A new global foF2 model for the International Reference Ionosphere 1,2 Lee-Anne McKinnell 3 Elijah...
17
A new global foF2 model for the A new global foF2 model for the International Reference International Reference Ionosphere Ionosphere 1,2 Lee-Anne McKinnell 3 Elijah Oyeyemi 1 Physics Department, Rhodes University, Grahamstown, South Africa 2 Space Physics Group, Hermanus Magnetic Observatory (HMO), Hermanus, South Africa 3 Department of Physics, University Of Lagos, Lagos, Nigeria
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Transcript of A new global foF2 model for the International Reference Ionosphere 1,2 Lee-Anne McKinnell 3 Elijah...
A new global foF2 model for the A new global foF2 model for the International Reference IonosphereInternational Reference Ionosphere
1,2Lee-Anne McKinnell3Elijah Oyeyemi
1Physics Department, Rhodes University, Grahamstown, South Africa
2 Space Physics Group, Hermanus Magnetic Observatory (HMO), Hermanus, South Africa
3 Department of Physics, University Of Lagos, Lagos, Nigeria
Global foF2 model
• neural network based• 135 ionosonde stations included• predicts foF2 parameter across the globe• mostly data from 1995 – 2005
Day NumberUniversal TimeZenith AngleGeographic LatitudeMagnetic InclinationMagnetic DeclinationMagnetic IndexSolar IndexAngle of Meridian
Latitude
Longitude
Year
Day Number
Hour
NN ModelfoF2
GLOBAL foF2 MODEL
2
2)(
i
ii
m
omNRMSE
IRI
NNIRI
NRMSE
NRMSENRMSEimp
%
Analysis
n
omRMSE ii
2)(
IRI
NNIRI
RMSE
RMSERMSEimp
%
0.000
5.000
10.000
15.000
20.000
25.000
30.000
35.000
40.000
Scott
Base(
1977
)
Terre
Ade
lie(1
977)
Argen
tine
Is(1
977)
Conce
pcion
(196
5)
Graha
mst
own(1
977)
Huanca
yo(1
974)
Singa
pore
(196
4)
Talara
(196
5)
Djibout
i(197
4)
Dakar
(197
6)
Tomsk
(197
7)
Narss
arss
uaq(1
965)
Resolu
te B
ay(1
977)
% i
mp
rov
em
en
t
CCIR
URSI
Low solar activity
Low Solar Activity
0.000
5.000
10.000
15.000
20.000
25.000
30.000
35.000
40.000
Scott
Base(
1979
)
Terre
Ade
lie(1
979)
Argen
tine
Is(1
979)
Conce
pcion
(196
8)
Graha
mst
own(1
979)
Huanca
yo(1
968)
Singa
pore
(195
8)
Talara
(196
1)
Djibout
i(197
8)
Dakar
(197
1)
Tomsk
(197
9)
Narss
arss
uaq(1
968)
Resolu
te B
ay(1
979)
% I
mp
rov
em
en
t
URSI
CCIR
High solar activity
High Solar Activity
0
2
4
6
8
10
12
14
% i
mp
rov
em
en
t
URSI CCIR
Low solar activity
High solar activity
Total
Station Latitude Longitude
Thule 77.5 290.8
Dyess 32.5 260.3
Vanimo -2.7 141.3
Grahamstown -33.3 26.5
Casey -66.3 110.5
Test Case: Year = 2006
0
5
10
15
20
25
30
Thule Dyess Vanimo Grahamstown Casey
% i
mp
rove
men
t
%URSI
%CCIRMarch 2006
0
10
20
30
40
50
60
70
80
90
Thule Dyess Vanimo Grahamstown Casey
% i
mp
rove
men
t
%URSI
%CCIR
June 2006
0
5
10
15
20
25
30
35
40
45
Thule Dyess Vanimo Grahamstown Casey
% i
mp
rove
men
t
%URSI
%CCIR
September 2006
0
5
10
15
20
25
30
35
40
45
Thule Dyess Vanimo Grahamstown Casey
% i
mp
rove
men
t
%URSI
%CCIR
December 2006
Comparisons with IRI
Equatorial Representation(Training Stations between -10 & +10)
Station Lat Long
Port Moresby -9.4 147.1
Ascension -7.9 12.4
Talara -4.6 278.7
Vanimo -2.7 141.3
Singapore 1.3 103.8
Bogota 4.5 285.8
Trivandrum 8.5 77
Kwajalein 9.1 167.2
Station Lat Long
Port Moresby -9.4 147.1
Ascension -7.9 12.4
Vanimo -2.7 141.3
Fortaleza -3.8 322
Sao Luis -2.6 315.8
Equatorial prediction comparisonsStations used for analysis
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Vanimo (2006) Ascension Is.(2005)
Fortaleza (2004) Pt Moresby (2004)
NR
MS
E
NRMSE_URSI
NRMSE_CCIR
NRMSE_NN
March
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Vanimo (2006) Ascension Is.(2005)
Fortaleza(2004)
Pt Moresby(2004)
Sao Luis (2006)
NR
MS
E
NRMSE_URSI
NRMSE_CCIR
NRMSE_NN
June
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Vanimo (2006) Ascension Is.(2005)
Fortaleza(2004)
Pt Moresby(2004)
Sao Luis (2006)
NR
MS
E
NRMSE_URSI
NRMSE_CCIR
NRMSE_NN
September
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Vanimo (2006) Ascension Is.(2005)
Fortaleza(2004)
Pt Moresby(2004)
Sao Luis (2006)
NR
MS
E
NRMSE_URSI
NRMSE_CCIR
NRMSE_NN
December
low solar activity
NRMSE Values
0
1
2
3
4
5
6
7
8
9
10
178 178.5 179 179.5 180 180.5 181
Day Number
foF
2,
[MH
z]
Observed
NN Predicted
URSI Predicted
CCIR Predicted
June 2006
0
2
4
6
8
10
12
265 265.5 266 266.5 267 267.5 268
Day Number
foF
2,
[MH
z]
Observed
NN Predicted
URSI Predicted
CCIR Predicted
September 2006
0
1
2
3
4
5
6
7
8
9
10
356 356.5 357 357.5 358 358.5 359
Day Number
foF
2,
[MH
z]
Observed
NN Predicted
URSI Predicted
CCIR Predicted
December 2006
Sao Luis, -3.6:315.8
NRMSE(URSI) NRMSE(CCIR) NRMSE(NN)0.17
0.18
0.19
0.2
0.21
NR
MS
E
Vanimo 2006
% Imp URSI % Imp CCIR7
7.2
7.4
7.6
7.8
8
8.2
8.4
8.6
8.8
9
Per
cen
tag
e Im
pro
vem
ent
Vanimo 2006
Vanimo-2.7, 141.3
Year = 2006
-10
0
10
20
30
40
50
Per
cen
tag
e Im
pro
vem
ent
%imp URSI %imp CCIRVanimo 2006
Summary
NN Model can predict accurate foF2 values across the globe
however
magnetic storm time analysis equatorial sector analysis high latitude sector analysis analysis of suitability over the ocean areas inclusion of more equatorial & high latitude data reworking of input parameters to tie in with current IRI inputs re-analyse the relative importance of input parameters
Requirements
