS-4 MHD simulation, AMIE, and satellite-based statistics: Towards a synthesis, Ann. Geophys., 23(6), ii,051–2,068, https://doi.org/10.5194/angeo-23-2051-2005.

Peterson, W. K., et al. (2012), Solar EUV and XUV energy input to thermosphere on solar rotation time scales derived from photoelectron observations, J. Geophys. Res., 117, A05320, https://doi.org/ten.1029/2011JA017382.

Pilinski, M. D., et al. (2016), Improved orbit determination and forecasts with an assimilative tool for satellite drag specification, paper presented at 50th Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, Hawaii, 20–23 Sept., amostech.com/TechnicalPapers/2016/Affiche/Pilinski.pdf.

Siscoe, G., J. Raeder, and A. J. Ridley (2004), Transpolar potential saturation models compared, J. Geophys. Res., 109, A09203, https://doi.org/ten.1029/2003JA010318.

Sutton, East. Thou. (2018), A new method of physics-based information assimilation for the quiet and disturbed thermosphere, Space Weather, sixteen, 736–753, https://doi.org/10.1002/2017SW001785.

Union of Concerned Scientists (2005), UCS Satellite Database, www.ucsusa.org/resources/satellite-database. [Updated 1 April 2020.]

Author Data

Sean Bruinsma (sean.bruinsma@cnes.fr), Space Geodesy Office, CNES, Toulouse, France; Mariangel Fedrizzi, Cooperative Constitute for Research in Environmental Sciences, University of Colorado Bedrock; likewise at Space Weather Prediction Center, NOAA, Boulder, Colo.; Jia Yue, NASA Goddard Space Flight Center, Greenbelt, Md.; Christian Siemes, Delft Academy of Technology, Delft, Netherlands; and Stijn Lemmens, European Space Operations Centre, Darmstadt, Germany

Citation:

Bruinsma, South.,Fedrizzi, Chiliad.,Yue, J.,Siemes, C., and Lemmens, Southward. (2021), Charting satellite courses in a crowded thermosphere, Eos, 102, https://doi.org/x.1029/2021EO153475. Published on nineteen January 2021.

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