Abstract
Direct measurements of plasma motions in the photosphere are limited to the line-of-sight component of the velocity. Several algorithms have therefore been developed to reconstruct the transverse components from observed continuum images or magnetograms. We compare the space and time averages of horizontal velocity fields in the photosphere inferred from pairs of consecutive intensitygrams by the LCT, FLCT, and CST methods and the DeepVel neural network in order to identify the method that is best suited for generating synthetic observations to be used for data assimilation. The Stein and Nordlund (Astrophys. J. Lett. 753, L13, 2012) magnetoconvection simulation is used to generate synthetic SDO/HMI intensitygrams and reference flows to train DeepVel. Inferred velocity fields show that DeepVel performs best at subgranular and granular scales and is second only to FLCT at mesogranular and supergranular scales.
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