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Interpreting HMI multi-height velocity measurements
Kaori Nagashima
Collaborators of this study: L. Gizon, A. Birch, B. Lptien, S.
Danilovic, R.
Cameron (MPS), S. Couvidat (Stanford Univ.),
B. Fleck (ESA/NASA), R. Stein (Michigan State Univ.) 1
2013.11.19. Solar Group Seminar @MPS
Postdoc of Interior of the Sun and Stars Dept. @MPS (May 2012 -
)
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Interpreting HMI multi-height velocity measurements
Motivation
Multi-height velocity info is useful in many purposes: Study of
energy transport in the solar atmosphere (e.g., Jefferies et
al.
2006, Straus et al. 2006) Detection of flows in the chromosphere
using multi-line observations
by helioseismology technique (e.g., Nagashima et al. 2009, see
next slides)
If we can obtain multi-height velocity info from HMI
full-disc
every-day observations, it has advantage in that we have much
larger amount of datasets available compared with any other current
observations.
2
Want to obtain multi-height velocity info from SDO/HMI
observation datasets!
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(Nagashima et al. 2009 ApJL) Measure acoustic travel time in AR
and in QS Use photospheric (ph) and chrospheric (ch) datasets In QS
supergranular patterns are seen both in ph and ch. In AR, only in
chromospheric datasets travel time anomaly
is detected Outward travel timeinward travel time
3
black : outward inwar grayscale:-1 +1 min
ch
ph
Ca II H
Fe Doppler
[Mm] outward-inward travel-time difference maps
[Mm]
Multi-wavelength helioseismology study example: Helioseismic
signature of chromospheric downflow in acoustic travel-time
measurements from Hinode
[Mm]
sample images [Mm]
They are different!!!
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What we could say by the multi-height helioseismology was
In an emerging flux region (EFR), we found travel time anomaly
in plage in chromosphere is stronger than in photosphere.
This can be interpreted as DOWNFLOWS in chromosphere.
4 4
chromosphere
photosphere
Emerging flux
plage Downflow
sunspots
magnetic field line
V~2km/s
V
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Want to obtain multi-height velocity info from SDO/HMI
observation datasets!
Interpreting HMI multi-height velocity measurements
Motivation
Multi-height velocity info is useful in many purposes: Study of
energy transport in the solar atmosphere (e.g., Jefferies et
al.
2006, Straus et al. 2006) Detection of flows in the chromosphere
using multi-line observations
by helioseismology technique (e.g., Nagashima et al. 2009, see
next slides)
If we can obtain multi-height velocity info from HMI
full-disc
every-day observations, it has advantage in that we have much
larger amount of datasets available compared with any other current
observations.
5
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some attempts to obtain multi-height info from SDO/ HMI
Fleck et al. (presentations @AGU 2010 etc.) Report the phase
difference in
their multi-height Dopplergrams made by HMI filtergrams
Rajaguru et al. (2012)
Exploit the multi-height HMI and AIA data to study power
enhancement around ARs in various heights.
downward propagating phase Atmospheric gravity mode
signature
Fleck et al. (a figure in their poster at AGU in 2010) So. It is
promising. 6
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Create multi-height Dopplergrams using SDO/HMI observables
7
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Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics
Observatory (SDO) HMI observes the Sun in Fe I line at 6173
HMI takes filtergrams at 6 wavelengths around the line.
I5 I0
Fig. 6 in Schou et al. 2011 SoPh
Standard Dopplergram is derived from these 6-wavelength
filtergrams basically the center of gravity of the line (see next
slide)
I0 at +172.0mA I1 at +103.2mA I2 at +34.4mA
I5 at-172.0mA I4 at -103.2mA I3 at -34.4mA
Fe I line profile
HMI filter tuning-position profiles
8
In this work, using these filtergrams, we try to make
multi-height Dopplergrams instead.
HMI
SDO
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Standard HMI Dopplergram (Couvidat et al. 2012)
Calculate the line shift based on the Fourier coefficients of
the 6 filtergrams
Considering the line asymmetry etc., they calibrate this v by
using calibration table, and make the standard Dopplergrams
(pipeline products)
9
I5 I0
Formation layer @ ~100km above the surface (Fleck et al. 2011)
Similar to the formation layer of the center of gravity of the 6
filtergrams.
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At first, we made 3 simple Dopplergrams, but it did not work
well.
