J. Space Weather Space Clim.
Volume 11, 2021
|Number of page(s)||16|
|Published online||29 January 2021|
Inferring depth-dependent plasma motions from surface observations using the DeepVel neural network
National Solar Observatory, 3665 Discovery Dr., Boulder, 80303 CO, USA
2 University of Colorado, Astrophysical and Planetary Sciences, 2000 Colorado Avenue, Boulder, 80303 CO, USA
3 Université de Montréal, Département de Physique, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, H2V 0B3 QC, Canada
4 Environment & Climate Change Canada, Dorval, H9P 1J3 QC, Canada
5 University of California, Space Sciences Laboratory, 7 Gauss Way, Berkeley, 94720 CA, USA
* Corresponding author: email@example.com
Accepted: 26 November 2020
Coverage of plasma motions is limited to the line-of-sight component at the Sun’s surface. Multiple tracking and inversion methods were developed to infer the transverse motions from observational data. Recently, the DeepVel neural network was trained with computations performed by numerical simulations of the solar photosphere to recover the missing transverse component at the surface and at two additional optical depths simultaneously from the surface white light intensity in the Quiet Sun. We argue that deep learning could provide additional spatial coverage to existing observations in the form of depth-dependent synthetic observations, i.e. estimates generated through the emulation of numerical simulations. We trained different versions of DeepVel using slices from numerical simulations of both the Quiet Sun and Active Region at various optical and geometrical depths in the solar atmosphere, photosphere and upper convection zone to establish the upper and lower limits at which the neural network can generate reliable synthetic observations of plasma motions from surface intensitygrams. Flow fields inferred in the photosphere and low chromosphere τ ∈ [0.1, 1) are comparable to inversions performed at the surface (τ ≈ 1) and are deemed to be suitable for use as synthetic estimates in data assimilation processes and data-driven simulations. This upper limit extends closer to the transition region (τ ≈ 0.01) in the Quiet Sun, but not for Active Regions. Subsurface flows inferred from surface intensitygrams fail to capture the small-scale features of turbulent convective motions as depth crosses a few hundred kilometers. We suggest that these reconstructions could be used as first estimates of a model’s velocity vector in data assimilation processes to nowcast and forecast short term solar activity and space weather.
Key words: active region / chromosphere / convection zone / deep learning / granulation / photosphere / velocity fields
© Tremblay et al., Published by EDP Sciences 2021
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