magnetogram evolution near polarity inversion lines
DESCRIPTION
Magnetogram Evolution Near Polarity Inversion Lines. Brian Welsch and Yan Li Space Sciences Lab, UC-Berkeley, 7 Gauss Way, Berkeley, CA 94720-7450, USA. A report on our work to address two questions: 1. How do strong gradients in B LOS form near PILs? - PowerPoint PPT PresentationTRANSCRIPT
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Magnetogram Evolution Near Polarity Inversion Lines
Brian Welsch and Yan Li Space Sciences Lab, UC-Berkeley,
7 Gauss Way, Berkeley, CA 94720-7450, USA
A report on our work to address two questions:
1. How do strong gradients in BLOS form near PILs?
2. What are “typical” flow patterns near PILs?
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Topic 1: Gradients near PILs. Why study strong gradients in fields along PILs?
Studies have correlated strong gradients alongPILs in LOS magnetograms with flares & CMEs. (Falconer et al., 2003, Falconer et al., 2006, Schrijver, 2007)
But how do these gradients arise? – From convergence of flux, and cancellation? – From flux emergence?
OUR GOAL: Correlate changes in gradients with changes in flux, to see if the occurrence of gradients is correlated with increases in total unsigned flux
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Active Region (AR) Selection• MDI full-disk, 96-minute cadence magnetograms
from 1996-98 were used.
• NAR = 64 active regions were selected.– ARs were selected for tracking – not random sample!– Each had a single, well-defined neutral line.– Hence, most were bipolar.– ARs both with & without CMEs were selected.– Several ARs were followed over multiple rotations; some
lacked NOAA AR designation.
• Here, we analyze Nmag = 4062 AR magnetograms.
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Data Handling
• Pixels more that 45º from heliographic origin were ignored.
• To estimate the radial field, cosine corrections were used, BR = BLOS/cos(Θ)
• Mercator projections were used to conformally map the irregularly gridded BR(θ,φ) to a regularly gridded BR(x,y).
(While this projection preserves shapes, it distorts spatial scales – but this distortion can be corrected.)
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A typical deprojected AR magnetogram.
Each AR was tracked over 3 - 5 days, and cropped with a moving window. A list of tracked ARs, as well as mpegs of the ARs, are online.1
1http://sprg.ssl.berkeley.edu/~yanli/lct/
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Finding Strong-Gradients Near PILs• We used the gradient identification
technique of Schrijver (2007).
• Positive/negative maps M± — where BR > 150 G & BR < -150 G, resp.— were found, then dilated by a (3x3) kernel.
• Regions of overlap, where MOL = M+M- 0, were identified as sites of strong-field gradients near PILs.2
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Using positive & negative masks (black & white contours, resp.) that were dilated (red & blue contours, resp.),
strong-field gradients near PILs were identified as points of overlap (white arrow).
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Quantifying Flux Near Strong Gradients
• MOL was convolved with a normalized Gaussian, G exp(-[x2+y2]/2σ2), with σ = 12.6 in pixel units (15 Mm at the equator).
• Following Schrijver (2007), we summed the unsigned magnetic field in |BR| x CMG, to get a measure, R, of the flux near strong-field PILs.
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A map of the product of BR with CMG , the convolution of the overlap map MOL and a normalized Gaussian, G.
Schrijver (2007) showed that the integral R of unsigned magnetic field |BR | over such maps is correlated with large flares.
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Changes in R vs. Total Unsigned Field, Σ|BR|
• For the NR =1621 magnetograms with R 0, we used the product of the previous BR with same CMG to compute the backwards-difference ΔR.
(When the overlap map MOL is identically zero, R is also zero, and no ΔR is computed.)
• We also computed the difference in summed, unsigned |BR| between the current and previous magnetograms.
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What factors can cause changes in R? And/or in the total unsigned field, Σ|BR|?
• Flux can emerge or submerge, which only happens at PILs. Either process could increase or decrease R.
• Horizontal flows could compress or disperse field, which could increase or decrease R.
• Flux emergence can only increase Σ|BR|, and flux cancellation can only decrease Σ|BR|.
• Flux could cross into or out of the field of view, thereby increasing or decreasing Σ|BR|.
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216
371
671
363
R is the total unsigned flux along PILs near strong fields.
