Australian Regional Ionospheric Disturbance Index based on the Principal
Component Analysis and GPS Data

Z.Bouya, M. Terkildsen, M. Francis, David Neudegg and Richard Marshall.
IPS Radio & Space Services, Bureau of Meteorology
Sydney, Australia

AOGS2012, 13-18 August , Singapore.

Outline:-Introduction- Ionospheric impact- Ionospheric Disturbance Index-Database and Methodology- Regional monitoring of the ionosphere- Eignmode Decomposition- Space Weather Indices/Forecast- Application of the disturbance Index approach: Test Cases- Conclusion/Future work

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Introduction
Plasma Control Envirenment

Space Weather Envirenment

Vertical structure: D, E, F1 and F2 layers

Changes in the interconnected system fromthe Sun to the Earth.
Conditions in Sun-solar windmagnetosphere -ionospherethermosphere system.

-Spatial variation:~3 geographical regions / different behaviors.
(Equatorial/ low latitude, Mid-latitude ,Polar/high latitude)-Temporal variations: connected to the solar activity-Short-scale disturbances:(Ionospheric storms, Traveling ionospheric disturbances (TID)s)

Environment
where space-vulnerable
systems operate
NASA/ESA-Operational radio systems sensitive tocurrent space weather conditions needreliable information as fast and as good aspossible.

-Space weather impacts radio waves travellingthrough the ionized part of the Earth‘satmosphere(ionosphere/plasmasphere).

www.ips.gov.au(from Hargreaves, 1992)

Structure and dynamics of the ionosphere are strongly controled by
the solar radiation and the solar wind and coupled with themagnetosphere, thermosphere and lower atmosphere.

Ionspheric impact
Effect on radio wave propagation (on various areas):-Global Navigation Satellite Systems (GNSS) are severely affected-Ionospheric ranging error /Ionospheric delay-Ionospheric gradients- Ionospheric scintillation-High variability of GNSS signal strength (radioscintillations)-Can be severe enough to cause loss of lock (nosignal availability) on one or more
GPS satellites, reducing positioning accuracy.

Ionospheric perturbations (Ionospheric storms ):-Degrade precise positioning services-Affect Safety of Life (SoL) applications of GNSS.
Near real time detection and forecasting of ionospheric storms : Probability of a possible impact
 Users WARNING
 Risk assessment of violating: Accuracy bounds/ Protection levels

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Detection / monitoring of ionospheric threats:- Geomagnetic and ionospheric storms are strongly couplled- Preliminary indicator: Geomagnetic indices
Geomagnetically based probability of ionospheric perturbations:Geomagnetic indices (Kp, Ap, and Dst )-Important in modelling and characterizing
the perturbation degree of the geospace.
-Characterize the planetary perturbation
quite well
NOT a correct description of theperturbation degree of the local or regionalionosphere.
-Temporal resolution: in the order of 1–3 h
NOT sufficient to fulfil customer needs.

The ionosphere reacts quite different on space weather factors depending on time andgeographic/geomagnetic location.

-GNSS users need more accurate information.
-Geomagnetic indices do not really match to customer needs toreceive correct ionospheric weather information in near real time.

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Proposed Disturbance Index
Actual and reliable index which describes the regional ionosphericstate with a potential to be used in operational applications !!!
Requirements:-Objective measure of the ionospheric perturbation
The complex information is condensed in a simple number(can easily be fed into user algorithms and models.)-Relevance to practical needs-Match to customer needs to receive correct ionospheric weather information in near real time

-Availability of reliable database-Continuous computation-Provision in near real time streaming mode
-Well suited for forecasting

Total Electron Content (TEC) (electron concentration over the local vertical).
Dual frequency measurements  Total Electron Content (TEC):STEC   ne ds~ ionospheric range error(1storder)

R-Outstanding parameter for quantifying the range error and also the strength of ionospheric perturbations.
-The availability of such data is permanently increasing which improves the conditions for estimating
the disturbance index with a good performance.

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Regional monitoring : Spherical Cap Harmonic Analysis (SCHA)- GNSS applications are in particular sensitive to spatial gradients and temporal variability of TEC.

-To fulfil user needs we focus on ionospheric perturbations which are characterized by a high spatial
and temporal variability.

-SCH model for mapping the Australian Regional TEC:VTEC ( c , c ) K max K Pmmnk ( m) (cos( c ) g k cos( mc ) hkm sin(mc )k 0 m 0(Haines,1985)k  n k(m)Non integer degree (  0   )SCHA accommodates spatially confined observations without
having to redistribute the data coordinates over the earth.

