WHAT CAN WE LEARN FROM SORCE
ABOUT THE CONTRIBUTION OF
DIFFERENT MAGNETIC
STRUCTURES TO THE SOLAR
SPECTRAL IRRADIANCE (SSI)?
Anatoly Vuiets1, Thierry Dudok de
Wit1 and Matthieu Kretzschmar1.
1. LPC2E and University of
Orleans, France.
MODELING OF SOLAR IRRADIANCE
(SSI AND TSI)
Lack of
observational data.
MODELING OF SOLAR IRRADIANCE
(SSI AND TSI)
Lack of
observational data.
strong need for modeling of
the SSI
MODELING OF SOLAR IRRADIANCE
(SSI AND TSI)
Lack of
observational data.
Common approach is semiempirical modeling (e.g.
SATIRE, COSI).
strong need for modeling of
the SSI
MODELING OF SOLAR IRRADIANCE
(SSI AND TSI)
Lack of
observational data.
Common approach is semiempirical modeling (e.g.
SATIRE, COSI).
strong need for modeling of
the SSI
Assumption: SSI variability
is driven by evolution of
surface magnetic field.
MODELING OF SOLAR IRRADIANCE
(SSI AND TSI)
Lack of
observational data.
Common approach is semiempirical modeling (e.g.
SATIRE, COSI).
characteristic
spectra
+
strong need for modeling of
the SSI
Assumption: SSI variability
is driven by evolution of
surface magnetic field.
MODELING OF SOLAR IRRADIANCE
(SSI AND TSI)
Lack of
observational data.
Common approach is semiempirical modeling (e.g.
SATIRE, COSI).
characteristic
spectra
+
magnetic structures
obtained from
magnetograms and
continuum images
strong need for modeling of
the SSI
Assumption: SSI variability
is driven by evolution of
surface magnetic field.
MODELING OF SOLAR IRRADIANCE
(SSI AND TSI)
Lack of
observational data.
Assumption: SSI variability
is driven by evolution of
surface magnetic field.
flux
Common approach is semiempirical modeling (e.g.
SATIRE, COSI).
strong need for modeling of
the SSI
characteristic
spectra
+
magnetic structures
obtained from
magnetograms and
continuum images
time
ISSUES TO BE SOLVED
ISSUES TO BE SOLVED
- How should the magnetic structures be defined?
?
?
?
?
?
?
?
?
?
?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
?
?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
- How important is the center-to-limb (CTL) effect?
170 nm
?
?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
- How important is the center-to-limb (CTL) effect?
170 nm
?
?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
- How important is the center-to-limb (CTL) effect?
170 nm
?
?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
?
?
170 nm
- How important is the center-to-limb (CTL) effect?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
?
?
170 nm
- How important is the center-to-limb (CTL) effect?
- What is the contribution of magnetic structures to the SSI?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
?
?
170 nm
- How important is the center-to-limb (CTL) effect?
- What is the contribution of magnetic structures to the SSI?
- What spatial resolution is sufficient?
ISSUES TO BE SOLVED
?
?
- How should the magnetic structures be defined?
?
?
?
?
?
?
?
?
170 nm
- How important is the center-to-limb (CTL) effect?
- What is the contribution of magnetic structures to the SSI?
- What spatial resolution is sufficient?
To answer these questions consider an
empirical (“less biased”) approach. No
assumptions on atmospherical models.
MORE EMPIRICAL MODEL
MORE EMPIRICAL MODEL
magnetogram
MORE EMPIRICAL MODEL
magnetogram
continuum image
MORE EMPIRICAL MODEL
classes f
magnetogram
continuum image
segmentation map
MORE EMPIRICAL MODEL
classes f
magnetogram
continuum image
rings r
segmentation map
MORE EMPIRICAL MODEL
classes f
magnetogram
continuum image
rings r
segmentation map
Linear SATIRE-like model:
- characteristic spectrum of class
f in ring r
- filling factor of class f in ring r
(fraction of solar disk covered by
magnetic structures of this class)
MODEL BUILDING
Difference
:
MODEL BUILDING
Difference
- spectra S are not imposed.
:
MODEL BUILDING
Difference
- spectra S are not imposed.
:
- number of classes f is not imposed.
MODEL BUILDING
Difference
- spectra S are not imposed.
:
- number of classes f is not imposed.
- threshold levels between classes f are not pre-defined.
MODEL BUILDING
Difference
- spectra S are not imposed.
:
- number of classes f is not imposed.
- threshold levels between classes f are not pre-defined.
- number of rings r is not imposed.
