WASHINGTON: Meteorologists sometimes struggle to accurately predict the weather here on Earth, but now we can find out how cloudy it is on planets outside our solar system, thanks to researchers at MIT. Researchers in the Department of Earth, Atmospheric, and Planetary Sciences (EAPS) at MIT describe a technique that analyzes data from NASA’s Kepler space observatory to determine the types of clouds on planets that orbit other stars.
The team, led by Kerri Cahoy, an assistant professor of aeronautics and astronautics at MIT, has already used the method to determine the properties of clouds on the exoplanet Kepler-7b. The planet is known as a “hot Jupiter,” as temperatures in its atmosphere hover at around 1,700 kelvins.
NASA’s Kepler spacecraft was designed to search for Earth-like planets orbiting other stars. It was pointed at a fixed patch of space, constantly monitoring the brightness of 145,000 stars. An orbiting exoplanet crossing in front of one of these stars causes a temporary dimming of this brightness, allowing researchers to detect its presence.
Researchers have previously shown that by studying the variations in the amount of light coming from these star systems as a planet transits, or crosses in front or behind them, they can detect the presence of clouds in that planet’s atmosphere. That is because particles within the clouds will scatter different wavelengths of light.
To find out if this data could be used to determine the composition of these clouds, the MIT researchers studied the light signal from Kepler-7b. They used models of the temperature and pressure of the planet’s atmosphere to determine how different types of clouds would form within it, says lead author Matthew Webber, a graduate student in Cahoy’s group at MIT.
“We then used those cloud models to determine how light would reflect off the atmosphere of the planet [for each type of cloud], and tried to match these possibilities to the actual observations from the Kepler mission itself,” Webber says. “So we ran a large set of models, to see which models fit best statistically to the observations.”
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