We find increases in R are correlated with increases in unsigned flux --- a signature of emergence.
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Conclusions Regarding Gradients
• Increases in R, the measure of unsigned flux near strong-field PILs, defined by Schrijver (1997), are associated with increases in total unsigned flux.
• With caveats, this supports Schrijver’s contention that flux emergence creates the strong field gradients that he found to be correlated with impulsive energy release.
• Our active region sample was not unbiased with respect to active region morphology and age. Hence, this bears further study, with a larger sample of active regions.
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Topic 2: Flows near PILs. Why study flows near PILs?
Observations and theory suggest that converg- ing and shearing flows along PILs are relevant toprominence formation and eruption.(Martin 1998, Antiochos et al. 1999, Amari et al. 2003a/b, Li et al. 2004)
How common are shearing and converging flows?
OUR GOAL: Estimate flows in active regions, and quantify the strength of shearing and converging motions.
Then, investigate correlations between solar activity and properties of estimated flows.
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Shearing & Converging Flow Examples
DeVore & Antiochos, 2000
Amari et al., 2003a, 2003b
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Active Region (AR) Selection• MDI full-disk, 96-minute cadence
magnetograms from 1996-2007 were used --- larger data set!
• NAR = 68 active regions were selected.– ARs were selected for easy tracking -- not
random sample!– Each had a single, well-defined neutral line.– Hence, most were bipolar.– ARs both with & without CMEs were selected.– Several ARs were followed over multiple
rotations; some lacked NOAA AR designation.
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Several techniques exist to estimate velocities.
• Time series of vector magnetograms can be used with:– FLCT, ILCT (Welsch et al. 2004, Fisher & Welsch 2007), – MEF (Longcope 2004), – MSR (Georgoulis & LaBonte 2006), – DAVE, DLCT (Schuck, 2006).
• We have been using FLCT, and have recently implemented versions of DAVE & DLCT.
• Flows discussed here were estimated with FLCT.
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Fourier local correlation tracking (FLCT) finds v(x1,x2) by correlating image subregions.
1) for ea. (xi, yi) above |B|threshold…
2) apply Gaussian mask at (xi, yi) …
3) truncate and cross-correlate…
*
4) v(xi, yi) is inter-polated max. of correlation funct
=
==
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- We created “synthetic magnetograms” from ANMHD simulations of an emerging flux rope. - In these data, both v & B are known exactly.
Recently, we conducted quantitative tests & comparisons of several available methods.
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We tested several methods, with increasing time intervals Δt between the correlated images.
% errors in magnitude showed biases – FLCT underestimates v.
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However, FLCT did estimate the direction of v to better than 30º, on average.
CVEC and CCS were as defined by Schrijver et al. (2005):
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We found that flows along contours of Bn are harder to estimate accurately than flows along Bn.
Unfortunately, flows along contours of Bn inject magnetic energy and helicity very efficiently!
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How do we determine if shearing or convering flows are present?
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First, we decompose velocities into parallel and perpendicular components…
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First, we decompose velocities into parallel and perpendicular components…
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First, we decompose velocities into parallel and perpendicular components…
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First, we decompose velocities into parallel and perpendicular components…
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First, we decompose velocities into parallel and perpendicular components…
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At each pixel, a local right-hand coordinate system is defined, with +x along BR.
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We can then study the whole-AR properties of flows along gradients and contours.
UNWEIGHTED
FIELD-WEIGHTED(signed B!)
These plots show the average flows along contours and gradients for each tracked magnetogram for this active region --- 50 velocity fields in this case.
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…then we isolate regions near PILs…
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We can then study the properties of flows along gradients and contours near PILs!
UNWEIGHTED
FIELD-WEIGHTED(signed B!)
These plots show the average flows along contours and gradients for each tracked magnetogram for this active region --- 50 velocity fields in this case.
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We can also compare the average flow properties of whole active regions with each other!
This plot shows field-weighted, AR-averaged contour and gradient flows near PILs for all 68 of our ARs.
Not all ARs show clear tendencies!
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Conclusions Regarding Flows
• This work is still very much in progress!
• We have tracked N = 68 active regions, and are analyzing our tracking results.
• We must still correlate our derived flows’ properties with measures of coronal activity.
• We are also working to improve our flow estimates. We will probably apply “new & improved” methods to our data set, too!