MODEL BUILDING
Difference
- spectra S are not imposed.
:
- number of classes f is not imposed.
- threshold levels between classes f are not pre-defined.
- number of rings r is not imposed.
- segmentation of magnetograms according
to area of magnetic structures.
MODEL BUILDING
Difference
- spectra S are not imposed.
:
- number of classes f is not imposed.
- threshold levels between classes f are not pre-defined.
- number of rings r is not imposed.
- segmentation of magnetograms according
to area of magnetic structures.
S = I F-1
S - model atmosphere
I - spectral irradiance observations
F -filling factors
DATA
DATA
- Model is calibrated with SSI observations (daily averages).
Instrument
Wavelength, nm
Spectral band
SDO/EVE
6.5-9.5
XUV
SDO/EVE
10.5-35.5
EUV
TIMED/SEE
36-115
EUV
SORCE/SOLSTICE
121
LyA
SORCE/SOLSTICE
115-200
FUV
SORCE/SOLSTICE
280
MgII
SORCE/SOLSTICE
200-300
MUV
SORCE/TIM
TSI
TSI
DATA
- Model is calibrated with SSI observations (daily averages).
Instrument
Wavelength, nm
Spectral band
SDO/EVE
6.5-9.5
XUV
SDO/EVE
10.5-35.5
EUV
TIMED/SEE
36-115
EUV
SORCE/SOLSTICE
121
LyA
SORCE/SOLSTICE
115-200
FUV
SORCE/SOLSTICE
280
MgII
SORCE/SOLSTICE
200-300
MUV
SORCE/TIM
TSI
TSI
- Magnetograms and continuum images
SDO/HMI and SDO/AIA respectively(4096x4096
compressed to 2048x2048 pxls)
DATA
- Model is calibrated with SSI observations (daily averages).
Instrument
Wavelength, nm
Spectral band
SDO/EVE
6.5-9.5
XUV
SDO/EVE
10.5-35.5
EUV
TIMED/SEE
36-115
EUV
SORCE/SOLSTICE
121
LyA
SORCE/SOLSTICE
115-200
FUV
SORCE/SOLSTICE
280
MgII
SORCE/SOLSTICE
200-300
MUV
SORCE/TIM
TSI
TSI
- Magnetograms and continuum images
SDO/HMI and SDO/AIA respectively(4096x4096
compressed to 2048x2048 pxls)
Time interval: 24/04/2010 - 01/07/2013 (about 3
years of data)
SEGMENTATION OF MAGNETOGRAMS BY
AREA VS INTENSITY
Segmentation by magnetic field intensity is
common approach.
Disadvantages:
SEGMENTATION OF MAGNETOGRAMS BY
AREA VS INTENSITY
Segmentation by magnetic field intensity is
common approach.
Disadvantages:
Absolute calibration errors
active region
intensity
difference
intensity is
big
difference in
area is small
x
SEGMENTATION OF MAGNETOGRAMS BY
AREA VS INTENSITY
Segmentation by magnetic field intensity is
common approach.
Disadvantages:
Absolute calibration errors
active region
intensity
difference
intensity is
big
Limb processing
B0
observer
Blos
SUN
difference in
area is small
x
limb
SEGMENTATION OF MAGNETOGRAMS BY
AREA VS INTENSITY
Segmentation by magnetic field intensity is
common approach.
Disadvantages:
Solution:
Limb processing
B0 by
segmentation
observer
difference
area.
