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

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

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

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 2011 to 2013 : Observation of the scintillations in West Africa Correlation with positioning anomalies Investigate means to forecast the scintillation Work in 2014-2015: 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

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

Schematic impact OK OK OK NOK OK Ionosphere NOK GPS receiver

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

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)

Ionospheric scintillations Yellowknife, 29 Oct. 2003 (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

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

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 +/- 15-20° 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

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

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

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

Time distribution of S4 Important : scintillation intensities are not lower at the solstices: they are less frequent Example for Pointe-Noire : In October 2011 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

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

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.

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

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

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

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

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

1-6 hours forecast results For all forecast ranges between 1 and 6 hours, the performances for a +/- 0.15 interval of S4 vary from 79% to 85% for daily maxima predictions.

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

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

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.

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

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.

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.