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J. Gjerloev, S. Ohtani, R. Barnes, C. Waters, T.
Motoba, C. Olson
How can we investigate global Magnetosphere-Ionosphere coupling?
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J. Gjerloev, S. Ohtani, R. Barnes, C. Waters, T.
Motoba, C. Olson
How can we investigate global Magnetosphere-Ionosphere coupling?
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3
Outline
1. How do we derive global solutions from sparse data coverage?
2. What are the limitations of the global solutions?
Derived parameters
Processes/phenomena
3. The holy grail of M-I physics: Global, continuous and complete electrodynamic
solutions.
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4
Outline
1. How do we derive global solutions from sparse data coverage?
2. What are the limitations of the global solutions?
Derived parameters
Processes/phenomena
3. The holy grail of M-I physics: Global, continuous and complete electrodynamic
solutions.
![Page 5: How can we investigate global Magnetosphere …gem.epss.ucla.edu/.../Gjerloev-2016-Global-MI-Coupling.pdfMagnetosphere-Ionosphere coupling? J. Gjerloev, S. Ohtani, R. Barnes, C. Waters,](https://reader033.vdocuments.us/reader033/viewer/2022053019/5f25cfc8a50344798c619d03/html5/thumbnails/5.jpg)
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Challenges:
- Spatial coverage
- Temporal resolution
Deriving global solutions: What is the problem?
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Challenges:
- Spatial coverage
- Temporal resolution ?
? ?
Deriving global solutions: What is the problem?
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Approach 1: No assumed
knowledge of system behavior:
1) Simple spatial interpolation
Approach 2: Assuming
knowledge of the system
behavior
2) External driver
3) State descriptors
Deriving global solutions: Three approaches
?
? ?
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Approach 1: No assumed knowledge of system behavior Simple Spatial Interpolation
Approach 1: No assumed
knowledge of system behavior:
1) Simple spatial interpolation
Question: Are the scale sizes of
the features larger than the spatial
data gaps?
Answer: No/yes/maybe.
Depend on:
- science objective
- parameter
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Spherical
harmonics fitting
(weighted)
Sparse
Observations
Fill-in data
Approach 2: Assuming knowledge of the system behavior External Drivers
Fill-in data are provided by discrete
bins of solar wind driver:
Pettigrew et al. (2010)
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Approach 2: Assuming knowledge of the system behavior External Drivers
time
driver /
response
Inherent complexities
• Delay (causality)
• M-I low pass filtering
• SW-M-I history
• M-I internal processes
• Predictability / system info
Pettigrew et al. (2010)
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where βk are the regression
coefficients, xki are SuperMAG indices,
and εi is the model errors. Waters, Gjerloev, Dupont, Barnes, JGR, 2015
Spherical cap
harmonics fitting
(weighted)
Sparse
Observations
Fill-in data
Approach 2: Assuming knowledge of the system behavior State Descriptors
Fill-in data are provided by a multi-
linear regression model:
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Inherent complexities
• Can a scalar describe a 2D or 3D
system?
• What does the index even mean?
• Does it have appropriate temporal
resolution?
• Does it have appropriate spatial
resolution?
Approach 2: Assuming knowledge of the system behavior State Descriptors
= ?
Rostoker [1972] concluded that in order
to avoid the obvious pitfalls for the AE
index it should be used only in
statistical studies rather than individual
events.
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Newell and Gjerloev,
JGR, 2012 Haaland and Gjerloev,
2013
Approach 2: Assuming knowledge of the system behavior State Descriptors
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Newell and Gjerloev,
JGR, 2012 Haaland and Gjerloev,
2013
High energy Van Allen proton
observations produce a slowly
varying ground magnetic field
perturbations that is not
captured by the SYMH index.
Gkioulidou et al., GRL, 2015
Approach 2: Assuming knowledge of the system behavior State Descriptors
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Newell and Gjerloev, JGR, 2011 a,b
AE
S
ME
Approach 2: Assuming knowledge of the system behavior State Descriptors
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Darkness
Sunlit
Global Regional
Approach 2: Assuming knowledge of the system behavior State Descriptors
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External Driver State Descriptors
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Deriving global distributions: What have we learned?
Conclusions:
• Simple spatial interpolation:
- assume scale sizes are larger than spatial data gaps
• External driver:
- assume causality
• State descriptors:
- assume these adequately describe the system state
• It is unclear to what extend pretty smooth distributions provide insight into system
behavior.
Recommendations:
• More emphasis should be put on validation of results/models.
• We must remember which underlying physical processes are excluded/ignored by the
large-scale solutions.
• New models should include/acknowledge the dynamics of the system.
• Models should increasingly allow the user to control settings.
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Outline
1. How do we derive global solutions from sparse data coverage?
2. What are the limitations of the global solutions?
Derived parameters
Processes/phenomena
3. The holy grail of M-I physics: Global, continuous and complete electrodynamic
solutions.
