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.

The InSight mission (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) has been collecting high-quality seismic data from Mars since February 2019, shortly after its landing. The Marsquake Service (MQS) is the team responsible for the prompt review of all seismic data recorded by the InSight’s seismometer (SEIS), marsquake event detection, and curating seismicity catalogues. Until sol 1011 (end of September 2021), MQS have identified 951 marsquakes that we interpret to occur at regional and teleseismic distances, and 1062 very short duration events that are most likely generated by local thermal stresses nearby the SEIS package. Here, we summarize the seismic data collected until sol 1011, version 9 of the InSight seismicity catalogue. We focus on the significant seismicity that occurred after sol 478, the end date of version 3, the last catalogue described in a dedicated paper. In this new period, almost a full Martian year of new data has been collected, allowing us to observe seasonal variations in seismicity that are largely driven by strong changes in atmospheric noise that couples into the seismic signal. Further, the largest, closest and most distant events have been identified, and the number of fully located events has increased from 3 to 7. In addition to the new seismicity, we document improvements in the catalogue that include the adoption of InSight-calibrated Martian models and magnitude scales, the inclusion of additional seismic body-wave phases, and first focal mechanism solutions for three of the regional marsquakes at distances ~30 degrees.

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.

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.

NASA’s InSight seismometer has been recording Martian seismicity since early 2019, and to date, over 1300 marsquakes have been catalogued by the Marsquake Service (MQS). Due to typically low signal-to-noise ratios (SNR) of marsquakes, their detection and analysis remain challenging: while event amplitudes are relatively low, the background noise has large diurnal and seasonal variations and contains various signals originating from the interactions of the local atmosphere with the lander and seismometer system. Since noise can resemble marsquakes in a number of ways, the use of conventional detection methods for catalogue curation is limited. Instead, MQS finds events through manual data inspection. Here, we present MarsQuakeNet (MQNet), a deep convolutional neural network for the detection of marsquakes and the removal of noise contamination. Based on three-component seismic data, MQNet predicts segmentation masks that identify and separate event and noise energy in time-frequency domain. As the number of catalogued MQS events is small, we combine synthetic event waveforms with recorded noise to generate a training data set. We apply MQNet to the entire continuous 20 samples-per-second waveform data set available to date, for automatic event detection and for retrieving denoised amplitudes. The algorithm reproduces all high quality-, as well as majority of low quality events in the manual, carefully curated MQS catalogue. Furthermore, MQNet detects 60% additional events that were previously unknown with mostly low SNR, that are verified in manual review. Our analysis on the event rate confirms seasonal trends and shows a substantial increase in the second Martian year.

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.