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.