![Page 20: How can we investigate global Magnetosphere …gem.epss.ucla.edu/.../Gjerloev-2016-Global-MI-Coupling.pdfMagnetosphere-Ionosphere coupling? J. Gjerloev, S. Ohtani, R. Barnes, C. Waters,](https://reader033.vdocuments.us/reader033/viewer/2022053019/5f25cfc8a50344798c619d03/html5/thumbnails/20.jpg)
20
Iijima and Potemra, 1978
Global Birkeland Current Distribution: Inherent Assumptions
Inherent assumption:
Currents with scale sizes smaller than the R1-R2 currents are insignificant;
mlat
Reality
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Iijima and Potemra, 1978
Inherent assumption:
Currents with scale sizes smaller than the R1-R2 currents are insignificant;
mlat
Global Birkeland Current Distribution: Inherent Assumptions
mlat
BA
BB
Model
mlat
Δ
Δ
Reality
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Iijima and Potemra, 1978
Inherent assumption:
Currents with scale sizes smaller than the R1-R2 currents are insignificant;
The observed magnetic field perturbations are due to static currents.
mlat
Global Birkeland Current Distribution: Inherent Assumptions
mlat
BA
BB
Model
mlat
Δ
Δ
Reality
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Observational Challenge: Mixing Space and Time
Leaving at 9 am, temperature is 22°C
Arriving at 1 pm, temperature is 31°C
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Slavin et al., 2008
Observations appear to indicate:
The FAC density is highly structured;
The FAC density changes significantly over the 1-6 min
separation of the ST 5 satellites.
ST 5 Mission: Multi point measurements enabling separation of space and time
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Typical Events: Δt~15 sec
Current filaments with scale
sizes larger than ~50 km
change on time scales
longer than ~15 sec.
Gjerloev et al. [2011]
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Current filaments with scale
sizes larger than ~200 km
change on time scales
longer than ~60 sec.
Gjerloev et al. [2011]
Typical Events: Δt~60 sec
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On time scales of ~600 sec
we find significant changes
to the entire FAC system at
all scale sizes.
Typical Events: Δt~600 sec
Gjerloev et al. [2011]
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Highly
Correlated
Poorly
Correlated
?
Is it reasonable to assume that the magnetosphere-ionosphere system is repeatable?
Scale Size and Variability of Birkeland Currents Anticipated result
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Scale Size and Variability of Birkeland Currents Actual result
Is it reasonable to assume that the magnetosphere-ionosphere system is repeatable?
Surprisingly the answer is yes.
Gjerloev et al. [2011]
Highly
Correlated
Poorly
Correlated
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~7 km/s
A satellite pass is
marginally in the high
correlation region on the
dayside.
A sounding rocket is
typically in the poor
correlation region.
~2 km/s
~2 km/s
~7 km/s
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31 Humberset et al. [2016]
Scale Size and Variability of Green Light Emissions
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Calculating FAC’s using Swarm
Single satellite (classical)
Assumptions:
• Static over the time it takes to traverse current sheet
• Simplistic current configuration (e.g. infinite sheet)
Three satellites (curlometer technique)
Assumption:
• Constant current over area
T0
T0+ΔT
T0
T0
T0
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MLT ~ 00 SML ~ -150 nT followed by an onset
Calculating Birkeland Currents using ESA Swarm Example on 1 September 2014
SWARM C
SWARM A
Special thanks to Tetsuo Motoba(STEL, Nagoya U) and Natl. Inst. Polar Res. (Japan)
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23:15:14 UT 23:15:24 UT 23:15:34 UT 23:15:44 UT
Even for best case scenario (stable arcs)
Birkeland currents:
• are not static
• are not infinite sheets
• are not uniform over 50 km
Ohtani et al., 2015
Sat. Lat
arc crossing
Calculating Birkeland Currents using ESA Swarm Example on 1 September 2014
Curlometer
SWARM-A
SWARM-C
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Special Session at the 2016 Fall AGU Meeting (San Francisco; 12-16 December 2016; Abstract Submission : 15 June - 3
August 2016)
Birkeland Currents: Achievements since Iijima and Potemra [1976],
and Challenges in Years to Come
Conveners:
Aoi Nakamizo,1 Natalia Ganushkina,2 Hermann J. Opgenoorth,3
and Lawrence J. Zanetti,4,5 1: NICT (Japan); 2: FMI (Finland)/Univ. Michigan (USA); 3: IRF (Sweden); 4: NOAA (USA); 5: JHU/APL (USA)
Takeshi Iijima (1938–) Thomas A. Potemra (1938–1998)
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Outline
1. How do we derive global solutions from sparse data coverage?
2. What are the limitations of the global solutions?
Derived parameters
Processes/phenomena
3. The holy grail of M-I physics: Global, continuous and complete electrodynamic
solutions.
