1. CLS (Collecte Localisation Satellites)
Regional forecast of ionospheric scintillation dedicated to offshore operators
Ph. Yaya, L. Hecker
CLS (Collecte Localisation Satellites)
Toulouse, FRANCE

2. Summary Introduction Ionospheric scintillation phenomenon
Scintillation characteristics in West Africa
An ionospheric dedicated network
Time distribution of scintillations
An improved model based on measurements
Main principles
Performances of local forecast
Pilote sites of the service
Future steps

3. Summary Introduction Ionospheric scintillation phenomenon
Scintillation characteristics in West Africa
An ionospheric dedicated network
Time distribution of scintillations
An improved model based on measurements
Main principles
Performances of local forecast
Pilote sites of the service
Future steps

4. Introduction
Observation: some positioning processing based on GNSS may be degraded, sometimes very severely, on recurrent moment of the day
Consequences: very important financial issues for off-shore applications.
A consortium (CLS, Fugro, Telecom B.) worked under a CITEPH funding from to 2013 :
Observation of the scintillations in West Africa
Correlation with positioning anomalies
Investigate means to forecast the scintillation
Work in : development of a model based on the long-term observations (few years) and the very last observations (few hours)  an operational service is proposed.
More than 10 m of error
Fugro-Topnav

5. Introduction PPP processing with CNES/GINS software on NKLG
S4 modelization near Libreville (Gabon)
PPP processing with CNES/GINS software on NKLG
IGS station (Libreville), from 13 to 21 March 2014:
Interval: 30s
Constellation: GPS only
Mode: undifferenced ambiguity resolution
Cutoff : 15 deg
Corrections: none (ocean or atmospheric tides,…)
10 m
10 m
10 m

6. Schematic impact OK OK OK
NOK OK
Ionosphere
NOK
GPS receiver

7. Summary Introduction Ionospheric scintillation phenomenon
Scintillation characteristics in West Africa
An ionospheric dedicated network
Time distribution of scintillations
An improved model based on measurements
Main principles
Performances of local forecast
Pilote sites of the service
Future steps

8. Vizualisation of scintillation bubbles
The origin of the scintillation phenomenon is an instability of Rayleigh-Taylor in the E layer (100 km) at the sunset. These instabilities are sub-ionized structures which can elevate up to 1400 km.
Numerical simulation of bubbles The color code corresponds to an increasing ionization density with the altitude (Huton, 2008)
Observations of bubbles : Structures measured at Jicamarca (9°N) : the altitudes are between 400 and 1400 km before 21:00 LT. On GPS signal there are decreases of TEC and scintillations (Valladares,2005)

9. Ionospheric scintillations
Yellowknife, 29 Oct (Lassudrie, Fleury)
When the environment crossed by a radioelectric signal is homogeneous, its phase and amplitude are regular and predictable.
But when the signal crosses a medium where the electronic density is not constant, both amplitude and phase are affected by rapid fluctuations that may last up to a few hours

10. Quantification of degradation
Ascension, 27 March 2000 (Groves,2004)
Amplitude
Phase
 Define standardized indices
Normalized standard-deviation of the signal intensity (amplitude)
Standard deviation of the signal phase
S4 index gives the level of perturbation on the signal amplitude
sf index gives the level of perturbation on the signal phase

11. Climatological models
Climatological models (WBMOD and GISM) give global occurrence probabilities. They are not adapted to local phenomenon (in time and space) The accumulation of scintillation observations points out zones of high electronic density, the equatorial « crests » at +/ ° on each side of the equator.
Map of scintillation intensity
(WBMOD climatological model). IPS.
Map of scintillation intensity
(GISM climatological model). IEEA
North equatorial crest
South equatorial crest

12. Summary Introduction Ionospheric scintillation phenomenon
Scintillation characteristics in West Africa
An ionospheric dedicated network
Time distribution of scintillations
An improved model based on measurements
Main principles
Performances of local forecast
Pilote sites of the service
Future steps

13. Ionospheric Monitor Network
ISM = Iono. Scintillation Monitor = GNSS receiver robust to scintillations, and rated at 50 Hz (= frequency adapted to observe the scintillations, which have characteristic times of a few ms)
These monitors directly give S4 and sf.
Monitor installed in 2011 on Fugro bases on the Guinean Gulf.
Position of the ISMs w.r.t. the South equatorial crest :
Port-Gentil station (POG1) on the maximum
Pointe-Noire station (PNR1) is a bit lower
Lagos station (LOS1) is almost on the magnetic equator

