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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 $\Gamma_{\rm act}$.