A Framework for Relating the Structures and Recovery Statistics in Pressure Time-Series Surveys for Dust Devils

TL;DR

This paper presents a statistical framework to correct biases in pressure time-series surveys of dust devils on Mars, revealing higher dust flux and occurrence rates than previously estimated.

Contribution

It introduces a de-biasing method that converts pressure signal durations into physical dust devil parameters using simple statistical and geometric considerations.

Findings

Dust flux and occurrence rates are significantly higher after correction.

Only about 20% of low-pressure cells produce visible dust devil tracks.

The new model aligns pressure data with dust lifting efficiency estimates.

Abstract

Dust devils are likely the dominant source of dust for the martian atmosphere, but the amount and frequency of dust-lifting depend on the statistical distribution of dust devil parameters. Dust devils exhibit pressure perturbations and, if they pass near a barometric sensor, they may register as a discernible dip in a pressure time-series. Leveraging this fact, several surveys using barometric sensors on landed spacecraft have revealed dust devil structures and occurrence rates. However powerful they are, though, such surveys suffer from non-trivial biases that skew the inferred dust devil properties. For example, such surveys are most sensitive to dust devils with the widest and deepest pressure profiles, but the recovered profiles will be distorted, broader and shallow than the actual profiles. In addition, such surveys often do not provide wind speed measurements alongside the pressure time series, and so the durations of the dust devil signals in the time series cannot be directly converted to profile widths. Fortunately, simple statistical and geometric considerations can de-bias these surveys, allowing conversion of the duration of dust devil signals into physical widths, given only a distribution of likely translation velocities, and the recovery of the underlying distributions of physical parameters. In this study, we develop a scheme for de-biasing such surveys. Applying our model to an in-situ survey using data from the Phoenix lander suggests a larger dust flux and a dust devil occurrence rate about ten times larger than previously inferred. Comparing our results to dust devil track surveys suggests only about one in five low-pressure cells lifts sufficient dust to leave a visible track.