-Reconstruction of regional
ionosphere needed forgetting reliable information

Regional TEC map on 02/01/2010 at 04:00UT(right) and 15:00UT(left)

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Eignmode Decomposition: PCAz(t,x) Matrix of N Regional TEC maps at P locations (N: Number of hours (January –December, 2010).
-Spatial structures: Ej(θ,φ) Eigenvectors (Basis Function) (generated directly by themselves)(ranged in descending order according to the proportion of variance explained)-Their time evolution :A (t )  z ' (t , , ) E ( , )Tjj

(uncorrelated and carrying information about the variation of TEC along E j)

Dynamic approach: Ignoring smooth and slowly developing variations (first 2 components)-Seasonal variation and the solar dependence of the background TEC over Australia. “Regional Climatology”-The meteorology of the regional TEC perturbation is investigated.

-With decreasing variance PCs
explain increasingly complexfeatures-As PCs are not correlatedwith each other they offer an
added advantage as
predictors .
“Meteorology”

Explained variance and the accumulated explained variance for the TEC .

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Seasonal variation and the solar dependence of the backgroundSignificance: 69.7%
A1E1 mainly controls the intensity of the
Ionospheric electron concentration.

Significance: 16.3%
A2E2 : the correction of the main trend.

First two EOFs of the TEC for the year 2010, function of longitude and latitude

A1: Semiannual : 2 maxima(March/April and September/October ).

A2: Annual variation

Time series of the first 2 EOFs coefficients shown above (A1, A2) (blue, pink) for the year 2010.

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Higher order components: PC3-Present mainly short-term variation and noise-Less correlated with the primary variation in TEC.
PC3280
240
200
160
120
80
400-40-80-120-160-200

Time series of the third component for the year 2010.

0600

-0.05

400-0.1-0.15

200-0.2

0028

56

84112

140

168

196-200

224

252

280

308

336364-0.25-0.3-0.35-400-0.4048121620

24-600

Bases function representing the variation with universal time and associated coefficient representing theseasonal variation.

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Space Weather Indices and TEC perturbation
F10.7
1E-20
9.5E-219E-21
8.5E-218E-21
7.5E-21
7E-21
028

56

84

112

140

168

196

224

252

280

196

224

252

280

196

224

252

280

224

252

280

308

336

364

dF10.7

1.50E-21
1.00E-21
5.00E-22
0.00E+00
028

56

84

112

140

168

308

336

364

-5.00E-22-1.00E-21-1.50E-21
AP6050403020100-100285684112

140168308336364PC3600
40020000285684112

140168196308

336364-200-400-600

Daily TEC perturbation, solar activity index(F10.7), deviation of F10.7 and geomagnetic
activity index Ap.

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Space Weather Indices and TEC perturbation: April 2010

Most influential factors
Factors

F10.7

dF10.7

AP

Dst

Correlation

0.23

0.38

0.51

0.43

-Delayed perturbation acros
the region-Potential for predictionbasedon earlier space weatherobservations

Comparison of daily Dst index (a), change in the geomagnetic field’s horizontal component atCanberra (b), Ap index (c) and TEC perturbation (d) over the Australian region (April 2010).

Application of the approach (test cases): December 2006 Geomagnetic Storm
Development of the storm:-Solar flares and Sunspots associated with large CMEs
-Change in Dst index (SSCs)-Severity of the disturbed geomagnetic conditions (Ap Index)

Probable CME observed by SOHO satellite
Probable CME (UTC)Class13 Dec 02:40

X3.4

13 Dec 14:23

C2.2

13 Dec 18:25

C1.7

14 Dec 12:10

C1.014 Dec 16:49

C1.214 Dec 22:15

X1.5
E3

- Rapid direction changes in the IMF Bz component and the Solar Wind .

Ap:

563104
Delayedperturbation
across the region

Potential for
prediction

E1218100

1695

141290

1085

86(e)

80

4
275

345

346

347

348

349

Days(2006)

Storm dynamics:-Three interrelated causal mechanisms: mechanical,electrical and chemical.
-Competition among electric fields as well as
thermospheric dynamics and composition changes.

Complex (Incomplete knowledge)
www.ips.gov.auSimple(a) Solar wind speed, (b) interplanetary magnetic field Bz,(c) AE index, (d) Ap and Dst index and (e) ionosphericperturbation on 13–15 December 2006. The patches
indicate the initial phase of this storm.

Application of the approach (test cases): November 2004 Geomagnetic Storm

5-11 November 2004

E12

E3

100%variance

995

790

585

380

1310

311

312313314315316

Days(2004)-The largest components(PC1 and PC2)
-Capture the slow-varying trends- Primarily deterministic-Lower component (PC3)- short-term variation
Comparison of daily Ap index (top), change in the geomagnetic
field’s horizontal component at Canberra (middle) and TEC
perturbation (bottom) over the Australian region.

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Conclusion:
A new Ionosphere Disturbance Index is established through the statistical
Eigen mode analysis based on GPS measurements with great practicalvalue in operational applications.



A regional dynamic approach with high temporal and spatial resolution
sufficient to meet the need of the ionosphere variability study and thedemand of practical application like the GNSS-based navigation andpositioning.

Potential for prediction and related performance changes in GNSS
applications using early space weather information.

The next step of this research is to translate the ionospheric parametersdescribing the perturbation degree into users terms.

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