Ionosphere Prediction Service for GNSS Users

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1 Ionosphere Prediction Service for GNSS Users International Technical Symposium on Navigation and Timing Filippo Rodriguez, PhD, Telespazio 15th November 2018

2 Introduction Monitoring and forecasting of Space Weather is a complex and very important business in order to predict and mitigate the potential and disruptive effects; The IPS objective is to provide Monitoring, Predictive and Warning capabilities to mitigate the impacts of these disruptive events with a focus on the operations of several GNSS based application domains; The Ionosphere Prediction Service (IPS) project is developed in the framework of the Galileo Programme (funded by the European Union's R&D programme Horizon 2020); The project team is composed of: Telespazio (coordinator); Nottingham Scientific Ltd; Telespazio Vega DE The University of Nottingham; The University of Rome Tor Vergata; The National Institute of Geophysics and Volcanology 2

3 User Requirement Survey User requirements survey was carried out to define the service requirements per each application (aviation, high accuracy, mass market, road charging); the most clear/mature requirements drove the choice of the GNSS applications supported by IPS prototype: Aviation community; High Accuracy community; User communities identified a clear need for services able to: Monitor the environment (Sun, atmosphere) to assess the status; Predict disruptive events with 72 hours in advance -> target considered unfeasible, 24 hours chosen coherently with the state of the art; Warn with enough time before the event takes place -> most of the requests focused on IP based interface (like REST etc.). Provide information to be used in the operational chain (PVT accuracy, PVT availability, etc). More workshops will be necessary to help users to identify how integrate at best the IPS services into their operational chain 3

IPS Architecture Sensors: the sources of phenomena observations and measurements RPFs Layer: federation of Remote Processing Facilities that processes collected input measurements to generate

4 IPS Architecture Sensors: the sources of phenomena observations and measurements RPFs Layer: federation of Remote Processing Facilities that processes collected input measurements to generate nowcasting and forecasting products CSPF Layer: central storage and distribution unit. User Interface: webservice application that provides users with ad hoc views of the phenomena through the generated products 4

Sources of Input Data IPS is designed to operate in real time processing data

the raw data to feed the algorithms: GNSS Raw Data: Public data providers like

(scintillation receiver ISMR) copyright https://gong.nso.

org/network Solar Physics Related Data Space solar observatories (SOHO, GOES etc.

5 Sources of Input Data IPS is designed to operate in real time processing data coming from external sensors; Many and eterogenous external sources to provide the raw data to feed the algorithms: GNSS Raw Data: Public data providers like IGS, EUREF and ESA-EDAS National networks (INGV RING) Special networks (scintillation receiver ISMR) copyright copyright Solar Physics Related Data Space solar observatories (SOHO, GOES etc.) Terrestrial sensor networks (magnetographs, H-Alpha sensors etc.) Telescopes (MOTH) copyright nasa.gov/gallery/spacecraf t/soho_photo2.html 5

IPS added-value: IPS Overview Near-real-time and forecast (30' and 24 hours) of ionosphere and solar events translated into GNSS user metrics (loss of lock,

quickly access the space-weather conditions ("console" approach for operational monitoring); Send to the user alerts messages on the trend of a monitored

6 IPS added-value: IPS Overview Near-real-time and forecast (30' and 24 hours) of ionosphere and solar events translated into GNSS user metrics (loss of lock, tracking errors); More than 160 products are available Direct access to the physical quantity or data ('downloads'); Easy configuration of the monitors to quickly access the space-weather conditions ("console" approach for operational monitoring); Send to the user alerts messages on the trend of a monitored quantity ('notifications', based on user-defined thresholds); Validation of the forecast: dedicated function to assess the goodness of the forecast products. 6

RPFS Description RPF1 - Solar Activity Related Products Monitoring and prediction of solar events

scientific payloads, like GOES X, SOHO, MOTH telescope etc.

and global level; Input data: several GNSS reference stations data (e.g. IGS) and scintillation data (e.

7 RPFS Description RPF1 - Solar Activity Related Products Monitoring and prediction of solar events (flares, CME and solar energetic particles (SEP) linked to CME); Input data: several sensors and scientific payloads, like GOES X, SOHO, MOTH telescope etc. RPF2 - Ionospheric Activity Related Products Nowcast and forecast of TEC and scintillation at regional and global level; Input data: several GNSS reference stations data (e.g. IGS) and scintillation data (e.g. ISMR, RING networks). RPF 1 and 2 provide scientific information per se but the main objective is to provide event forecasting for RPF-3 and RPF-4. 7

RPFS Description RPF3 / RPF4 - GNSS User Receiver and Service Related Products RPF 3 is dedicated to high accuracy users; RPF 4

Augmentation System (SBAS)): SBAS nowcasting at high frequency (5') is implemented for the provision of the forecast in the future

They both take as input the ionosphere estimation provided by RPF2 and provide local, regional and global level products.

