An Early Warning Ionospheric Alert System using GPS
Observations at Guntur, India
1&2 D.Venkata
Ratnam, 1G. Sivavaraprasad,1 S .Lakshmi Narayana,
1M.Venu Gopala Rao and 1JRK Kumar
1Dept.
of ECE, K L University, Vaddeswaram, Guntur Dist, Andhra Pradesh, India.
2Dept. of Atmospheric Sciences, K L University, Vaddeswaram, Guntur Dist, Andhra Pradesh,
India.
E-mail: 1dvratnam@kluniversity.in
4th Feb ,2016
UIM 2016, NRSC, Hyderabad
Outline
• Introduction
• Significance of Ionospheric Forecasting
• Ionospheric TEC Estimation
• Ionospheric Forecasting Models
• Results & Discussion
• Conclusion
Introduction
• GPS (Global positioning system), GLONASS
and Galileo
• GAGAN (GPS Aided Geo Augmented
Navigation)
• Ground Based Augmentation Systems
• Indian Regional Navigation Satellite Systems
• Pseudolite Based Navigation Systems
Predominate error source- Ionosphere- India (Low Latitude region)
1 TECU would cause about 0.3 m of position error
Importance of an Early Warning
Ionospheric Alert System
Ionospheric ,
Solar and
Geomagnetic
Sensors Data
Analysis,
Modelling and
Prediction of
ionospheric
Data
Forecasting of
Ionospheric
Time delays
App/Web
based
Ionospheric
Space weather
Monitoring
system for
GNSS users
The spatial and temporal variations of the ionosphere were analyzed using the TEC
values derived from three Indian low-latitude GPS stations separated by 12-180 in
northern latitudes and 78-820 in East longitudes for high solar activity year 2013.
2/11/2016
S
4
Error budget of SBAS and GBAS/LAAS for category I landing (Pullen, 2000)
Error source
Segment
source
Space
Satellite clock
Satellite
perturbations
Other
(thermal
radiation, etc.)
Control Ephemeris error
Other
(thruster
performance, etc.)
User
Ionospheric delay
Tropospheric
delay
Receiver noise
Multipath
Other
(inter
channel bias, etc.)
Total
SBAS[1]
(m -1)
1 error
(m), before
correction is
applied
3.0
1.0
0.5
GBAS/LAAS
(m - 1)
0.64
0.21
4.2
0.9
5.0
1.5
0.24
1.5
2.5
0.5
1.5
2.5
0.5
1.5
2.5
0.5
8.0
3.04
2.96
GAGAN and Iono Model Concept
GPS
satellite
GEO
GPS
satellite
GPS
satellite
Broadcasts
1. Differential corrections
2. Ranging signal
3. Integrity information
6
Measured mean
UIPP delay
Estimated mean
UIPP delay
Mean UIPP error
Std UIPP delay error
5
July 06 2004
Users IPP Delay(m)
4
3
2
Ionospheric thin
shell
1
0
0
5
10
15
Local time (Hrs)
IGP delays,
GIVE
350Km
from
Ground
Estimates user’s
IPP delay, UIVE
INRES
INLUS
IPP
IGP
Ground
INRES
INMCC Indian Mission Control Center
INLUS Indian Land Uplink Station
INRES Indian Reference stations
INMCC
User’s IPP
IPP locations, IPP
measured delays
IPP locations, IPP
measured delays
20
25
International Status
•
ADVANCED FORECAST FOR ENSURING
COMMUNICATIONS THROUGH SPACE (AFFECTS)
 Development of the ionospheric forecasting
system over Europe.
Participants:
•
•
•
•
Institute for Astrophysics, University of Göttingen (DE)
Royal Observatory of Belgium (BE)
Space Research Institute of NASU and NSAU (UA)
Fraunhofer Institute for Physical Measurement Techniques
(DE)
•
Tromsoe Geophysical Observatory, University of
Tromsoe
•
•
•
German Aerospace Center, Neustrelitz (DE)
EADS Astrium (EU)
NOAA Space Weather Prediction Center (US)
Space weather alert and Apps are developed by
AFFECTS research group
Official website: http://www.affects-fp7.eu/
KL University GNSS stations



Koneru Lakshamaih University (Geographic 16.31N, 80.37E)
Vaddeswaram, that falls under the transition zone between the
equatorial trough and the anomaly crest in Indian region.
