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We present a sample-based, autoregressive (AR) method for the generation andtime evolution of atmospheric phase screens that is computationally efficientand uses a single parameter per Fourier mode to vary the power contained in thefrozen flow and stochastic components. We address limitations of Fourier-basedmethods such as screen periodicity and low spatial frequency power content.Comparisons of adaptive optics (AO) simulator performance when fed AR phasescreens and translating phase screens reveal significantly elevated residualclosed-loop temporal power for small increases in added stochastic content ateach time step, thus displaying the importance of properly modeling atmospheric"boiling". We present preliminary evidence that our model fits to AO telemetryare better reflections of real conditions than the pure frozen flow assumption.

A computationally efficient autoregressive method for generating phase screens with frozen flow and turbulence in optical simulations