(ORDO NEWS) — Data from European SWARM satellites was used to train artificial intelligence to predict atmospheric plasma bubbles
Swarm is a European Space Agency mission to study and map the Earth‘s magnetic field. The mission consists of three identical satellites placed in low polar orbits.
Shortly after sunrise, pockets of superheated gas form high in the atmosphere in the tropics, called equatorial plasma bubbles (EPBs).
At first, they are the size of a football field, but in a few hours they can swell to the size of several hundred kilometers.
These bubbles are a serious problem as they can interfere with radio signals, especially when it comes to communicating with satellites.
It would be very useful if we could predict the appearance of large bubbles, like rain, for example. Sachin Reddy of University College London in the UK and his collaborators have developed artificial intelligence for this purpose.
They trained it on data collected by the mission of three European SWARM satellites over 8 years of operation.
Superheated bubbles in the atmosphere
These satellites operate at an altitude of about 460 kilometers, which is approximately the average height of large plasma bubbles.
The equipment of the SWARM mission also includes an automatic detector of these bubbles – the index of ionospheric bubbles.
The device analyzes the electron density and magnetic field strength to determine if the satellite is passing through the bubble.
The results so far show that the occurrence of plasma bubbles varies throughout the year, much like the weather. Most of them appear in the region of the South Atlantic anomaly of the Earth’s inner radiation belt.
Solar activity has a significant effect on the appearance and behavior of plasma bubbles. The stronger it is, the more bubbles appear and the larger they are.
“Like weather forecasts, we need forecasts of equatorial plasma bubbles. With their help, we could solve problems with satellite communications, ”assumes Reddy.
According to him, such a forecast could look something like this: “Tomorrow at eight o’clock in the evening there will be a 30 percent probability of a plasma bubble in the Horn of Africa.” Such information can be very useful for satellite operators as well as for users dependent on satellite data.
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