Model : GPSstation 6, Novatel, Canada
KLU GNSS network consists of four permanent ionospheric
monitoring systems at Guntur, Machilipatnam,Bapatla and
Narsapur with spacing of 100 kms along Bay of Bengal sea coastal
belt.
http://igscb.jpl.nasa.gov
GNSS S TEC Data Used
20o
N
18oN
Latitude (Degrees)
*Hyderabad
*KLU Guntur
16oN
14oN
*Bangalore
12oN
10oN
8 oN
72oE
75oE
78oE
Longitude (Degrees)
81oE
o
84 E
S.No Station Name Geographical
Geographical Longitude Year
Latitude in degrees N in degrees E
data
1
2
3
Guntur
Hyderabad
Bangalore
16.37
17.41
13.0212
80.37
78.55
77.57
2013
2013
2013
of
Comparison of GPS Observations with IRI Model Predicted Observations in 2013
40TECU
45TECU
Jan,2013
55TECU
Feb,2013
Mar,2013
Winter
Equinox
VTEC(TECU)
Winter
45TECU
60TECU
55TECU
April,2013
Equinox
May,2013
Summer
June,2013
Summer
Local time (Hrs)
Fig: Seasonal variation of GPSTEC over low latitude station, KL University,Vaddeswaram, India.
 During the high solar activity year 2013 (sun spot number 70) the maximum
values of TEC are observed from 55 to 75 TECU during the period from 1st Jan
to 30th June.
 The TEC values are high in equinoctial months than the TEC values during
winter and summer seasons is due to the seasonal variation of TEC is directly
controlled by thermospheric neutral compositions.
11
During the day time, the equator is hotter than the poles, therefore
meridional winds flows from equator towards pole.
The flow of meridional wind O/N2 ratio increases at equatorial and
low latitude stations. This increase is maximum in equinox.
At 350km altitude (F2 layer) N2 dissociation is the major process
which removes ambient electrons.
Hence the increase in O/N2 ratio (N2 decreases, i.e., loss decreases)
will result in higher electron density (TEC) and therefore in
equinoctial months TEC will be highest.
TEC diurnal variation is larger for winter months (January) than that of
the summer months (May) is due to the winter anomaly.
Winter anomaly effect is due to the seasonal changes in neutral gas
composition.
12
Geomagnetic storm 1 (March 17, 2013)
June 29, 2013
Storm day
-92nT
-13nT
-98nT
70TECU
60TECU
26-07-2013
27-07-2013
28-07-2013
29-07-2013
26.87TECU
30-07-2013
2.472TECU
24.9TECU
Before the storm day at 12:00LT Dst index reaches to -13nT and TEC to 26.87TECU
at 24:00LT Dst to -92nT(Initial phase).
 TEC Enhancement- Positive storm.
The prompt penetration of electric field is eastward
13
Geomagnetic storm 1 (March 17, 2013)
DST Index
DST Index Values (nT)
100
50
0
-50
-100
-150
0
2
4
6
8
10
12
14
16
18
Number of Days
Hourl y Forecasti ng
100
VTEC Values (TECU)
Original Values
ARMA model
IRI-07 model
IRI-12 model
50
0
0
2
4
6
8
10
12
14
16
18
Number of Days
DST Index variations along with the original and comparison between forecasted VTEC values
and predicted IRI – 07 & 2012 values
Geomagnetic storm 1 (March 17, 2013)
Error i n Forecasti ng
20
ARMA model
IRI-07 model
IRI-12 model
15
Obsereved Error (TECU)
10
5
0
-5
-10
-15
Prestorm Day
Post storm Day
Storm Day
-20
0
0.5
1
1.5
2
2.5
Number of Days (forecasted)
Forecasted error of ARMA compared with error of IRI – 07 & IRI -2012 model
3
Geomagnetic storm 2 (June 29, 2013)
15nT
March 17, 2013
-88nT
Storm day
-106nT
70TECU
62TECU
59.81TECU
5.56TECU
14-03-2013
15-03-2013
16-03-2013
17-03-2013
18-03-2013
 At the time of commencement of storm a pulse of increment in Dst
index to15nT at 06:00LT & TEC reaches to 47.01TECU, then starts
decreasing from 07:00LT.
