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In the decade since, technological developments in miniaturization and data storage now provide a wealth of robust and inexpensive instrumentation, ideally suited for the long-term field deployment required to study dust devils, without the need for direct human involvement. Recently, \citet{Lorenz_2015} deployed an array of ten miniature pressure- and sunlight-logging stations at La Jornada Experimental Range in New Mexico, providing a census of vortex and dust-devil activity at this site. The simultaneous measurements resolved horizontal pressure structures for several dust devils, giving a sense for vortex size and intensity.   Even though such a sensor network can provide spatially resolved information, the resulting estimates of devil structural parameters are still somewhat degenerate as a result of the uncertain encounter geometry. In fact, the distortion biases discussed here can be incorporated via a Bayesian inference scheme to work out the likely devil structure: Equations \ref{eqn:dpdP_obs} and \ref{eqn:dpdGamma_obs} actually represent likelihood functions, providing the probability to recover $P_{\rm obs}$ and $\Gamma_{\rm obs}$, given a devil with $P_{\rm act}$ and $\Gamma_{\rm act}$.  The rich and growing databases of high-time-resolution meteorological data, both for the Earth and Mars, combined with the wide availability and affordability of robust instrumentation, point to bright future for dust devil studies. In particular, the data streaming in from the Mars Science Laboratory Meteorological suite will be provide new insight into Martian dust devils. The formulation presented here provides a simple but robust scheme for relating the dust devils' statistical and physical properties. Though it has some limitations, it provides an important next step in improving our knowledge of these dynamic and ethereal phenomena.