Doppler signal: +
= +
fitting the average Doppler signals by 3rd order polynomial
using the SDO orbital motion
Disadvantage 1SDO motion (and fitting range) is limited (
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11
2
3
+
Doppler signal averaged over FOV
SDO velocity [m/s]
core
Usable only within a limited range
Limited valid range due to small wavelength separation
saturated
If = .km/s = .mA
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We tried several other definitions of Dopplergrams, and found
these two look good.
1. Average wing (for deeper layer) Calculate the Doppler signals
using the
average of each blue and red wing.
+
( =5+42
, =0+12
)
12
I5 I0 I4
I1
Convert the signal into the velocity: 1. Calculate the average
line profile 2. Parallel-Dopplershift the average
line profile 3. Calculate the Doppler signals 4. Fit to a
polynomial function of the
signal
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13
2. Line center (for shallower layer) Doppler velocity of the
line center
derived from 3 points around the minimum intensity
wavelength
Calculate the parabola through the 3 points and use its apex as
the line shift
So, we have 1. Average-wing Dopplergrams 2. Line-center
Dopplergrams 3. And Standard HMI Dopplergrams (pipeline
products) Now we have 3 Dopplergrams!
Are they really multi-height?
We tried several other definitions of Dopplergrams, and found
these two look good.
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Are they really multi-height Dopplergrams? (1)
Estimate of the formation height using simulation datasets
14
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Are they really multi-height Dopplergrams? (1) Estimate of the
formation height using simulation
datasets 1. Use the realistic convection simulation
datasets: STAGGER (e.g., Stein 2012) and MURaM (Vgler et al.
2005)
2. Synthesize the Fe I 6173absorption line profile using SPINOR
code (Frutiger et al. 2000)
3. Synthesize the HMI filtergrams using the line profiles, HMI
filter profiles, and HMI PSF
4. Calculate these Dopplergrams: Line center & Average wing
& standard HMI
5. Calculate correlation coefficients between the synthetic
Doppler velocities and the velocity in the simulation box
15
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16
Sample filtergram images (10Mm square)
HMI observation data ~370km/pix
STAGGER synthetic filtergrams (reduced resolution using HMI PSF,
~370km/pix)
STAGGER synthetic filtergrams (with STAGGER original resolution,
48km/pix)
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17
Sample synthetic Dopplergrams (10Mm square)
HMI observation
Average wing
Line center
Synthetic HMI Dopplergram
Standard HMI Dopplergram
STAGGER synthetic filtergrams (reduced resolution using HMI PSF,
3.7e2km/pix)
STAGGER synthetic filtergrams (with STAGGER original resolution,
48km/pix)
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Estimate of the formation height using simulation datasets
Correlation coefficients between the synthetic Doppler velocities
and
the velocity in the simulation box
18
Correlation coefficients
Peak heights Line center 221km Standard HMI 195km Average wing
170km
Line center Standard HMI
Average wing
26km 25km
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19
w/ PSF they are higher!
(with original STAGGER resolution (no HMI PSF))
Estimate of the formation height using simulation datasets
Correlation coefficients between the synthetic Doppler velocities
and
the velocity in the simulation box Correlation coefficients
Peak heights Line center 144km Standard HMI 118km Average wing
92km
Line center Standard HMI
Average wing
26km 25km
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20
17.6km/pix
Estimate of the formation height using simulation datasets
Correlation coefficients between the synthetic Doppler velocities
and
the velocity in the simulation box Correlation coefficients
MURaM simulation data
Peak heights Line center 150km Standard HMI 110km Average wing
80km
Line center Standard HMI
Average wing
40km 30km
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The width of the correlation peak is so large.
21
Vz auto-correlation coefficient in the wavefield provided by
STAGGER datasets
STAGGER (original resolution) STAGGER (w/ HMI PSF)
Wide peaks Therefore, the Dopplergram of this wavefield should
have such a wide range of contribution heights.
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Contribution layer is higher when the resolution is low (i.e.,w/
PSF) If the formation height in the cell is higher
In the cell it is brighter than on the intergranular lane
The cell contribution is larger than the intergranular lanes
contribution?
Therefore, the contribution layer is higher. right???
22 a) Continuum intensity map
STAGGER simulation data
b) Surface vertical velocity map
c) = 1 layer height map
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Are they really multi-height Dopplergrams? (2)
Phase difference measurements
23
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Power maps of the Dopplergrams
24
HMI observation data STAGGER simulation data
Line center
Standard HMI Dopplergrams
Average-wing
*No data due to the different cadence (1-min for STAGGER, 45-sec
for HMI obs)
Horizontal wavenumber x Rsun
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Phase difference between Doppler velocity datasets from two
different height origins
25
The waves above the photospheric acoustic cutoff (~5.4mHz) can
propagates upward. -> Phase difference between two layers with
separation
2=
Rough estimate: Photospheric sound
speed: ~7 km/s Phase difference measured:
= 30 deg @8mHz
~ 73km This meanswhat?
