Automatic Recognition and Characterisation of Supergranular Cells from Photospheric Velocity Fields

Abstract

We have developed an exceptionally noise resistant method for accurate and automatic identification of supergranular cell boundaries from velocity measurements. Due to its high noise tolerance the algorithm can produce reliable cell patterns with only very small amounts of smoothing of the source data in comparison to conventional methods. In this paper we describe the method and test it with simulated data. We then apply it to the analysis of velocity fields derived from high-resolution continuum data from MDI (Michelson Doppler Imager) on SOHO. From this, we can identify certain basic properties of supergranulation cells, such as their characteristic sizes, the flow speeds within cells and their dependence on cell areas at high resolution. The effect of the noise and smoothing on the derived cell boundaries is investigated and quantified using simulated data. We show in detail the evolution of supergranular cells over their lifetime, including observations of emerging, splitting, and coalescing cells. A key result of our analysis of cell internal velocities is that there is a simple linear relation between cell size and cell internal velocity, rather than the power law usually suggested.

Precursors of Magnetic Flux Emergence in the Moat Flows of Active Region AR12673

“…We apply a cell recognition algorithm on the flow fields that provides a map of the boundaries of large‐scale flows (Potts & Diver, ) whose main purpose so far has been to extract the supergranular boundaries more accurately than using derivatives (e.g., divergence maps). By large scale we mean any structure whose size is at least 1 order of magnitude greater than the size of a typical granule (∼1 Mm).…”