Blos
intensity is
Absolute calibration errors
intensity
active region
big
B = aSb SUN
difference in
area is small
x
limb
MAGNETIC STRUCTURES
small structures ≈ active
network
big structures ≈ faculae
umbra
penumbra
quiet sun
200
400
600
800
1000
1200
1400
1600
1800
2000
200
400
600
800
1000
1200
1400
1600
1800
2000
MODEL QUALITY
Model quality estimator:
0
irradiance, W/m2
10
1 � (y(f, r) − y)2
E(f, r) =
N n
σy2
ï2
10
ï4
10
ï6
quality estimator (error)
10
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320
1.2
1
0.8
Training and
validations sets are
different
0.6
0.4
0.2
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320
wavelength, nm
RECONSTRUCTION EXAMPLES
TSI
Model
- model
Observations
- observations
1362
Irradiance, w/m2
1361.5
1361
1360.5
1360
1359.5
27 54 81 108 135 162 189 216 243 270 297 324 351 378 405 432 459 486 513 540 567 594 621 648 675 702 729 756 783 810 837 864 891 918 945 972
Time, days
RECONSTRUCTION EXAMPLES
TSI
Model
- model
Observations
- observations
1362
Irradiance, w/m2
1361.5
Good
1361
1360.5
1360
1359.5
27 54 81 108 135 162 189 216 243 270 297 324 351 378 405 432 459 486 513 540 567 594 621 648 675 702 729 756 783 810 837 864 891 918 945 972
Time, days
RECONSTRUCTION EXAMPLES
Ly Alpha, 121 nm (SORCE)
ï3
9
x 10
Model
Data
- model
- observations
irradiance, W/m2
8.5
8
7.5
7
6.5
6
Aprï2010
Julï2010
Octï2010
Janï2011
Aprï2011
Julï2011
time
Octï2011
Janï2012
Aprï2012
Julï2012
Octï2012
RECONSTRUCTION EXAMPLES
Ly Alpha, 121 nm (SORCE)
ï3
9
x 10
Model
Data
- model
- observations
irradiance, W/m2
8.5
Good
8
7.5
7
6.5
6
Aprï2010
Julï2010
Octï2010
Janï2011
Aprï2011
Julï2011
time
Octï2011
Janï2012
Aprï2012
Julï2012
Octï2012
RECONSTRUCTION EXAMPLES
56 nm (TIMED)
ï5
2.8
x 10
Model
Data
- model
- observations
2.7
irradiance, W/m2
2.6
2.5
2.4
2.3
2.2
2.1
2
Aprï2010
Julï2010
Octï2010
Janï2011
Aprï2011
Julï2011
Octï2011
time, days
Janï2012
Aprï2012
Julï2012
Octï2012
RECONSTRUCTION EXAMPLES
56 nm (TIMED)
ï5
2.8
x 10
Model
Data
- model
- observations
2.7
irradiance, W/m2
2.6
2.5
Data has
problem with
degradation
correction
2.4
2.3
2.2
2.1
2
Aprï2010
Julï2010
Octï2010
Janï2011
Aprï2011
Julï2011
Octï2011
time, days
Janï2012
Aprï2012
Julï2012
Octï2012
RECONSTRUCTION EXAMPLES
270 nm (SORCE)
0.279
Model
Data
- model
- observations
0.278
irradiance, W/m2
0.277
0.276
0.275
0.274
0.273
0.272
0.271
Aprï2010
Julï2010
Octï2010
Janï2011
Aprï2011
Julï2011
time
Octï2011
Janï2012
Aprï2012
Julï2012
Octï2012
RECONSTRUCTION EXAMPLES
270 nm (SORCE)
0.279
Model
Data
0.278
irradiance, W/m2
0.277
- model
- observations
Data suffer
from
instrumental
artifacts
0.276
0.275
0.274
0.273
0.272
0.271
Aprï2010
Julï2010
Octï2010
Janï2011
Aprï2011
Julï2011
time
Octï2011
Janï2012
Aprï2012
Julï2012
Octï2012
THE CENTER-TO-LIMB VARIATION
ction
Theoretical center-to-limb curves
THE CENTER-TO-LIMB VARIATION
ction
Theoretical center-to-limb curves
optically thin
optically thin emissions
emissions
THE CENTER-TO-LIMB VARIATION
ction
Theoretical center-to-limb curves
optically thin
emissions
THE CENTER-TO-LIMB VARIATION
ction
Theoretical center-to-limb curves
optically thin
emissions
optically
thick
emissions
optically
thick
emissions
THE CENTER-TO-LIMB VARIATION
ction
Theoretical center-to-limb curves
optically thin
emissions
optically thick
emissions
THE CENTER-TO-LIMB VARIATION
XUV/EUV
0.55
1 ring, 1 class
0.5
error
0.45
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
THE CENTER-TO-LIMB VARIATION
Rings in model = CTL effect
XUV/EUV
0.55
1 ring, 1 class
0.5
error
0.45
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
THE CENTER-TO-LIMB VARIATION
Classes in model =
atmosphere models
Rings in model = CTL effect
XUV/EUV
0.55
1 ring, 1 class
0.5
error
0.45
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
THE CENTER-TO-LIMB VARIATION
Classes in model =
atmosphere models
Rings in model = CTL effect
XUV/EUV
0.55
1 ring, 1 class
2 ring, 1 class
0.5
error
0.45
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
THE CENTER-TO-LIMB VARIATION
Classes in model =
atmosphere models
Rings in model = CTL effect
XUV/EUV
0.55
1 ring, 1 class
2 ring, 1 class
0.5
error
0.45
Empirical
refinement: major
part of XUV/EUV
emission is optically
thin (as expected).