No significant phase difference (in p-mode regime)
Atmospheric gravity wave ? (e.g., Straus et al. 2008, 2009)
Significant phase difference is seen. Surely they are from
different height origin.
HMI observation data
Line center HMI
Average wing
a b
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We have estimated the contribution layers by calculating the
correlation coefficients between the Doppler velocities and Vz in
the atmosphere That was for bulk velocities.
Here by the phase difference map in the k- space, we
consider each (k, ) component. In this case, the velocity for
each component is small (can be
considered as linear perturbation from the total velocity) Here
we try to use response function
26
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Response function convolved with the HMI filter profiles
I , , const
= ,
I , : Intensity at the wavelength if the velocity field is = ()
z: geometrical height ( = 0 @ 5000 = 1)
response function
Height [km] 27
I0 I5
Def:
Calculated by STPRO in SPINOR code (Frutiger et al. 2000)
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Response functions for simple Dopplergrams
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Center-of-gravity heights 147.4km 166.6km 143.9km 191.8km
For simplicity, here we consider only for the simple
Dopplergrams, =
+
And assume response function for is ~ . Difference between
average-wing and core (substitute for line center) is 44km The
height difference roughly estimated by the phase difference is ~
73km
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29
*No data due to 1-min cadence (STAGGER, 45-sec for HMI obs)
HMI observation data STAGGER simulation data
Line center HMI
Average wing
a b
In the STAGGER simulation data Acoustic cutoff frequency seems
lower (
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Phase difference of Vz in different height layers
30
STAGGER synthetic Dopplergrams
Line center HMI
Average wing
a b
170km 144km
92km 118km a
b c
Similar to the synthetic Dopplergrams
STAGGER Vz
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Phase difference (CO5BOLD case)
Fig. 1 in Straus et al. 2008 31
IBIS obs. COBOLD
Phase difference of the velocity fields at 250km and 70km above
surface They have -negative phase shift above the acoustic cutoff -
Positive phase shift in the lower frequency ranges (atmospheric
gravity waves)
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Summary
32
Confirm that we can obtain multi-height velocity information in
the solar atmosphere using SDO/HMI data By estimating the
contribution layer height of the multi-height velocity
info using STAGGER/MURaM simulation datasets
By calculating the phase difference between the velocities with
different
height origins.
Note: We limit these discussions in the Quiet Sun.
Line center Standard HMI
Average wing
30km 30-40km
Interpreting HMI multi-height velocity measurementsInterpreting
HMI multi-height velocity measurementsMotivation(Nagashima et al.
2009 ApJL)What we could say by the multi-height helioseismology
wasInterpreting HMI multi-height velocity measurementsMotivation
some attempts to obtain multi-height info from SDO/ HMICreate
multi-height Dopplergrams using SDO/HMI observablesHelioseismic and
Magnetic Imager (HMI) onboard Solar Dynamics Observatory
(SDO)Standard HMI Dopplergram(Couvidat et al. 2012)At first, we
made 3 simple Dopplergrams, but it did not work well.coreWe tried
several other definitions of Dopplergrams, and found these two look
good.We tried several other definitions of Dopplergrams, and found
these two look good.Are they really multi-height Dopplergrams?(1)
Estimate of the formation height using simulation datasetsAre they
really multi-height Dopplergrams?(1)Estimate of the formation
height using simulation datasetsSample filtergram images (10Mm
square)Sample synthetic Dopplergrams (10Mm square)Estimate of the
formation height using simulation datasetsCorrelation coefficients
between the synthetic Doppler velocities and the velocity in the
simulation boxSlide Number 19Estimate of the formation height using
simulation datasetsCorrelation coefficients between the synthetic
Doppler velocities and the velocity in the simulation boxVz
auto-correlation coefficient in the wavefield provided by STAGGER
datasets Slide Number 22Are they really multi-height
Dopplergrams?(2) Phase difference measurementsPower maps of the
DopplergramsPhase difference between Doppler velocity datasets from
two different height originsSlide Number 26Response function
convolved with the HMI filter profilesResponse functions for simple
DopplergramsPhase difference between Doppler velocity datasets from
two different height originsPhase difference of Vz in different
height layersPhase difference (CO5BOLD case)Summary