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Reducing the risk of collisions in space through better thermosphere modelling

There’s a lot going on in our thermosphere, from solar storms to the launch of an ever-increasing number of satellites. Operators need accurate projections of orbits and of the highly variable air drag if they are to avoid collisions.

Space

Our upper atmosphere, or thermosphere, is very tenuous. Even if mass densities are more than a billion times smaller than at the surface of our planet, predicting drag due to air resistance is particularly important for satellite operators. In low Earth orbit, air drag is the dominant error source, playing a central role in satellite mission planning, trajectory, and re-entry prediction, and planning evasive manoeuvres to avoid collisions. The International Space Station is manoeuvred about once a year for just that reason. Sean Bruinsma, based at the Toulouse Space Centre (CNES) in France, is in charge of outreach for the EU-supported SWAMI project. He explains: “The main problem today is collision avoidance, and it will be an even greater problem tomorrow as very large constellations of satellites, such as Starlink, are currently being constructed. It’s clear that the sheer number of objects in orbit will lead to greater collision risks.” An accurate density model is required along with reliable forecasts of solar and geomagnetic activity to predict collision probabilities. The only European thermosphere model, Drag Temperature Model (DTM), has a 120 km altitude boundary, which made it necessary to rely on American models for re-entry computations below that altitude. “Now, through a combination of monthly-mean digital tables of the Unified Model computed by the United Kingdom’s Met Office, and an updated DTM, we have an atmosphere model up to about 1 500 km. This model, MOWA Climatological Model (MCM), can be used for satellite operations, including re-entry computations,” Bruinsma says. “The MCM is a significant step forward for Europe’s independence in space operations although the COVID-19 crisis has meant we are not as accurate in the re-entry zone as we had intended.”

Calculating the effect of solar wind

The SWAMI project also created the new, geomagnetic index Hpo, a proxy for the energy contributed to the upper atmosphere by interaction with the solar wind. The Hpo employs shorter sampling intervals, 30 and 60 minutes in comparison to the 180 minutes of the previously used index Kp, and can better represent geomagnetic storms. “The higher temporal resolution of Hpo makes a more accurate modelling of the variability in the thermosphere possible. “In satellite operations, the orbits of objects have to be predicted for days to several weeks out. This necessitates forecasts of solar activity and geomagnetic activity for the same period,” notes Bruinsma. To achieve this, the project used machine learning, and applied a special algorithm to optimise accuracy during geomagnetic storms.

Tools for analysis

The MCM and DTM models are available on Github and are free for non-commercial use, and under licence otherwise. These models are the operational tools created by the SWAMI team to be implemented in a user’s own software. Instructions for implementation and benchmarks are given on the SWAMI website and on Github.

Keywords

SWAMI, space, weather, climate, solar, atmosphere, satellites, orbit, thermosphere

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