14. Time distribution of scintillations
Data span : about 2 years and ½
Scintillation occurrence:
Depends on local time:
beginning at around19h LT,
ending at around 02h LT.
Depends on the season:
maximum at the equinoxes (Spring & Automn), with an earlier beginning (19h instead of 20h LT)
minimum at the solstices (even though the occurrences are a bit higher during Summer than during Winter)
Télécom Bretagne

15. Time distribution of S4
Important : scintillation intensities are not lower at the solstices: they are less frequent
Example for Pointe-Noire :
In October S4 is > 0.8 almost each day
In August 2012, there are also peaks of S4 > 0.8
This behavior cannot be reproduced with a climatological model
 Need for a model based on real time observation

16. Summary Introduction Ionospheric scintillation phenomenon
Scintillation characteristics in West Africa
An ionospheric dedicated network
Time distribution of scintillations
An improved model based on measurements
Main principles
Performances of local forecast
Pilote sites of the service
Future steps

17. Construction of the crest model
Data span: January 2013 to December 2014
Type of data:
S4 from the three ISM
S4 derived from GNSS receivers, computed from a Std Dev of SNR on L1.
Construction of the model:
Each grid point is computed as the 95th percentile of all data values
FFT low pass filter on the cumulated data leads to a model, which is then adjusted on the last hours in order to forecast the level of scintillation.

18. Forecast method
Every hour, the model is adjusted to the observations of the last 2 hours
Computation of an observed scale factor
The observed scale factor of the afternoon is correlated to the scale factor of the evening
Scale factor at 17 UT (blue) and 20 UT (red) over 24 months

19. Scale factor statistics
Based on 24 months statistics, a forecasted scale factor is computed for the next hours
With observations until 17UT, the scale factor for 20UT is predicted with an RMS of 0.24

20. Regional Forecast examples
Top : observations at the IPPs (Ionospheric Pierce Points)
Bottom : Model at same locations
NB: only the South
crest is considered

21. Local validation (ex. at Libreville)
A forecast can be given for any location covered by the model

22. Local validation: 3-h forecast results
The daily maximum is predicted 3 hours in advance with an RMS of 0.1

23. 1-6 hours forecast results
For all forecast ranges between 1 and 6 hours, the performances for a +/ interval of S4 vary from 79% to 85% for daily maxima predictions.

24. Combined model
Sinus fitting on the scale factor to derive a climatological model
 improvement of performances for few hours forecast

25. Summary Introduction Ionospheric scintillation phenomenon
Scintillation characteristics in West Africa
An ionospheric dedicated network
Time distribution of scintillations
An improved model based on measurements
Main principles
Performances of local forecast
Pilote sites of the service
Future steps

26. Pilote web sites
Put in place as a demo for some oil&gas companies NB. Many feedbacks from TOTAL that help us improving our model
Customer’s sites of interest
Hourly value of modelised index
(Observed on last 18h /
Prediction next 6 hours)
History of daily max.

27. Summary Introduction Ionospheric scintillation phenomenon
Scintillation characteristics in West Africa
An ionospheric dedicated network
Time distribution of scintillations
An improved model based on measurements
Main principles
Performances of local forecast
Pilote sites of the service
Future steps

28. Perspectives Tuning the model in order to: Improve the detection rate,
15 March, NKLG
Color : forecasted S4<0.6
Grey : forecasted S4 >0.6
Tuning the model in order to:
Improve the detection rate,
Decrease the false alarm rate.
Time forecast per satellite  towards a mitigation solution for positioning during scintillation events
Analyse the possibility to construct and apply a model for the South-East Brasilian area, as it is also affected by scintillations.

29. Conclusions
Ionospheric scintillations can severly affect the precision of GNSS-based position techniques (up to several meters during high scintillation events) Scintillations have a global well known behavior but they are very difficult to forecast at a local level. Thanks to ionosphere-dedicated instruments and classical GNSS receivers we propose a model to predict the level of amplitude scintillation (S4) in the Guinean Gulf. The model is adjusted on a few hours of near real time observations and is able to give the maximum level of S4 3 hours before the beginning of the eventual event with a confidence level of 0.15 on 83% of the cases (4% of false alarms, 13% of missed events) The performance can still be improved, and a prediction per satellite will be considered in the future. The same model will be tested for the South-East Brasilian coast.