Wind and Turbulence Observations With the Mars Microphone on Perseverance

We utilize SuperCam's Mars microphone to provide information on wind speed and turbulence at high frequencies on Mars. To do so, we first demonstrate the sensitivity of the microphone signal level to wind speed, yielding a power law dependence. We then show the relationship between the microphone signal level and pressure, air and ground temperatures. A calibration function is constructed using Gaussian process regression (a machine learning technique) taking the microphone signal and air temperature as inputs to produce an estimate of the wind speed. This provides a high rate wind speed estimate 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. To analyze the turbulent behavior of this wind speed estimate, we calculate its normalized standard deviation, known as gustiness. To characterize the behavior of this high frequency turbulent intensity at Jezero crater, correlations are shown between the evaluated gustiness statistic and pressure drop rates/sizes, temperature and energy fluxes. This has implications for future atmospheric models on Mars, taking into account turbulence at the finest scales.

Automated summary

We use SuperCam's Mars microphone to provide information on high-frequency wind speed and turbulence on Mars. We demonstrate the sensitivity of the microphone signal level to wind speed and establish a power law dependence. A calibration function is developed using Gaussian process regression, which takes the microphone signal and air temperature as inputs to estimate the wind speed. This enables us to analyze the fast fluctuations and turbulent behavior of the wind on Mars.