Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real‐time applications. Key Points Artificial neural networks can quickly characterize electromagnetic wave propagation within the marine atmospheric boundary layer Bistatic sampling of propagation factors is practical for estimating EM duct height with neural network models The rationale underpinning the design of our machine learning models is carefully explained: to demystify the process