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
THE CENTER-TO-LIMB VARIATION
Classes in model =
atmosphere models
Rings in model = CTL effect
XUV/EUV
0.55
1 ring, 1 class
2 ring, 1 class
1 ring, 2 class
0.5
error
0.45
Empirical
refinement: major
part of XUV/EUV
emission is optically
thin (as expected).
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
THE CENTER-TO-LIMB VARIATION
Classes in model =
atmosphere models
Rings in model = CTL effect
XUV/EUV
0.55
1 ring, 1 class
2 ring, 1 class
1 ring, 2 class
3 ring, 2 class
0.5
error
0.45
Empirical
refinement: major
part of XUV/EUV
emission is optically
thin (as expected).
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
THE CENTER-TO-LIMB VARIATION
Classes in model =
atmosphere models
Rings in model = CTL effect
XUV/EUV
0.55
1 ring, 1 class
2 ring, 1 class
1 ring, 2 class
3 ring, 2 class
0.5
error
0.45
Empirical
refinement: major
part of XUV/EUV
emission is optically
thin (as expected).
0.4
0.35
0.3
0.25
0.2
5
10
15
20
25
30
35
wavelength, nm
Empirical
refinement: 2 classes
are needed to
reconstruct XUV/
EUV.
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
1 ring 1 class
0.9
0.8
bad data quality
error
0.7
0.6
0.5
0.4
0.3
0.2
120
140
160
180
200
220
240
260
280
wavelength, nm
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
1 ring 1 class
2 ring 1 class
0.9
0.8
bad data quality
error
0.7
0.6
0.5
0.4
0.3
0.2
120
140
160
180
200
220
240
260
280
wavelength, nm
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
1 ring 1 class
2 ring 1 class
1 ring 2 class
0.9
0.8
bad data quality
error
0.7
0.6
0.5
0.4
0.3
0.2
120
140
160
180
200
220
240
260
280
wavelength, nm
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
1 ring 1 class
2 ring 1 class
1 ring 2 class
3 ring 2 class
0.9
0.8
bad data quality
error
0.7
0.6
0.5
0.4
0.3
0.2
120
140
160
180
200
220
240
260
280
wavelength, nm
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
1 ring 1 class
2 ring 1 class
1 ring 2 class
3 ring 2 class
0.9
error
0.7
classes are more
important than rings
bad data quality
0.8
0.6
0.5
0.4
0.3
0.2
120
140
160
180
200
220
240
260
280
wavelength, nm
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
0.9
error
0.7
classes are more
important than rings
bad data quality
0.8
0.6
0.5
0.4
0.3
0.2
1 ring 1 class
2 ring 1 class
1 ring 2 class
3 ring 2 class
classes and rings are of
equal importance
120
140
160
180
200
220
240
260
280
wavelength, nm
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
rings are important
0.9
error
0.7
classes are more
important than rings
bad data quality
0.8
0.6
0.5
0.4
0.3
0.2
120
140
160
180
1 ring 1 class
2 ring 1 class
1 ring 2 class
3 ring 2 class
classes and rings are of
equal importance
200
220
240
260
280
wavelength, nm
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
rings are important
0.9
error
0.7
classes are more
important than rings
bad data quality
0.8
0.6
0.5
0.4
0.3
0.2
120
140
160
180
1 ring 1 class
2 ring 1 class
1 ring 2 class
3 ring 2 class
classes and rings are of
equal importance
200
220
240
260
280
wavelength, nm
3 rings and 2
classes are needed
to reconstruct
FUV/MUV range.
THE CENTER-TO-LIMB VARIATION
FUV/MUV
1
rings are important
0.9
error
0.7
classes are more
important than rings
bad data quality
0.8
0.6
0.5
0.4
0.3
0.2
120
140
160
180
1 ring 1 class
2 ring 1 class
1 ring 2 class
3 ring 2 class
classes and rings are of
equal importance
200
220
240
260
3 rings and 2
classes are needed
to reconstruct
FUV/MUV range.
280
wavelength, nm
Most part of FUV/MUV emission is optically thick. Especially in range:
170-265 nm.