![Page 37: How can we investigate global Magnetosphere …gem.epss.ucla.edu/.../Gjerloev-2016-Global-MI-Coupling.pdfMagnetosphere-Ionosphere coupling? J. Gjerloev, S. Ohtani, R. Barnes, C. Waters,](https://reader033.vdocuments.us/reader033/viewer/2022053019/5f25cfc8a50344798c619d03/html5/thumbnails/37.jpg)
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North-South Structures
06:12:49 UT 06:16:25 UT 06:21:26 UT
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06:12:49 UT 06:16:25 UT 06:21:26 UT
North-South Structures
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Cousins, E. D. P. and S. G. Shepherd (2010)
Ins
tan
tan
eo
us
S
tati
sti
ca
l
Limitations of statistical models
To what extend does pretty smooth average solutions
provide the system information we seek?
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Limitations of statistical models
N (north-south) E (east-west) Z (down-up)
Waters, Gjerloev, Dupont, Barnes, JGR, 2015
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SM003. Advancing our Understanding of the Dynamic Magnetosphere-Ionosphere System Using Auroral Imaging Conveners: J. Gjerloev, R. Floberhagen, S. Zou, D. Knudsen
Special Session at the 2016 Fall AGU Meeting (San Francisco; 12-16 December 2016; Abstract Submission : 15 June - 3 August
2016
3 min
3 min
3 min
3 min
Henderson et al. [2009]
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SM003. Advancing our Understanding of the Dynamic Magnetosphere-Ionosphere System Using Auroral Imaging Conveners: J. Gjerloev, R. Floberhagen, S. Zou, D. Knudsen
Special Session at the 2016 Fall AGU Meeting (San Francisco; 12-16 December 2016; Abstract Submission : 15 June - 3 August
2016
3 min
3 min
3 min
3 min
Henderson et al. [2009]
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Limitations of global distributions: What have we learned?
Conclusions:
• Derived quantities from measurements is based on assumptions that often are violated.
• SuperMAG-SuperDARN-AMPERE spatiotemporal resolution limit the processes and
phenomena that can be addressed.
Recommendations:
• Users should keep assumptions and limitations in mind before making conclusions.
Providers should be open about these complexities and provide quality flags (when
possible).
• Relationship between large-scale and meso-scale processes should be emphasized (e.g.
feeding and drainage of the auroral electrojet system).
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Outline
1. How do we derive global solutions from sparse data coverage?
2. What are the limitations of global solutions?
Derived parameters
Processes/phenomena
3. The holy grail of M-I physics: Global, continuous and complete electrodynamic
solutions.
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AMPERE-SuperMAG
AMPERE-SuperMAG substorm.
- Non-storm conditions.
Special thanks to Cameron Olson (JHU/APL, Augsburg College)
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AMPERE-SuperMAG
AMPERE-SuperMAG substorm.
- Storm conditions.
Special thanks to Cameron Olson (JHU/APL, Augsburg College)
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Magnetosphere-Ionosphere-Atmosphere-Coupling Project (MIAC)
Challenges: Difference in temporal resolution of datasets
Non uniform spatial coverage
Measurement uncertainties and errors
Technique of deriving parameters from measurements
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Magnetosphere-Ionosphere-Atmosphere-Coupling Project (MIAC)
Touched
Discussed
Discussed
Recommended
Recommended
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Global, continuous and complete solutions: What have we learned?
Conclusions:
• SuperMAG-SuperDARN-AMPERE allow complete-continuous-global first-principle
solutions but:
- measurements have inherent limitations
- parameters derived from measurements use fundamental assumptions
- difference in temporal resolution
- difference in spatial coverage
The solutions will not (generally) allow studies of small to meso-scale processes.
Recommendations:
• Comprehensive objective validation of solutions is essential.
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Outline
1. How do we derive global solutions from sparse data coverage?
2. What are the limitations of the global solutions?
Derived parameters
Processes/phenomena
3. The holy grail of M-I physics: Global, continuous and complete electrodynamic
solutions.
4. Bonus slide.
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Drawing Conclusions From Local/Sparse Observations
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Backup
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53 Special thanks to Tetsuo Motoba(STEL, Nagoya U) and Natl. Inst. Polar Res. (Japan)
MLT ~ 00
SML ~ -200~-300 nT
SWARM C
SWARM A
Calculating Birkeland Currents using ESA Swarm Example on 3 May 2014
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21:25:44 UT 21:25:54 UT 21:26:04 UT 21:26:14 UT
SWARM-C(MLT: 22:20)
Curlometer
SWARM-A
SWARM-C
Curlometer and single-SC
results differs because FACs are
structured along the SWARM
orbit.
Calculating Birkeland Currents using ESA Swarm Example on 3 May 2014
arc crossing