Wind measurements from landed missions on Mars are vital to characterise the near surface atmospheric behaviour on Mars and improve atmospheric models. These winds are responsible for aeolian change and the mixing of dust in and out of the atmosphere, which has a significant effect on the global circulation. The NASA InSight mission successfully recorded wind data for around 750 sols. The seismometer, however, recorded nearly continuous data for around 1400 sols. The dominant source of energy in the seismic data is in fact due to the wind. To this end, we propose a machine learning model, dubbed WindSightNet, to map the seismic data to wind speed and direction. This converts the atmospheric information in the seismic data into a physically meaningful wind signal which can be used for analysis. We retrieve wind data from the entire period the seismometer was recording which enables a comparison of the year-to-year wind variations at InSight. The continuous nature of the dataset also enables the extraction of information on baroclinic activity at long periods and the periodicity of observed convective cells. A data science based metric is proposed to provide a quantification of the year-to-year differences in the wind speeds, which highlights variations linked to dust activity as well as other transient differences worthy of further study. On the whole, the seismic-derived winds confirm the dominance of the global circulation on the winds leading to highly repeatable weather patterns.

Convective vortices (whirlwinds) and dust devils (dust-loaded vortices) are one of the most common phenomena on Mars, observable on a daily basis. They reflect the local thermodynamical structure of the atmosphere and are the driving force of the dust cycle. Additionally, they cause an elastic ground deformation, which is useful for retrieving the subsurface rigidity. Therefore, investigating Martian convective vortices with the right instrumentation can lead to a better understanding of the Martian atmospheric structures as well as the subsurface physical properties. In this study, we quantitatively characterized the convective vortices detected by NASA’s InSight mission (~13,000 events) using both meteorological (e.g., pressure, wind speed, temperature) and seismic data. The evaluated parameters, such as the signal-to-noise ratio, event duration, asymmetricity of pressure drop profiles, and cross-correlation coefficient between seismic and pressure signals, are compiled as a catalog. To demonstrate how our catalog can contribute to scientific investigations, we show two analysis results about (i) asymmetrical features seen in the vortex-related pressure drops and (ii) atmospheric-ground interaction to retrieve the subsurface physical properties.

One of the goals of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission is to constrain the interior structure of Mars. We present a hierarchical transdimensional Bayesian approach to extract phase velocity dispersion and interior shear-wave velocity (VS) models from a single seismogram. This method was adapted to Mars from a technique recently developed for Earth (Xu & Beghein, 2019). Monte Carlo Markov Chains (MCMC) seek an ensemble of one dimensional (1-D) VS models between a source and a receiver that can explain the observed waveform. The models obtained are used to calculate the phase velocities of fundamental and higher modes at selected periods, and a subsequent analysis is performed to assess which modes were reliably measured. An advantage of our approach is that it can also fit unknown data noise, which reduces the risk of overfitting the data. In addition, uncertainties in the source parameters can be propagated, yielding more accurate model parameter uncertainties. In this paper, we first present our technique and discuss the challenges stemming from using a single station to characterize both structure and the source and from the absence of a Mars reference model. We then demonstrate the method feasibility using the MSS blind test data and our own synthetic data, which included realistic noise levels based on the noise recorded by InSight.

We characterize the vortex and dust devil activity at Jezero from pressure and winds obtained with the MEDA instrument on Mars 2020 over 415 sols (Ls=6-213º). Vortices are abundant (4.9 vortices per sol with pressure drops >0.5 Pa when correcting from gaps in coverage) and peak at noon. At least one in every 5 vortices carries dust from RDS-MEDA data, and intense vortices are more likely to carry dust. Seasonal variability was small but dust devils were abundant during a dust storm (Ls=152-156º). Vortices are more frequent and intense over terrains with lower thermal inertia favoring a higher daytime surface-to-air temperature gradient. We fit measurements of wind and pressure during dust devil encounters to models of vortices, and investigate their physical characteristics. Diameters range from 5 to 135 m with a mean of 20 m. Three 100-m size events passed within 30 m of the rover. From the close encounters we estimate a dust devil activity of 2.0-3.0 dust devils km$^{-2}$ sol$^{-1}$. A comparison of MEDA observations with a Large Eddy Simulation of Jezero at Ls=45º produces a similar result. We estimate that large dust devils with diameters $>100$ m have a density of 0.1 dust devils km-2sol-1, implying that dust lifting is dominated by the largest vortices in the region. At least one vortex had a central pressure drop of 9.0 Pa and internal winds of 25 ms-1. The MEDA wind sensors were partially damaged during two dust devil encounters, and we detail these events.

