56 citations · 117 across the 16 of their papers we have counts for
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physics.ao-ph2019★ 13 cited
Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer
Hilarie Sit, Christopher J. Earls
We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled p…
physics.ao-ph2019★ 16 cited
Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
Hilarie Sit, Christopher J. Earls
We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using spar…
physics.ao-ph2019
A subspace pursuit method to infer refractivity in the marine atmospheric boundary layer
Marc Aurèle Gilles, Christopher Earls, David Bindel
Inferring electromagnetic propagation characteristics within the marine atmospheric boundary layer (MABL) from data in real time is crucial for modern maritime navigation and commu…