1. European Space Weather Week – ESWW#14
Northern scintillations characteristics and impact on SBAS behavior: EGNOS case
R. Chaggara(*), C. Paparini (*), U. Ngayap(**), B. Duparc(*),
S.M. Radicella(***), L. Ciraolo(***)
(*) European Satellite Services Provider (ESSP-SAS)
(**) ABBIA GNSS Technologies
(***)ICT4D Laboratory / International Center for Theoretical Physics (ICTP)
European Space Weather Week – ESWW#14
27th November – 1st December 2017
Ostend, Belgium
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ESSP-MEMO-20929

2. Table of Contents Introduction Space Weather and EGNOS
RIMS behaviour and Space Weather
AATR: time and space correlation analysis
Final considerations
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3. Ionospheric activity at different latitudes
North (~above 60N)
impact of geomagnetic activity
Auroral scintillations
Middle latitudes (30-60N)
South (~below 35N)
Higher values of TEC/high TEC gradients
Equatorial scintillations
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4. Space weather and EGNOS performance (1)
Impact on the availability of GPS/GEO
measurements and data
Impact on ground station measurements
quality and validity (Typ. Scintillations)
Triggering of barriers and consistency checks
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5. Space weather and EGNOS performance (2)
Northern Scintillation is the phenomena that impacts EGNOS performance the most (in terms of occurrences)
Main observation consists on the loss of GPS L2 signal on many high latitude ground stations.
The lack of observable deprives the processing facility from the computation of the ionosphere delay with consequence on both ionosphere and satellite monitoring.
IGP monitoring is more concerned than SV monitoring which is only (slightly)impacted during severe episodes.
Increased phase noise (C/No) that will lead to increased noise in the results.
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6. Space weather and EGNOS performance (3)
SBAS has to remain safe under disturbed ionosphere conditions: Needs for barriers at different system levels.
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7. Northern latitudes stations reception quality is mainly affected during high geomagnetic activity: increased L2 signal loss
Relationship between geomagnetic activity and the average rate of L2 losses for some northern stations located above 69°N (Svalbard, Jan Mayen, Kirkenes and Tromsoe) since 2014
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8. In-house ionosphere parameters computations
Slant TEC (Total Electron Content) can be computed from the integral path of the Ne  (Electron density - electrons/m3), and S is the distance along the propagation path (m).
Extracted from RIMS stations
(TEC Cal. Technique)
measure of the slant TEC in the ionosphere along the propagation direction of the beam.
𝑇𝐸𝐶= 𝑧 𝑁𝑒 𝑑𝑠 (TECU)
𝑇𝐸𝐶= ( 𝑓1 𝑓2 𝑓1 −𝑓2 ) (P2-P1)
ROT (Rate of change of TEC) and ROTI (ROT index)
𝑅𝑂𝑇= 𝑆𝑇𝐸𝐶(𝑘+1)−𝑆𝑇𝐸𝐶(𝑘) 𝑡𝑖𝑚𝑒(𝑘+1)− 𝑡𝑖𝑚𝑒(𝑘)
𝑅𝑂𝑇𝐼= < 𝑅𝑂𝑇 2 >− <𝑅𝑂𝑇> 2
Ref. X. Pi, A. J. Mannucci, U.J. Lindqdwister, C.M. Ho, “Monitoring of Global Ionospheric Irregularities using the worldwide GPS,” Geophysical Research Letters, 24, (18), Doi/ /97GL
𝐴𝐴𝑆𝑅𝑖= ∆𝑆𝑇𝐸𝐶 ∆𝑡
AATRi = ∆𝑆𝑇𝐸𝐶 (𝑀 𝜀 ) 2 ∆𝑡 = 𝐴𝐴𝑆𝑅𝑖 (𝑀 𝜀 ) 2
AATR = 1 𝑁 𝐴𝐴𝑇𝑅𝑖 2
Calculated
AATR (Arc-Along TEC Rate)
Ref. J.M. Juan, J. Sanz, M. Hernandez-Pajares, R. Prieto and S. Schlueter. “New Indicator for Definition of Ionospheric Operational Conditions”. SBAS IONO Working Group. Bath (UK), July 2013.