 The disturbance dynamo electric field as against the prompt penetration field is
westward whereas daytime ionospheric dynamo electric field is in eastward
direction.
This causes suppression of EIA and the consequent TEC depletion
TEC depletions-Negative storm
16
Geomagnetic storm 2 (June 29, 2013)
DST Index
DST Index Values (nT)
100
50
0
-50
-100
-150
0
5
10
15
20
25
30
Number of Days
Hourly Forecasting
100
VTEC Values (TECU)
Original Values
ARMA model
IRI-07 model
IRI-12 model
50
0
0
5
10
15
20
25
30
Number of Days
DST Index variations along with the original and comparison between forecasted VTEC values
and predicted IRI – 07 & 2012 values
Geomagnetic storm 2 (June 29, 2013)
Error i n Forecasti ng
15
ARMA model
IRI-07 model
IRI-12 model
10
Obsereved Error (TECU)
5
0
-5
-10
-15
-20
Prestorm Day
0
0.5
Post storm Day
Storm Day
1
1.5
2
2.5
3
Number of Days (forecasted)
Forecasted error of ARMA compared with error of IRI – 07 & IRI -2012 model
Analysis of Ionospheric Time Delays
Forecasting Methods
Analysis of Ionospheric Time Delays
Forecasting Methods(cntd.,)
Actual Forecast Error of Methods (m)
10
ARIMA Method
HW-Additive Method
HW-Multiplicative Method
5
0
-5
1
50
100
150
200
No.of Days
250
300
350 365
Performance Evaluation of Early Warning
Ionospheric Forecasting Models
Multiple Forecast Models MAPE error measurements over 2013 year
MAPE (%)
60
Additive
40
Multiplicative
20
0
ARIMA
1
2
3
2
3
4
5
6
7
8
9
No.of Months (Jan-Dec,2013)
Multiple Forecast Models MAE error measurements over 2013 year
10
11
12
4
5
10
11
12
MAE(m)
1
0.5
0
1
6
7
8
9
No.of Months (Jan-Dec,2013)
Multiple Forecast Models MSD error measurements over 2013 year
MSD(m)
2
1
0
1
2
3
4
5
6
7
No.of Months (Jan-Dec,2013)
8
9
10
11
12
GPS-TEC and SSN for the first EOF associated coefficient
2/11/2016
22
Coherence wavelet spectrum between three stations observation data
and the Dst index
2/11/2016
23
Proposed Ionospheric Warning System
Early
warning
L1warning
• Detection and estimation of solar transient event
• 1-2 days before arrival at Earth
• Measurement of solar transient event near the lagrangian point L1
• Approx. 30 minutes before arrival at Earth
• Forecast of ionospheric Perturbations/TEC
Forecast- • Local Forecast
warning • A few hours before ionospheric perturbations
• Measurement of ionospheric perturbations (Scintillations, TEC
gradients, flares,…. )
Ionosphe
ric-Alert • Near real time alert
Ref: http://elib.dlr.de/89916/1/085.Borries.pdf
Conclusion
• A novel method of approach was introduced for early warning ionospheric
alert systems using GPS observations through regression and statistical
methods.
• The performance of early warning methods to alert ionospheric delays (1hahead) over low latitude Guntur station, India has evaluated.
• It was seen that the forecasting results were in good agreement with
diurnal variations of annually observed ionospheric GPS observations.
• The forecasting accuracy of analysed methods was in the range of 53-85%
and Holt-Winter multiplicative method is 2% more accurate than other
models.
• In future, multiple GPS stations data from different geographical locations
will be considered for testing the forecasting capability of ARMA along with
ARIMA and Holt-Winter models under nominal and sever geomagnetic
storm conditions.
• The outcome of these results would be helpful in the process of shielding
the communication navigation systems from adverse space weather events.
• Ionospheric forecassting models will be implemented with ionospheric TEC,
gradients and other solar, geomagnetic indices as input parameters.
Contact: Dr.D .Venkata Ratnam
dvratnam@kluniversity.in,
ratnam2002v@gmail.com.
TEC (Total Electron Content)

Total number of free electrons present in a unit cross
section area.

Time delay directly propositional to the TEC and inversely
proportional to square of the frequency.
Ionospheric Effects on the CNS Applications