Seismic noise recorded at the surface of Mars has been monitored since February 2019, using the seismometers of the InSight lander. The noise on Mars is 500 times lower than on Earth at night and it increases during the day. We analyze its polarization as a function of time and frequency in the band 0.03-1Hz. We use the degree of polarization to extract signals with stable polarization whatever their amplitude. We detect polarized signals at all frequencies and all times. Glitches correspond to linear polarized signals which are more abundant during the night. For signals with elliptical polarization, the ellipse is in the horizontal plane with clockwise and anti-clockwise motion at low frequency (LF). At high frequency (HF), the ellipse is in the vertical plane and the major axis is tilted with respect to the vertical. Whereas polarization azimuths are different in the two frequency bands, they are both varying as a function of local time and season. They are also correlated with wind direction, particularly during the day. We investigate possible aseismic and seismic origin of the polarized signals. Lander or tether noise are discarded. Pressure fluctuation transported by environmmental wind may explain part of the HF polarization but not the tilt of the ellipse. This tilt can be obtained if the source is an acoustic emission in some particular case. Finally, in the evening when the wind is low, the measured polarized signals seems to correspond to a diffuse seismic wavefield that would be the Mars microseismic noise.

Mars 2020 Mars Environmental Dynamics Analyzer (MEDA) instrument data acquired during half of a martian year (Ls 13º-180º), and modeling efforts with the Mars Regional Atmospheric Modeling System (MRAMS) and Mars Climate Database (MCD) allow us to study the seasonal evolution and variability of nocturnal atmospheric turbulence at Jezero crater. Nighttime conditions in Mars’s Planetary Boundary Layer are highly stable because of strong radiative cooling that efficiently inhibits convection. However, MEDA nighttime observations of simultaneous, rapid fluctuations in horizontal wind speed and air temperatures suggest the development of nighttime turbulence in Jezero crater. Mesoscale modeling with MRAMS also shows a similar pattern and enables us to investigate the origins of this turbulence and the mechanisms at play. As opposed to Gale crater, less evidence of turbulence from breaking mountain/gravity wave activity was found in Jezero during the period studied with MRAMS. On the contrary, the model suggests that nighttime turbulence at Jezero crater is explained by mechanical turbulence produced by increasingly strong wind shear related to the development of a bore-like disturbance at the nocturnal inversion interface. The enhanced wind shear leads to a reduction in the Ri and an onset of mechanical turbulence. Once the critical Richardson Number is reached (Ri ∼< 0.25), shear instabilities can mix warmer air aloft down to the surface.

Seismic observations involve signals that can be easily masked by noise injection. For InSight, NASA's lander on Mars, the atmosphere is a significant noise contributor for two thirds of a Martian day, and while the noise is below that seen at even the quietest sites on Earth, the amplitude of seismic signals on Mars is also considerably lower requiring an understanding and quantification of environmental injection at unprecedented levels. Mars' ground and atmosphere provide a continuous coupled seismic system, and although atmospheric functions are of distinct origins, the superposition of these noise contributions is poorly understood, making separation a challenging task. We present a novel method for partitioning the observed signal into seismic and environmental contributions. Pressure and wind fluctuations are shown to exhibit temporal cross-frequency coupling across multiple bands, injecting noise that is neither random nor coherent. We investigate this through comodulation, quantifying the signal synchrony in seismic motion, wind and pressure. By working in the time-frequency domain, we discriminate the origins of underlying processes and provide the site's environmental sensitivity. Our method aims to create a virtual vault at InSight, shielding the seismometers with effective post-processing in lieu of a physical vault. This allows us to describe the environmental and seismic signals over a sequence of sols, to quantify the wind and pressure injection, and estimate the seismic content of possible Marsquakes with a signal-to-noise ratio that can be quantified in terms of environmental independence. Finally, we exploit the temporal energy correlations for source attribution of our observations.

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.

Orbital and surface observations demonstrate that aeolian activity is occurring on Mars. Here we report the aeolian changes observed in situ by NASA's InSight lander during the first 400 sols of operations. Aeolian changes include creep of grains with diameters of up to 3 mm, dust removal, dark trails left by passing vortices and possible saltation. InSight has observed such changes by using, for the first time, simultaneous imaging and continuous, high-frequency meteorological, seismological, and magnetic measurements. We show that this multi-instrument combination constrains both the timing, and specific atmospheric conditions during which, aeolian changes occur. The observed changes are infrequent and episodic, consistently occur between noon and 3 pm, and are systematically associated with the passage of convective vortices. The sudden onset of peak vortex wind speeds promotes particle motion during sequences of enhanced vortex activity and stronger ambient winds. Aeolian changes are correlated with excursions in ground acceleration and magnetic field strength, suggesting vortex-induced ground deformation and charged-particle motion.