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 - )

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!

(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!!!

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

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

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

Create multi-height Dopplergrams using SDO/HMI observables

7

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

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.

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 (

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

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

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.

Are they really multi-height Dopplergrams? (1)

Estimate of the formation height using simulation datasets

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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

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)

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)

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

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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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

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.

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

Are they really multi-height Dopplergrams? (2)

Phase difference measurements

23

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

Phase difference between Doppler velocity datasets from two different height origins

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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

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

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)

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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*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 (

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

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)

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