In this paper, we describe an effort to build a new deep edge detection method designed to detect weather-related phenomena such as clouds and planetary boundary layer heights present in backscatter profile imagery. This method builds on the existing deep model called Holistically-Defined Edge Detection (HED), which was shown to perform better than other information theory and convolutional network techniques for edge detection. Though HED outperforms techniques such as Canny Edge detection, HED’s performance is based on it being trained on natural images with very little noise. Weather-related backscatter profiles, such as those generated from LIDAR-based ceilometers, often contain noise. In addition, there is often less of a difference in the pixel density between edges and non-edges, and due to atmospheric dynamics, continuous edges are not always detected in the images. Under these conditions when using HED, subtle but useful edges are lost from side outputs during the fusing process while the network is being trained. Canny Edge detection also does not perform well under these conditions, as it determines edges based on the differences in pixel density. We describe a new edge detection deep network developed specifically for overcoming these issues by applying physics-aware attention mechanisms to the side outputs of the HED learning process. We show how this method is able to learn the subtle edges as opposed to HED or Canny, when used to identify planetary boundary layer heights which involves distinguishing the mixing layer, residual layer, and nocturnal layer in addition to the cloud heights for ceilometerbased backscatter. Though the intent of this network is to learn planetary boundary layer heights and cloud heights, this method could be applied to other weather-related phenomena and applied to backscatter imagery generated from other sources such as satellites.