ionospheric forecasting models

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An Early Warning Ionospheric Alert System using GPS Observations at Guntur, India 1&2 D.Venkata Ratnam, 1 G. Sivavaraprasad, 1 S .Lakshmi Narayana, 1 M.Venu Gopala Rao and 1 JRK Kumar 1 Dept. of ECE, K L University, Vaddeswaram, Guntur Dist, Andhra Pradesh, India. 2 Dept. of Atmospheric Sciences, K L University, Vaddeswaram, Guntur Dist, Andhra Pradesh, India. E-mail: 1 dvratnam@kluniversity. in 4 th Feb ,2016 UIM 2016, NRSC, Hyderabad

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Page 1: Ionospheric Forecasting Models

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: [email protected]

4th Feb ,2016

UIM 2016, NRSC, Hyderabad

Page 2: Ionospheric Forecasting Models

Outline• Introduction

• Significance of Ionospheric Forecasting

• Ionospheric TEC Estimation

• Ionospheric Forecasting Models

• Results & Discussion

• Conclusion

Page 3: Ionospheric Forecasting Models

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

Page 4: Ionospheric Forecasting Models

Importance of an Early Warning Ionospheric Alert System

Ionospheric , Solar and

Geomagnetic Sensors Data

Analysis, Modelling and Prediction of ionospheric

Data

Forecasting of IonosphericTime delays

App/Web based

IonosphericSpace weather

Monitoring system for GNSS users

S2/11/2016 4

The spatial and temporal variations of the ionosphere were analyzed using the TECvalues derived from three Indian low-latitude GPS stations separated by 12-180 innorthern latitudes and 78-820 in East longitudes for high solar activity year 2013.

Page 5: Ionospheric Forecasting Models

Segment

source

Error source 1 error

(m), before

correction is

applied

SBAS[1]

(m -1)

GBAS/LAAS

(m - 1)

Space Satellite clock 3.0

Satellite

perturbations

1.0

Other (thermal

radiation, etc.)

0.5 0.64 0.21

Control Ephemeris error 4.2

Other (thruster

performance, etc.)

0.9

User Ionospheric delay 5.0

Tropospheric

delay

1.5 0.24

Receiver noise 1.5 1.5 1.5

Multipath 2.5 2.5 2.5

Other (inter

channel bias, etc.)

0.5 0.5 0.5

Total 8.0 3.04 2.96

Error budget of SBAS and GBAS/LAAS for category I landing (Pullen, 2000)

Page 6: Ionospheric Forecasting Models

IPP locations, IPP measured delays

IPP locations, IPP measured delays

350Km

from

Ground

INRES

INRES

INMCCIPP

IGP

Ground

GPS satellite

GPS satelliteGEO

IGP delays,GIVE

User’s IPP

Estimates user’s IPP delay, UIVE

Broadcasts1. Differential corrections2. Ranging signal 3. Integrity information

Ionospheric thin shell

INLUS

GAGAN and Iono Model Concept

INMCC Indian Mission Control CenterINLUS Indian Land Uplink StationINRES Indian Reference stations

GPS satellite

0 5 10 15 20 250

1

2

3

4

5

6

Local time (Hrs)

Users

IP

P D

ela

y(m

)

Measured mean

UIPP delay

Estimated mean

UIPP delay

Mean UIPP error

Std UIPP delay error

July 06 2004

Page 7: Ionospheric Forecasting Models

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/

Page 8: Ionospheric Forecasting Models

KL University GNSS stations Koneru Lakshamaih University (Geographic 16.31N, 80.37E)

Vaddeswaram, that falls under the transition zone between theequatorial trough and the anomaly crest in Indian region.

Model : GPSstation 6, Novatel, Canada

KLU GNSS network consists of four permanent ionosphericmonitoring systems at Guntur, Machilipatnam,Bapatla andNarsapur with spacing of 100 kms along Bay of Bengal sea coastalbelt.

http://igscb.jpl.nasa.gov

Page 9: Ionospheric Forecasting Models

72oE 75oE 78oE 81oE 84

oE 8oN

10oN

12oN

14oN

16oN

18oN

20oN

*KLU Guntur

*Hyderabad

*Bangalore

Longitude (Degrees)

Latitu

de (

Degre

es)

GNSS S TEC Data Used

S.No Station Name Geographical

Latitude in degrees N

Geographical Longitude

in degrees E

Year of

data

1 Guntur 16.37 80.37 2013

2 Hyderabad 17.41 78.55 2013

3 Bangalore 13.0212 77.57 2013

Page 10: Ionospheric Forecasting Models

Comparison of GPS Observations with IRI Model Predicted Observations in 2013

Page 11: Ionospheric Forecasting Models

11

Fig: Seasonal variation of GPSTEC over low latitude station, KL University,Vaddeswaram, India.

Local time (Hrs)

VT

EC

(TE

CU

)

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.

Jan,2013 Feb,2013 Mar,2013

April,2013 May,2013June,2013

WinterEquinox

Equinox

Winter

SummerSummer

40TECU 45TECU 55TECU

60TECU 55TECU

45TECU

Page 12: Ionospheric Forecasting Models

12

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.

