Expert system can do more than paint worlds as bowls of soup. Its now assisting scientists obtain better environment change data by teaching Earth observation satellites how to measure ice density in the Arctic year-round.
Satellites have been monitoring the Earths icy north pole for decades now, however the quality of these observations have long been seasonally dependent. In winter season, when the ice is strong, cold, and dry, measurement techniques are reliable and basic. Things get a lot more difficult in the summer, when melted ponds of water form on the icy surface area. From area, these meltwater pools are highly reflective, blinding the satellites instruments, or making the pools appear to be the open ocean. In these conditions, satellites are not able to compare seawater and melting ice.
In a brand-new paper published in Nature last week, scientists describe how they were able to utilize artificial intelligence to overcome these limitations. Their work made it possible for the very first ever year-round ice thickness record from a satellite.
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The satellite in question is the European Space Agencys CryoSat-2, introduced in 2010. CryoSat-2 uses a synthetic aperture radar system that acts as an altimeter: in perfect conditions, it is able to measure its height above the ocean, and above the ice floating on the water.
To fill the understanding gap, scientists utilize a range of buoys, planes, and vessels to keep an eye on ice levels in the summer season, however none of these approaches use the grand-scale coverage a satellite could provide.
To make it possible for CryoSat-2 to continue making helpful observations from May to September, when it would usually be inadequate, the team dealt with previous satellite information from 2011 to 2020, and computer system modeling, to teach the system how to recognize the distinction in between meltwater and open ocean. This maker learning method enabled a total, 12-month ice thickness record, something never ever before attained by a spacecraft.
Pond water forming on sea ice in the Arctic, July 12, 2011. Credit: NASA.
The applications of this new ability are maybe most straight valuable to shipping, making weather condition pattern prediction simpler, and using assistance as to when northern sea passages are most likely to close for the winter season. It will also make these forecasts offered much previously in the season.
In the long term, the information will work to climate scientists intending to understand the procedures associated with year-over-year water level modifications.
Michel Tsamados (University College London) was one of the authors of the paper. He discusses, “when we use the brand-new ice density data in innovative climate models, it will improve both our short-term forecasts for the weather condition at the mid-latitudes and the long-term forecasts that reveal what climate we will have in the future.”
Jack Landy et al. “A year-round satellite sea-ice density record from CryoSat-2.” Nature.
Featured Image: Melting sea ice in the Arctic, photographed from the Alfred Wegener Institutes airborne sea-ice survey IceBird. Credit: Alfred-Wegener-Institute/ Esther Horvath.
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Satellites have actually been keeping an eye on the Earths icy north pole for decades now, but the quality of these observations have long been seasonally dependent. In winter, when the ice is solid, cold, and dry, measurement strategies are simple and effective. From space, these meltwater swimming pools are highly reflective, blinding the satellites instruments, or making the swimming pools appear to be the open ocean. In these conditions, satellites are not able to identify between seawater and melting ice.
CryoSat-2 uses an artificial aperture radar system that acts as an altimeter: in perfect conditions, it is able to determine its height above the ocean, and above the ice drifting on the water.