8 RPFS Description RPF3 / RPF4 - GNSS User Receiver and Service Related Products RPF 3 is dedicated to high accuracy users; RPF 4 provides nowcasting and forecasting of aviation related performance (Aircraft Based Augmentation System (ABAS), Satellite Based Augmentation System (SBAS)): SBAS nowcasting at high frequency (5') is implemented for the provision of the forecast in the future evolution of IPS. They both take as input the ionosphere estimation provided by RPF2 and provide local, regional and global level products. Other relevant IPS functions are: Statistical analysis. Generation of statistical parameters on the basic IPS products (moments, Probability Density Functions, CDF, etc.); Forecast retro-validation. comparison between the past forecast analysis and the corresponding actual value. 8

9 Nowcasting example 7-9 September 2017 (G4 Storm CME, max Kp 8+) Sequence of IPS nowcasting scintillation products (Slant Sigma Phi). The top left is the status at 23:45, top right at 00:00 and the last is the status at 00:15 Statio n PRN IPPLAT (deg) IPPLON (deg) Slant Sigma Phi (deg) Loss of Lock Prob (%)

instant t* : summation over all grid points of the nowcasted minus Forecast

10 Forecasting example G4 Storm (CME) Geomagnetic index Kp 8+ Short term forecast (regional) RGEC (Global Electron Content - Residual) at a given instant t* : summation over all grid points of the nowcasted minus Forecast TEC values The RGEC parameter is an adaptation of the GEC proposed by Afraimovich [6]. 10

11 RPFS Algorithms The main stringent requirement for IPS development is real-time processing: avoid long periods of processing for a good user experience and for a timely provision of warnings; To speed up : estimation, interpolation and learning techniques; E.g. forecasting: RPF 1 algorithms: CME forecasting based on models (i.e. Drag Based Model (DBM)) with extension to SEP; RPF 2 algorithms: TEC forecasting based on machine learning techniques; RPF 3 algorithms: Parameters of a closed form representation of the tracking error and position error estimated through the generalized linear modelling (GLM); RPF 4 algorithms: the forecasting based on the mixed use of models (orbit and clocks) and prediction of the ionosphere (coming from RPF 2): Pseudorange models computed for each element of a grid; Navigation algorithms are computed in real time for each point and for each epoch. 11

12 Products Validation The objective of the validation task is to demonstrate that the products generated by the IPS prototype are issued with a sufficient confidence level; The validation has been carried out through two different strategies. The first approach (offline): Offline comparison of the IPS nowcasted and forecast products against past external (i.e. coming from other services) products; It is a comparison with trusted references like IGS, ROB etc. The second approach (real-time): Based on the so called retro-validation : measure in real-time of the distance between the forecasting and the actual nowcasting of a specific product; The historical sequence of retro-validation products can be used to update day by day a statistical characterization of the behavior of the service 12

13 Validation: Flare, CME and SEP Flare forecasting goodness evaluation based on the so-called Confusion Matrix (consistency table) whose elements report the comparison between the 24-hours M-class and X-class flare predictions against the observed events in terms of normalized probabilities; M Flares X Flares TP = 0.0 FP = 0.03 TP = 0.0 FP = FN = 0.0 TN = 0.97 FN = 0.0 TN = Validation of CME with reference to Time of Arrival (ToA) on Earth: Set of hystorical data as benchmark (ref Shi, et al. (2015). Predicting the arrival time of coronal mass ejections with the graduated cylindrical shell and drag force model. Computation of hystogram of the differences between the predicted and archived ToA and its gaussian fitting; ToA residual dispersion is perfectly inside the expected accuracy of the prediction model (60% of cases predicted within +/- 10 hours). Validation of SEP based on the same approach of the CME. In this case the reference value is the number of measured energetic particles (> 10 MeV): The forecast fluxes are consistent with the measured SEP fluxes, within the statistical errors; We lack enough high-flux SEP events to satisfactorily evaluate the forecast performance. 13

14 Validation: Vertical TEC Global and regional nowcasting: Direct comparison with external reference products; Selection of specific events for different geomagnetic indices; Statistical characterization of the differences (all the values are in TECu): Global compared to UPC -> -1.6 mean -0.9, 4.1 σ 5.3 Regional (Europe) compared to ROB -> -0.3 mean 0.1, 1.3 σ 1.5. Short and long term forecasting: Once the nowcast has been validated, the result is compared to the Universitat Politècnica de Catalunya (UPC) products for short term and long term global products and with ROB for short term regional; This process has been performed on the same previous events; Statistical characterization of the differences (all the values are in TECu): Short term regional (Europe) compared to ROB -> -0.3 mean 0.3, 1.7 σ 1.9; Short term global compared to UPC -> -1.7 mean -1.0, 4.1 σ 5.3; Long term global compared to UPC -> -5.0 mean -3.0, 2.8 σ

