11 Dec 2024

 

Launched in May 2024, ESA’s EarthCARE satellite is nearing the end of its commissioning phase with the release of its first data on clouds and aerosols expected early next year. In the meantime, an international team of scientists has found an innovative way of applying artificial intelligence to other satellite data to yield 3D profiles of clouds.

This is particularly news for those eagerly awaiting data from EarthCARE in their quest to advance climate science.

 

Clouds play a critical role in Earth's climate system by reflecting sunlight back into space, known as the albedo effect, and by trapping heat radiating from Earth's surface, part of the greenhouse effect.

For example, high, thin clouds tend to warm the atmosphere because a high proportion of energy from the Sun can pass through and they are also efficient at trapping heat radiating from Earth’s surface. Low, thick clouds on the other hand, tend to have a cooling effect as they reflect a high proportion of the incoming sunlight back out to space.

While scientists know that clouds play an extremely an important role in both cooling and warming our atmosphere, there remains uncertainty when it comes to accounting for the exact influence they have on Earth’s energy balance.

Moreover, given the ongoing climate crisis, there is an urgent need to understand if changes to clouds will exert an overall cooling or warming effect in the future.

Global, realtime 3D cloud data would help reduce these uncertainties, improving climate predictions and helping decision-making.

Over the last decades NASA’s CloudSat mission has provided valuable vertical cloud profiles but was limited by infrequent revisits. Geostationary missions, such as Europe’s Meteosat Second Generation (MSG), on the other hand, take images over Europe every 15 mins, but only obtain a ‘top-down’ view, without directly probing cloud profiles.

Using advanced machine learning techniques, an international team of scientists, coordinated by ESA Φ-lab and FDL Europe, has leveraged advanced machine learning techniques to develop a method for generating ‘3D cloud profiles everywhere, all at once’.

In their proof-of-concept study, they analysed a year’s worth of archived CloudSat and MSG data from 2010. The resulting paper, which was presented this week at the Neural Information Processing Systems conference in Canada, demonstrates how artificial intelligence can extract new insights from existing satellite observations.

Michael Eisinger, from the EarthCARE project team and also from ESA’s Climate and Long-Term Action Division, added, “EarthCARE has already given us some very promising preliminary data and we are expecting great science from this new satellite mission. Our work generating these 3D cloud profiles lays the foundation for exploiting EarthCARE from a different angle.

“These new AI methods promise to maximise EarthCARE’s scientific potential and integrate its data into comprehensive global models that will push the boundaries of climate science.”

Stay tuned for more updates as EarthCARE data is harnessed to refine and expand this pioneering approach.

 

[Image]   

(A) Training AI to generate clouds in 3D

(B) Generating 3D cloud maps

(C) EarthCARE for a better understanding of Earth's radiation balance