deletions | additions       diff --git a/However_single_sensor_barometer_surveys__.tex b/However_single_sensor_barometer_surveys__.tex  index cc80dc2..61962f4 100644  --- a/However_single_sensor_barometer_surveys__.tex  +++ b/However_single_sensor_barometer_surveys__.tex 
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Although it is inherent in single-barometer surveys for dust devils, simple geometric considerations can mitigate the influence of the miss distance effect, allowing single-barometer surveys to be corrected. In particular, the physical parameters for dust devils estimated from the pressure time-series can be corrected for the miss distance effect, at least in a statistical sense. In this study, we consider the geometry of encounters between dust devils and barometers and present a statistical model for correcting the miss distance effect. This study is similar to and motivated by \citet{Lorenz_2014}, but, where that study used a numerical simulation to investigate biases in the recovered population of dust devil properties, we employ an analytic framework that allows wider applicability and provides more insight into the problem. As we discuss, though, our model involves some important assumptions and simplifications.  The plan of this paper is as follow: In Section \ref{sec:formulating_the_recovery_biases_and_signal_distortions}, we discuss the typical encounter geometry for a dust devil detection and how the geometry both biases and distorts the recovered parameters. In Sections \ref{sec:the_Pact_recovery_bias_and_distortion} and \ref{sec:the_Gammaact_recovery_bias_and_distortion}, we discuss these effects on the profile depth and width recovered for a dust devil, and in Section \ref{sec:combining_the_Pact-Gammaact_biases_and_distortions}, we show how to combine the effects on profile depth and width together. In Section \ref{sec:comparison_to_observational_data}, we provide a preliminary application of our model to data from two recent dust devil surveys, and in Section \ref{sec:discussion_and_conclusions}, we discuss some of the limitations of our model, ways to improve it, and future work.