A Deep Echo State Neural Network is used to predict total intensity at a detector, standard deviation of intensity over the area of a detector, and center-of-intensity for a deep turbulence example. A short description of the reason for choosing a Deep Echo State Network, as well as a full description of the network optimization and an example using 30 seconds of data is given. Specifically, indications are that this type of network can handle the nonstationary and nonlinear aspects of laser propagation through long distance deep atmospheric turbulence. The network shows a remarkable ability to predict future signals. At this time, more work needs to be done on optimizing the network to achieve even better results.
Two improvements are made in modeling polarimetric scattering by ensembles of particles. (1) We propagate the complex scattering matrix instead of the phase function. This allows us to calculate the phase and polarization properties of an entire cloud of particles. Radiation transfer models deal only with the intensity of the scattered light. The advantage is that the new model can deal with situations in which the light is scattered coherently. (2) Monte Carlo methods require calculation of scattering at all angles for each successive particle. The new model requires the calculation of only one scattering angle. This allows for use of sophisticated electromagnetic scattering models such as the Discrete Dipole Approximation, WKB or Digitized Green Function models. The advantage is that we can treat particles of arbitrary morphology more accurately.
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