Space Sustainability: artificial intelligence for atmospheric modelling

Atmospheric density is difficult to forecast, which makes the movement of space debris difficult to predict and endangers operational satellites and atmospheric re-entry of spacecraft. The Space Sustainability team, a finalist of our Competition Best Idea 2021, is using AI to better model the dynamics of the thermospheric density field and help tackle congestion in space.
13th October 2021
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The case for a Sustainable Development Goal for Space

Space-based services and technologies have an important role in supporting the achievement of all 17 of the United Nations Sustainable Development Goals (SDG). These range from the use of Earth Observation satellites for providing data on sea level rise, air quality and land use, vital for addressing the problems incorporated in the SDGs on climate action, life on land and below water (SDGs 13,14,15), hunger, poverty and sanitation (SDGs 1,2,6); to the use of commercial satellite constellations for providing telecommunication services and global internet access for economic growth, industry, innovation, and infrastructure (SDGs 8,9).

Considering an increased reliance on space as a platform for ensuring sustainable development on Earth, and with a growing need of more resilient space traffic management, there have been increasing calls to include an independent SDG for the sustainable use of outer space.

Atmospheric density is difficult to forecast

The dynamics of objects in low Earth orbit, which includes vital satellites and the International Space Station, are strongly determined by the effects of atmospheric drag (imagine running against a high wind and feeling the drag pushing you back in the direction). However, the density of the atmosphere is difficult to model, being highly dependent on both the altitude and the interactions with space weather.

Inaccurate predictions of atmospheric density can lead to orbit differences of multiple kilometers over just a few days, making drag modelling one of the largest limiting factors in collision and re-entry prediction. Therefore, it is important to understand and reduce these uncertainties atmospheric drag to support space traffic management.

Student-led team proposes AI as a tool to improve atmospheric density prediction

The Space Sustainability team, a finalist of our Competition Best Idea 2021, seeks to contribute towards ‘SDG 18’, by using AI to better predict atmospheric density and help tackle congestion in space. This will provide more accurate data on the movement of space debris, which is a major threat to space assets and infrastructures in objects in low Earth orbit.

The student-led team is using AI to apply a state-of-the-art forecasting model that can improve the prediction of space weather. This can help reduce the risk to operational satellites and atmospheric re-entry of spacecraft.

About the team

The Space Sustainability team is a multi-disciplinary and multi-national research team consisting of students (both Master’s and PhD) and staff (both early stage researchers and senior academic staff) who belong to four different universities across Europe (of which three are Members of CESAER). The academic experts in this project have strong connections to the space industry and the integration of expertise from mechanical, aerospace, information systems, computer science and space law departments is extremely beneficial to explore the effects of atmospheric density on space operations and sustainability.

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