inversion imaging of the sun-earth system damien allain, cathryn mitchell, dimitriy pokhotelov,...
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Inversion imaging of the Sun-Earth System
Damien Allain, Cathryn Mitchell, Dimitriy Pokhotelov, Manuchehr Soleimani, Paul Spencer, Jenna Tong, Ping Yin, Bettina Zapfe
Invert, Dept of E & E Engineering, University of Bath, UK
BICS, September 2007
Tomography and the ionosphere
• Outline the basic problem
GPS imaging of electron density
• large-scale slow moving (mid/low latitude)
• medium-scale fast moving (high latitude)
• high-resolution imaging
• small-scale structure
System applications
Next steps
Plan
Along each continuous arc measurements of time-evolving, biased TEC
Produce the time-evolving 3D distribution of electron density
Tomography applied to imaging the ionosphere
Ground-receiver
tomography
Measure – integral of electron density
Solve for spatial field of electron density
Problems
• Incomplete data coverage
• Variability of the measurement biases
• Temporal changes in the ionosphere
Tomography applied to imaging the ionosphere
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If each of the measurements (integrated quantities) are equal to 10, find the density in each pixel …
10
10
1010
Problem 1 - incomplete data coverage
Four equations, four unknowns … but there are many possible answers because the equations are not all independent
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5
5
5
5
8
8
2
2
7
7
3
3
… etc but if vertical ratio is known to be 4:1
Problem 1 - incomplete data coverage
10
10
1010
8
8
2
2
… then the solution is unique
If each of the measurements (integrated quantities) are equal to 10, find the density in each pixel …
See for example Fremouw et al, 1992
Satellite-to-ground measurements are biased in the vertical direction … this means that the inversion is better determined in the horizontal distribution of electron density
Problem 1 - incomplete data coverage
Low peak height small scale height High peak height large scale height
Example of basis set constraints of MIDAS
h
EOF1
EOF2
EOF3
Problem 1 - incomplete data coverage
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Problem 2 – variable measurement biases
Each set of satellite to receiver paths is assumed to have a ‘constant’ measurement bias, c …
In terms of a mathematical solution, this just results in a slightly more underdetermined problem,
because need to solve for c for each satellite-receiver pair
5+c 15+c
See for example Kunitsyn et al., 1994
NmF2 from ground based data?
20 TECu
Hei
gh
t (k
m)
TECu
TECu
Large differences in the profile still result in small TEC changes …
… so we need to use the differential phase not the
calibrated code observations
Problem 2 – variable measurement biases
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Problem 3 – temporal changes
Now, we had a static solution, but what if the ionosphere changes during the time we collect the measurements?
time1 TEC =5 ; time2 TEC=15
5 15
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4
4
1
1
Now, we had a static solution, but what if the ionosphere changes during the time we collect the measurements?
time1 TEC =5 ; time2 TEC=15
5 15
This gives a time-evolving solution of electron density, where (applying for example a linear time evolution) the solution is
8
8
2
2
12
12
3
3
Time 1 Time 2
Problem 3 – temporal changes
A relatively short period is chosen for the time-dependent inversion, for example one hour, and data collected at typically 30 second intervals are considered. The change in the ray path geometry, defined in the D matrix, multiplied by the unknown change in electron concentration (y) is equal to the change in TEC, Tc.
The mapping matrix, X, is used to transform the problem to one for which the unknowns are the linear (or other) changes in coefficients (G)
TcDy
TcD(XG)
MIDAS – time-dependent inversion
Spherical harmonics and EOFs (X)
The time-dependent solution to the inverse problem is then given by
The matrices can be re-written such that the ray path geometry is multiplied directly by the mapping matrix to create the basis set
and the change in the unknown contributions of each of these line integrations of electron concentration is solved for
.
TcDX(G)
TcDXG 1)(
MIDAS – time-dependent inversion
XGy
[electron density change] = [model electron density] [coefficients]
Solve for G
MIDAS – high latitude
Problems
• Grid geometry
• Limited ground-based data
• Severe gradients, localized features
• Fast moving structures
Solutions
• Rotated grid
• Convected background ionosphere
zHx r
1 tAxx
QAAPP T
t 1
1)( RHPHPHK TT
)( HxzKxx t
PKHIP )( t
State transition to project prior into the future
Convected ionosphere formulated in Kalman filter
New density is formed from projected previous state and new measurements
H is the path-pixel geometry defined by the satellite orbits and receivers
measurements
Variance in observations (IFB)
MIDAS – comparison to EISCAT
Acknowledgement: EISCAT Scientific Association, in particular Ian McCrea at CCLRC, UK
Electron density as a function of height and universal time 30th October 2003
EISCATradar
MIDAStomography
Conjugate plasma controlled by electric field
Arctic Antarctic
GPS data-sharing collaboration through International Polar Year 2007-2008
Extension of imaging to Antarctica
Equatorial imaging and GPS Scintillation – South America and Europe
In collaboration with Cornell University, USA
GPS
Ionosphere multi-scale problems – system effects
Credit: ESA
• Perturbs the signal propagation speed proportional to total electron content – tens of metres error at solar maximum
Space-based P-band radar (SAR)
• forest biomass estimation• ice sheet thickness determination
Ionosphere multi-scale problems – system effects
Ionospheric impacts
• Faraday rotations from several degrees to several cycles in high sun-spot periods
• defocusing by ionospheric irregularities
Tomography and the ionosphere
GPS imaging of electron density
System applications
Next steps …
Summary and Further Work
Goal – to nowcast and forecast the Sun-Earth System
Models –
• Do we know all of the physics of the Sun-Earth System?
• Can we simplify it into a useful Sun-Earth model?
• Computational – how can we minimise the computational costs?
Multi-scale data assimilation (temporal and spatial) will be essential
Next steps
Credit to ESA