Mars is one of the most explored components of the solar system, yet there are always more discoveries to unveil on Earth's planetary neighbor. On Earth we are able to take direct measurements to understand our planet's meteorological activities, but on Mars scientists must use evidence in the landscape to discern this information instead.
Dr. Lior Rubanenko, Assistant Professor at the Technion—Israel Institute of Technology, and colleagues used machine learning to characterize Mars' wind patterns based upon the morphology of more than 700,000 barchan dunes. This data was obtained from images taken by a specialized camera on the Mars Reconnaissance Orbiter, a spacecraft that has been orbiting the planet since 2006 to collect information on its geology and climate.
The research team found a distinct pattern arising in dune migration resulting from summer atmospheric circulation patterns, these being northwards directed at mid-latitudes and cyclonic near the north pole. The latter is also broken into a smaller component experiencing the opposite anticyclonic wind direction, which the authors specifically attribute to the effects of winds moving across the polar ice cap.
One limitation of the machine learning project however is that it does not fully consider the complexities of changing wind regimes between day and night and across seasons, but instead focuses on longer-term patterns. It also struggles in areas with significant topographic changes, such as the larger impact craters of Valles Marineris, Hellas and Argyre, where dunes are dispersed across a larger area.fields, which form closer to the center of the crater basin the deeper they are.
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