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9. AATR/L2 Loss probability evolution Tromso Station
2014
AATR/L2 Loss probability evolution Tromso Station
for February/March 2014
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10. AATR/L2 Loss probability evolution Tromso Station
2014
AATR/L2 Loss probability evolution Tromso Station
for October/November 2014
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11. 2015
AATR indicator evolution TRO - 15th/16th/17th/18th/19th/20th/21st/22nd/23rd March 2015
17/03/2015
19/03/2015
18/03/2015
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12. AATR indicator evolution on 17th March 2015 RIMS LYR/KIR/TRO/EGI/RKK
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13. Space weather vs EGNOS performance
Typical impact on APV-1 Availability (St. Patrick Storm March 2015)
March
March
March
March
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14. Tromso Station on 17th March 2015
C/No time evolution
Tromso Station on 17th March 2015
TRO - PRN 14
TRO - PRN 20
TRO - PRN 31
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15. Ground stations behavior analysis
High correlation between AATR parameters and RIMS L2 tracking capability has been observed
Other factors such as C/N0 drive the risk of loosing the signal tracking (mainly L2 signal).
AATR between 0.8 and 1 could be considered as harmful to ground station tracking performance.
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16. AATR correlation analysis (1)
For some stations, it has been observed that individual AATR evolution exhibits large day to day variation (regardless the level of SW disturbance)
Auto-Correlation based analysis provide more refined characterization of the experienced ionosphere conditions.
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17. AATR correlation analysis
The goal is to characterize the AATR behavior in terms of auto-correlation to better understand the ionosphere variability.
Different ground stations has been investigated to reflect the latitude dependency
The auto-correlation is computed over 24 hours as the following
𝑅(𝑘)= 1 𝑁 𝑛=1 𝑁 𝐴𝐴𝑇𝑅 𝑛 . 𝐴𝐴𝑇𝑅(𝑛+𝑘)
𝑅 𝑁 (𝑘)= 𝑅(𝑘) ⁡𝑚𝑎𝑥 (𝑅)
𝑅 𝑁 stands for the Normalized Auto correlation
AATR function computed each 30 seconds
N reflects the number of AATR samples over 24h =2880 samples
AATR values for two days are used to produce the daily auto-correlation
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18. AATR correlation analysis
The shape of the auto correlation function shows high consistency for Toulouse station (AATR derived mainly by sun exposure)
Situation changes dramatically with Tromsoe station (the AATR shows quite unpredictable behavior)
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19. AATR correlation analysis (2)
Average 24 hours autocorrelation was computed over one year (2014) data for four stations
Toulouse (43N, 1 E)
Gavle (60N, 17 E)
The 95% and 5% envelopes provide indication on the autocorrelation discrepancy
Results for High latitude suggests high discrepancy with
Flat (near-zero) average auto
correlation
High latitudes AATR shows higher variability than for mid-latitudes
Time of day dependency is more pronounced for mid-latitude stations
Tromsoe(69N, 18 E)
Longyearbyen (78N, 15 E)
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20. AATR correlation analysis (3)
Time period over which the AATR auto correlation level remains higher than 0.8 (and 0.5) with 95% confidence has been estimated
Station
Correlation above 0.8
Correlation above 0.5
Toulouse
41 minutes
1 hour and 25 minutes
Gavle
26 minutes
50 minutes
Tromsoe
Longyearbyen
21 minutes
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21. AATR correlation analysis (4)
Cross-correlation between different stations has been also computed (on month data)
Mid-Latitude stations shows quite moderate to high cross-correlation
Extreme north station (LYR 79N) exhibits low cross-correlation with other stations.
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22. Final considerations
Insight on EGNOS ground station response to northern scintillations has been discussed.
Northern stations are the most impacted with disturbance observed out of the Solar cycle episode.
High correlation between AATR parameters and RIMS L2 tracking capability has been observed.
Based on correlation analysis; it is confirmed that the ionosphere conditions show in general high time and space variability over high latitudes (above ~65N).
This study confirms that ionosphere prediction over these locations is much more complex than from mid-latitudes.
NO integrity issue either on pseudorange or in the user domains have been observed nor reported for all the events.
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23. thank you! www.essp-sas.eu claudia.paparini@essp-sas.eu
(H24/7)
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