15 Validation: User Receiver Performances Validation for receiver estimated parameters tracking error, loss-of-lock and positioning error due to scintillation; Stations used for validation: European high and middle latitudes for regional products; European/North American high and middle latitude Brazilian low latitude for global products; Implemented approach for nowcasting: Retrieve of real scintillation parameters (S4 and σ Φ ) from real stations and fed into models; Comparison between modelled trend of parameters and real behavior of the receivers: Loss of lock R (correlation values between what observed and what estimated) are all close to 1 i.e. proper recover of the variability observed in the probability of loss of lock; Positioning error RMSE < 10 cm; Tracking error RMSE values are all below 0.1 mm (< precision of the GPS L1 carrier phase measurements, 2mm). Implemented approach for long term forecasting: Use the outputs of RPF 2 (ROTI) and fed into models; comparison between modelled trend of parameters and real behavior of the receivers: Loss of lock R values all close to 1 Positioning error RMSE values all below 10 cm (within the expected accuracy of PPP solution); Tracking error RMSE values all below 0.1 mm (< precision of the GPS L1 carrier phase measurements, 2mm). 15

16 Validation: GNSS Service (Nowcasting) Comparison of the forecast results with nowcast information (at the same epochs and computed by using real GNSS receivers); Reference nowcasting product: Telespazio Galileo Service Operation (GSOp) User Terminal validated in the context of the GSOp program - currently running at Fucino Galileo Control Center for the monitoring of the Galileo navigation performances: Limitation of the approach: different algorithm configuration options may produce slightly different output results (tropospheric models, outlier detection algorithms, solution weighting schemes); Results show a substantial correspondence. Example: Ionospheric Event September 2017 EUREF Station Position Error 95% accuracy Mean Position Error Maximum Position Error Algorithm 1 Algorithm 2 Algorithm 1 Algorithm 2 Algorithm 1 Algorithm 2 KIR0 (Kiruna, SW) 6.59 m 6.80 m 3.35 m 3.58 m m m BRST (*) (Brest, FR) 4.68 m 4.40 m 2.41 m 2.34 m 8.90 m 9.01 m LPAL (Canary Islands, ES) 8.07 m 8.80 m 4.03 m 5.27 m m m (*) Calculated with cycle-slip detection algorithm enabled Positioning Error Reported for 3 GNSS Stations at Different Latitudes 16

17 Validation: GNSS Service (Forecasting) Comparison between performance at station level with position error forecast maps of grid points: Limitations of the approach: environmental conditions cannot be taken into account; First results: difference in the order of 1-2 meters for some network stations presumably depending upon: Error in forecast ionospheric maps; Precise orbit and clocks Under-modelled user error sources (dominant effect) e.g. signal obstructions due to the presence of reliefs, effect of multipath, etc.; Receiver noise and related intrinsic technical specifications, model, type and age. 17

18 Operational Phase: Possible Issues The IPS operations started on July 2018 and will last 6 months: IPS can be reached at Your feedback is welcome! The goal of IPS it the development and deployment of a service prototype with no defined SLA IPS presently is operated with some limitations (cold backup, 5/7 D, 8/24 H modality, relaxed time to react); Other potential issues: availability and continuity of the input data sources: IPS products are partially based on public third parties networks and data; The quality and availability of the data may impact the quality and availability of IPS products; 18

19 Conclusions The result of the project is a prototype of the IPS service: The originality of IPS is the capacity to provide info specifically targeted to the GNSS users; First step for a future integration in the GSC to broadcast notifications to registered users; Next steps and improvements: Collection of user feedbacks for a preliminary design and future improvement of products offering and web presentation; coverage extension over critical regions (high latitude and equatorial regions); Improvement of the knowledge of the solar phenomena and its interaction with the Earth atmosphere; Extension of the forecasting of GNSS performance to different applications. 19

20 THANK YOU FOR YOUR ATTENTION

Montréal, 7 to 18 July 2014

Montréal, 7 to 18 July 2014 INTERNATIONAL CIVIL AVIATION ORGANIZATION WORLD METEOROLOGICAL ORGANIZATION MET/14-WP/15 16/4/14 Meteorology (MET) Divisional Meeting (2014) Commission for Aeronautical Meteorology Fifteenth Session Montréal,