16 citations · 42 across the 4 of their papers we have counts for
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physics.ao-ph2020★ 13 cited
Deep Learning for Classifying and Characterizing Atmospheric Ducting within the Maritime Setting
Hilarie Sit, Christopher J. Earls
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 atm…
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…