Revisiting Atmospheric Features of Mars Orbiter Laser Altimeter Data Using Machine Learning Algorithms. Issue 1 (21st January 2023)

• Record Type: Journal Article
• Authors: Caillé, Vincent; Määttänen, Anni; Spiga, Aymeric; Falletti, Lola
• Is Part Of: Journal of geophysical research. Volume 128:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-21
• Subjects: Mars -- MOLA -- atmosphere -- clouds; Planets -- Periodicals; Geophysics -- Periodicals; 559.9
• DOI: 10.1029/2022JE007384
• Languages: English
• ISSNs: 2169-9097

Abstract

Abstract: The Mars Orbiter Laser Altimeter (MOLA) instrument has been drawing a map of Mars' topography between September 1997 and June 2001. It has also been able to observe clouds during the mission duration, providing data for the low Martian atmosphere for nearly 1.5 Mars years. The Mars Global Surveyor, which carried MOLA, also carried two other instruments that also observed clouds during the same time period (the Mars Orbiter Camera and the Thermal Emission Spectrometer). Combining observations from these three data sets could provide a complete recap of most atmospheric structures during MY24 and MY25. However, previous studies of MOLA data set often had to use stringent detection criteria. Using machine learning clustering methods, we end up finding way more atmospheric returns. Our results are presented in the form of an atmospheric features catalog that we then use to compare MOLA observations with Mars Orbiter Camera and Thermal Emission Spectrometer results, but also with more recent missions. We study the development of recurrent phenomenon in the Martian atmosphere, like the aphelion cloud belt or the south polar hood, but also spontaneous events such as regional dust storms. Methods could be tuned even more finely by using more complex clustering methods or deep learning algorithms to clearly distinguish atmospheric structures. Plain Language Summary: The Mars Orbiter Laser Altimeter (MOLA) instrument has been emitting laser pulses toward the Martian surface. Time of flight of the laser before returning to the instrument was originally used to estimate the altitude of Mars' surface, but the sensibility of the detector was good enough to detect clouds' signatures coming from the atmosphere. We propose that studying the MOLA data set using machine learning methods that gather similar laser returns into groups can enable the formation of a cluster made of atmospheric features, distinguishing them from noise and surface returns. These features are then grouped into clouds or dust structures and compared with other mission results that also observed the Martian atmosphere between 1997 and 2001. This paints a picture of many phenomena in the low Martian atmosphere, their seasonal and interannual variability and their varying intensity. Key Points: We reanalyze the Mars Orbiter Laser Altimeter data set with clustering methods and retrieve a new, large atmospheric structure data set Comparing the data set with other observations allows us to provide a global view of atmospheric structures We observe the development of the aphelion cloud belt, the polar hoods, regional dust storms, and clouds over topographic features