Page 13: Ionospheric Forecasting Models

13

-13nT-92nT -98nT

June 29, 2013

Storm day

26.87TECU2.472TECU

24.9TECU

26-07-2013 27-07-2013 28-07-201329-07-2013

30-07-2013

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

60TECU

70TECU

Geomagnetic storm 1 (March 17, 2013)

Page 14: Ionospheric Forecasting Models

Geomagnetic storm 1 (March 17, 2013)

DST Index variations along with the original and comparison between forecasted VTEC valuesand predicted IRI – 07 & 2012 values

0 2 4 6 8 10 12 14 16 18-150

-100

-50

0

50

100

Number of Days

DST

Ind

ex V

alue

s (n

T)

DST Index

0 2 4 6 8 10 12 14 16 18

0

50

100

Number of Days

VT

EC

Val

ues

(TE

CU

)

Hourly Forecasting

Original Values ARMA model IRI-07 model IRI-12 model

Page 15: Ionospheric Forecasting Models

Geomagnetic storm 1 (March 17, 2013)

0 0.5 1 1.5 2 2.5 3-20

-15

-10

-5

0

5

10

15

20

Number of Days (forecasted)

Obse

reve

d Err

or (T

ECU)

Error in Forecasting

ARMA model IRI-07 model IRI-12 model

Storm DayPrestorm Day Post storm Day

Forecasted error of ARMA compared with error of IRI – 07 & IRI -2012 model

Page 16: Ionospheric Forecasting Models

16

March 17, 2013

Storm day-88nT

15nT

-106nT

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

70TECU

62TECU

Geomagnetic storm 2 (June 29, 2013)

Page 17: Ionospheric Forecasting Models

Geomagnetic storm 2 (June 29, 2013)

DST Index variations along with the original and comparison between forecasted VTEC valuesand predicted IRI – 07 & 2012 values

0 5 10 15 20 25 30-150

-100

-50

0

50

100

Number of Days

DST

Ind

ex V

alue

s (n

T)

DST Index

0 5 10 15 20 25 300

50

100

Number of Days

VT

EC

Val

ues

(TE

CU

)

Hourly Forecasting

Original Values ARMA model IRI-07 model IRI-12 model

Page 18: Ionospheric Forecasting Models

Geomagnetic storm 2 (June 29, 2013)

0 0.5 1 1.5 2 2.5 3

-20

-15

-10

-5

0

5

10

15

Number of Days (forecasted)

Obs

erev

ed E

rror

(TEC

U)

Error in Forecasting

ARMA model IRI-07 model IRI-12 model

Storm Day Post storm DayPrestorm Day

Forecasted error of ARMA compared with error of IRI – 07 & IRI -2012 model

Page 19: Ionospheric Forecasting Models

Analysis of Ionospheric Time Delays

Forecasting Methods

Page 20: Ionospheric Forecasting Models

Analysis of Ionospheric Time Delays

Forecasting Methods(cntd.,)

1 50 100 150 200 250 300 350 365-5

0

5

10

No.of Days

Actu

al

Fo

reca

st E

rro

r o

f M

eth

od

s (m

)

ARIMA Method

HW-Additive Method

HW-Multiplicative Method

Page 21: Ionospheric Forecasting Models

Performance Evaluation of Early Warning

Ionospheric Forecasting Models

1 2 3 4 5 6 7 8 9 10 11 120

20

40

60

No.of Months (Jan-Dec,2013)

MA

PE

(%

)

Multiple Forecast Models MAPE error measurements over 2013 year

Additive

Multiplicative

ARIMA

1 2 3 4 5 6 7 8 9 10 11 120

0.5

1

No.of Months (Jan-Dec,2013)

MA

E(m

)

Multiple Forecast Models MAE error measurements over 2013 year

1 2 3 4 5 6 7 8 9 10 11 120

1

2

No.of Months (Jan-Dec,2013)

MS

D(m

)

Multiple Forecast Models MSD error measurements over 2013 year

Page 22: Ionospheric Forecasting Models

GPS-TEC and SSN for the first EOF associated coefficient

2/11/2016 22

Page 23: Ionospheric Forecasting Models

Coherence wavelet spectrum between three stations observation data

and the Dst index

2/11/2016 23

Page 24: Ionospheric Forecasting Models

Proposed Ionospheric Warning System

Early

warning

• Detection and estimation of solar transient event

• 1-2 days before arrival at Earth

L1-warning

• Measurement of solar transient event near the lagrangian point L1

• Approx. 30 minutes before arrival at Earth

Forecast-warning

• Forecast of ionospheric Perturbations/TEC

• Local Forecast

• A few hours before ionospheric perturbations

Ionospheric-Alert

• Measurement of ionospheric perturbations (Scintillations, TECgradients, flares,…. )

• Near real time alert

Ref: http://elib.dlr.de/89916/1/085.Borries.pdf

Page 25: Ionospheric Forecasting Models

Conclusion

• A novel method of approach was introduced for early warning ionosphericalert systems using GPS observations through regression and statisticalmethods.

• The performance of early warning methods to alert ionospheric delays (1h-ahead) over low latitude Guntur station, India has evaluated.

• It was seen that the forecasting results were in good agreement withdiurnal 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 othermodels.

• In future, multiple GPS stations data from different geographical locationswill be considered for testing the forecasting capability of ARMA along withARIMA and Holt-Winter models under nominal and sever geomagneticstorm conditions.

• The outcome of these results would be helpful in the process of shieldingthe 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.

Page 27: Ionospheric Forecasting Models

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.

Page 28: Ionospheric Forecasting Models

Ionospheric Effects on the CNS Applications