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
Big magnetic structures
contribute more to shortwavelength emissions
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
Big magnetic structures
contribute more to shortwavelength emissions
Small magnetic structures
contribute more to long-wavelength
emissions
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative
relative
contribution
contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative
relative
contribution
contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative
relative
contribution
contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative
relative
contribution
contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
1
Active network
small structures
Faculae
big structures
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative
relative
contribution
contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
Active network
small structures
Faculae
big structures
1
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
Big magnetic structures
contribute more to hot
lines (T > 5K oC)
wavelength, nm
CONTRIBUTION OF DIFFERENT CLASSES TO
SSI VARIABILITY
0
relative
relative
contribution
contribution
irradiance, W/m2
10
ï2
10
ï4
10
ï6
10
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
1.5
Active network
small structures
Faculae
big structures
1
0.5
0
0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200210220230240250260270280290300310320
Big magnetic structures
contribute more to hot
lines (T > 5K oC)
Small magnetic structures
contribute more to cold lines
(T < 5K oC)
wavelength, nm
FILLING FACTORS
MS class 1
-penumbra
MS class 2
4.5
Umbra
Penumbra
4
- umbra
arbitrary units
3.5
3
- large
structures ≈
faculae
2.5
2
1.5
1
1
28 55 82 109 136 163 190 217 244 271 298 325 352 379 406 433 460 487 514 541 568 595 622 649 676 703 730 757 784 811 838 865 892 919 946 973
Time, days
- small
structures ≈
active network
LOWER THRESHOLD FOR AREA
What spatial resolution for solar
images is needed?
Gradually increase the lower
limit for area of active regions
that are taken into the model and
see how the reconstruction
quality changes.
LOWER THRESHOLD FOR AREA
XUV/EUV
What spatial resolution for solar
images is needed?
1
2
3
4
5
6
7
8
0.8
Error, %
Gradually increase the lower
limit for area of active regions
that are taken into the model and
see how the reconstruction
quality changes.
1
0.6
0.4
0.2
0
size[pixels] = 2 pow (bin number - 1)
20
40
Wavelength, nm
60
LOWER THRESHOLD FOR AREA
XUV/EUV
What spatial resolution for solar
images is needed?
1
0.8
Error, %
Gradually increase the lower
limit for area of active regions
that are taken into the model and
see how the reconstruction
quality changes.
0.6
0.4
0.2
0
size[pixels] = 2 pow (bin number - 1)
FUV/MUV
bin number = 1
2
3
4
5
6
7
8
Error, %
1
0.8
0.6
0.4
0.2
150
200
250
Wavelength, nm
300
1
2
3
4
5
6
7
8
350
20
40
Wavelength, nm
60
LOWER THRESHOLD FOR AREA
XUV/EUV
What spatial resolution for solar
images is needed?
1
1
2
3
4
5
6
7
8
0.8
Error, %
Gradually increase the lower
limit for area of active regions
that are taken into the model and
see how the reconstruction
quality changes.
0.6
0.4
0.2
0
20
40
Wavelength, nm
size[pixels] = 2 pow (bin number - 1)
FUV/MUV
60
TSI
0.6
bin number = 1
2
3
4
5
6
7
8
Error, %
1
0.8
0.6
0.4
0.2
150
200
250
Wavelength, nm
300
350
Error, %
0.5
0.4
0.3
0.2
0.1
1
2
3
4
5
6
Size lower limit (bin number)
7
8
LOWER THRESHOLD FOR AREA
XUV/EUV
What spatial resolution for solar
images is needed?
1
1
2
3
4
5
6
7
8
0.8
Error, %
Gradually increase the lower
limit for area of active regions
that are taken into the model and
see how the reconstruction
quality changes.
0.6
0.4
0.2
0
20
40
Wavelength, nm
size[pixels] = 2 pow (bin number - 1)
FUV/MUV
60
TSI
0.6
bin number = 1
2
3
4
5
6
7
8
Error, %
1
0.8
0.6
0.4
0.2
150
200
250
Wavelength, nm
300
350
Error, %
0.5
0.4
0.3
0.2
0.1
1
2
3
4
5
6
Size lower limit (bin number)
7
8
Magnetic structures with are
less than 23 pixels (8’’x 8’’) do
not bring new information to
the model.
CONCLUSIONS
- We find 4 principal classes of magnetic structures (big magnetic
structures ≈ faculae, small magnetic structures ≈ active network,
umbra and penumbra) that allow to reconstruct the SSI variability
in the optimal way.
- Big magnetic structures have size grater that 128’’ x 128’’.
- Small magnetic structures have size from 8’’ x 8’’ to 128’’ x 128 ’’.
- Small magnetic structures contribute more to cold emissions from
chromosphere and photosphere.
- Big magnetic structures contribute more to hot lines .
- Center -to-limb variation effect plays significant role for MUV/FUV
emissions in range from 170 to 265 nm.
Thank you!