  Real-time characterization of refractivity within the marine atmospheric
boundary layer can provide valuable information that can potentially be used to
mitigate the effects of atmospheric ducting on radar performance. Many duct
characterization models are successful at predicting parameters from a specific
refractivity profile associated with a given type of duct; however, the ability
to classify, and then subsequently characterize, various duct types is an
important step towards a more comprehensive prediction model. We introduce a
two-step approach using deep learning to differentiate sparsely sampled
propagation factor measurements collected under evaporation ducting conditions
with those collected under surface-based duct conditions in order to
subsequently estimate the appropriate refractivity parameters based on that
differentiation. We show that this approach is not only accurate, but also
efficient; thus providing a suitable method for real-time applications.
