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Wind and turbulence observations with the Mars microphone on Perseverance
  • +21
  • Alexander E Stott,
  • Naomi Murdoch,
  • Martin Gillier,
  • Donald Banfield,
  • Tanguy Bertrand,
  • Baptiste Chide,
  • Manuel de la Torre Juárez,
  • Ricardo Hueso,
  • Ralph D. Lorenz,
  • German Martinez,
  • Asier Munguira,
  • Luis Mora Sotomayor,
  • Sara Navarro López,
  • Claire Newman,
  • Paolo Pilleri,
  • Jorge Pla-García,
  • José A Rodriguez-Manfredi,
  • Agustín Sánchez-Lavega,
  • Michael D. Smith,
  • Daniel Viúdez-Moreiras,
  • Nathan Robert Williams,
  • Sylvestre Maurice,
  • Roger C. Wiens,
  • David Mimoun
Alexander E Stott
Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)

Corresponding Author:[email protected]

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Naomi Murdoch
ISAE SUPAERO
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Martin Gillier
ISAE SUPAERO
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Donald Banfield
Cornell
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Tanguy Bertrand
Paris Observatory
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Baptiste Chide
Los Alamos National Laboratory
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Manuel de la Torre Juárez
Jet Propulsion Laboratory- California Institute of Technology, Pasadena, CA, USA
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Ricardo Hueso
UPV/EHU
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Ralph D. Lorenz
Johns Hopkins University Applied Physics Lab
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German Martinez
Lunar and Planetary Institute
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Asier Munguira
UPV/EHU
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Luis Mora Sotomayor
Centro de Astrobiología
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Sara Navarro López
Centro de Astrobiología
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Claire Newman
Aeolis Research
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Paolo Pilleri
IRAP, Université de Toulouse, France
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Jorge Pla-García
Centro de Astrobiología (CSIC-INTA)
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José A Rodriguez-Manfredi
Centro de Astrobiología (CSIC-INTA)
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Agustín Sánchez-Lavega
Universidad del Pais Vasco UPV/EHU
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Michael D. Smith
NASA Goddard Space Flight Center
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Daniel Viúdez-Moreiras
Centro de Astrobiología (INTA-CSIC)
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Nathan Robert Williams
Jet Propulsion Lab
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Sylvestre Maurice
IRAP
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Roger C. Wiens
Los Alamos National Laboratory (DOE)
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David Mimoun
ISAE, INSTITUT SUPERIEUR DE L'AERONAUTIQUE ET DE L'ESPACE
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Abstract

We utilise SuperCam’s Mars microphone to provide information on wind speed and turbulence at high frequencies on Mars. This is achieved through a correlation analysis between the microphone and meteorological data which shows that the microphone signal power has a consistent relationship with wind speed and air temperature. A calibration function is constructed using Gaussian process regression (a machine learning technique) to use the microphone signal and air temperature to produce an estimate of the wind speed. This wind speed estimate is at a high rate for in situ measurements on Mars, with a sample every 0.01 s. As a result, we determine the fast fluctuations of the wind at Jezero crater which highlights the nature of wind gusts over the martian day. We evaluate the normalised wind standard deviation (gustiness) on the estimated wind speed to analyse the turbulent behaviour. Correlations are shown between the evaluated gustiness statistic and pressure drop rates, temperature, energy fluxes and optical opacity to characterise the behaviour of high frequency turbulent intensity at Jezero crater. This has implications for future atmospheric models on Mars, taking into account turbulence at the finest scales.