Document Type

Article

Publication Date

1-1-2018

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.

Copyright Statement

This is an author-produced, peer-reviewed version of this article. © 2018, Elsevier. Licensed under the Creative Commons Attribution Non-Commercial No Derivatives 4.0. http://creativecommons.org/licenses/by-nc-nd/4.0/. The final, definitive version of this document can be found online at Icarus, doi: 10.1016/j.icarus.2017.07.027

Available for download on Wednesday, January 01, 2020

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