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arXiv · 1110.6117

Decorrelation Times of Photospheric Fields and Flows

Brian T. Welsch·Kanya Kusano·Tetsuya T. Yamamoto·K. Muglach
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Abstract

We use autocorrelation to investigate evolution in flow fields inferred by applying Fourier Local Correlation Tracking (FLCT) to a sequence of high-resolution (0.3 \arcsec), high-cadence ($\simeq 2$ min) line-of-sight magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the {\em Hinode} satellite over 12--13 December 2006. To baseline the timescales of flow evolution, we also autocorrelated the magnetograms, at several spatial binnings, to characterize the lifetimes of active region magnetic structures versus spatial scale. Autocorrelation of flow maps can be used to optimize tracking parameters, to understand tracking algorithms' susceptibility to noise, and to estimate flow lifetimes. Tracking parameters varied include: time interval $\Delta t$ between magnetogram pairs tracked, spatial binning applied to the magnetograms, and windowing parameter $\sigma$ used in FLCT. Flow structures vary over a range of spatial and temporal scales (including unresolved scales), so tracked flows represent a local average of the flow over a particular range of space and time. We define flow lifetime to be the flow decorrelation time, $\tau$. For $\Delta t > \tau$, tracking results represent the average velocity over one or more flow lifetimes. We analyze lifetimes of flow components, divergences, and curls as functions of magnetic field strength and spatial scale. We find a significant trend of increasing lifetimes of flow components, divergences, and curls with field strength, consistent with Lorentz forces partially governing flows in the active photosphere, as well as strong trends of increasing flow lifetime and decreasing magnitudes with increases in both spatial scale and $\Delta t$.

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Brian T. Welsch, Kanya Kusano, Tetsuya T. Yamamoto, K. Muglach. 2011-10-27. Decorrelation Times of Photospheric Fields and Flows. https://doi.org/10.1088/0004-637x%2F747%2F2%2F130

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