Vr: A New Index to Represent the Regional Geomagnetic Activity

Vr: A New Index to Represent the Regional Ge

omagnetic Activity

Dongmei Yang, Yufei He, Chuanhua Chen, Yahong Yuan

2010-9-21

Changchun

Outline

• Introduction

• Data Processing

• Results and Discussion

• Conclusion

Outline

• Introduction

• Data Processing

• Results and Discussion

• Conclusion

The disturbances revealed by the first differences could be seen in all the observatories simultaneously which reminded us that the source for the disturbances was in the magnetosphere.

The first differences also showed 27 solar-cycle recurrences. The feature was quite similar with the Kp’s.

There are some disturbances that Kp could not describe.

Another new index might be needed for people to understand the change of the magnetic field.

Outline

• Introduction

• Data Processing

• Results and Discussion

• Conclusion

Data

• Minute data for the year 2008 from the following observatories– Chinese observatories– Global observatories

• CLF, MMB, HON and SJG• Data downloaded from INTERMAGNET website

Method

• Vr index calculation: – Calculating the first differences of the minute values o

f the geomagnetic horizontal component (H D/X Y).– calculating the hourly standard deviations of the abov

e first differences.– Subtracting background noises (0.1nT) from the hourl

y standard deviations– In case that the hourly standard deviation was less th

an 0.1nT, the Vr value was assigned to be 0.

Noise level of 1s data

• Noise Level achieved– D 0.06nT– H 0.07nT– Z 0.07nT– F 0.04nT

Outline

• Introduction

• Data Processing

• Results and Discussion

• Conclusion

Results and Discussion

• Relationship between Vr and other indices

• Temporal Change of Vr– Changes with the 27 solar rotation cycle – Seasonal change

• Spatial change of Vr– Latitudinal dependences (not talked today)– Longitudinal dependences

(Local time dependences)

SJG MMB CLF HON WMQ

KP 0.77462 0.757963 0.767482 0.751112

0.745843

AP 0.768873 0.717089 0.745654 0.723117

0.716811

Correlation between Vr and other indices

MZLy = 4. 1981x + 0. 8526

0

10

20

30

40

50

0 2 4 6 8 10

SDD

∑Kp

MZLy = 24. 484x - 7. 2058

0

50

100

150

200

250

300

0 2 4 6 8 10

SDD

∑ap/2nT

LYHy = 5. 7662x + 1. 6468

0

10

20

30

40

50

0 2 4 6 8 10

SDD

∑Kp

WHNy = 5. 4122x + 1. 4586

0

10

20

30

40

50

0 2 4 6 8 10

SDD

∑Kp

QZHy = 7. 189x + 2. 2576

0

10

20

30

40

50

0 2 4 6 8 10

∑ SDDH/ nT•mi n-1

∑Kp

LYHy = 34. 413x - 3. 999

0

50

100

150

200

250

300

0 2 4 6 8 10

SDD

∑ap/2nT

WHNy = 32. 345x - 5. 2062

0

50

100

150

200

250

300

0 2 4 6 8 10

SDD

∑ap/2nT

QZHy = 42. 926x - 0. 5726

0

50

100

150

200

250

300

0 2 4 6 8 10

∑ SDDH/ nT•mi n-1

∑ap/2nT

MZLy = 4. 1981x + 0. 8526

0

10

20

30

40

50

0 2 4 6 8 10

SDD

∑Kp

MZLy = 24. 484x - 7. 2058

0

50

100

150

200

250

300

0 2 4 6 8 10

SDD

∑ap/2nT

LYHy = 5. 7662x + 1. 6468

0

10

20

30

40

50

0 2 4 6 8 10

SDD

∑Kp

WHNy = 5. 4122x + 1. 4586

0

10

20

30

40

50

0 2 4 6 8 10

SDD

∑Kp

QZHy = 7. 189x + 2. 2576

0

10

20

30

40

50

0 2 4 6 8 10

∑ SDDH/ nT•mi n-1

∑Kp

LYHy = 34. 413x - 3. 999

0

50

100

150

200

250

300

0 2 4 6 8 10

SDD

∑ap/2nT

WHNy = 32. 345x - 5. 2062

0

50

100

150

200

250

300

0 2 4 6 8 10

SDD

∑ap/2nT

QZHy = 42. 926x - 0. 5726

0

50

100

150

200

250

300

0 2 4 6 8 10

∑ SDDH/ nT•mi n-1

∑ap/2nT

The first differences also showed 27 solar-cycle recurrences. The feature was quite similar with the Kp’s.

Monthly Means for H/X Component

0

2

4

6

8

10

12

1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280

WMQ

SDDH- 1HR-月总和

按月排

按时段排

0

2

4

6

8

10

12

1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280

WMQ

SDDH- 1HR-月总和

按月排

按时段排

SDDD-1HR-月总和

0

2

4

6

8

10

12

14

16

1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286

WMQ

SDDD-1HR-月总和

0

2

4

6

8

10

12

14

16

1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286

WMQ

Monthly Means for D/Y Component

Local time dependence of Vr

Observatories used in the study

IAGA code

Latitude

(°)

Longitude

(°)

Local time differences

referred to UTC

CLF 48.02 2.27 0

WMQ 43.80 87.70 6h

MMB 43.91 144.19 10h

HON 21.32 202.00 13h

SJG 18.12 293.85 20h

Annual MeansSDDH- 1HR-年总和

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24时段s

ddh

SJ G MMB CLF HON WMQ

SDDD- 1HR- 年总和

0

200

400

600

800

1000

1200

1400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

SJ G MMB CLF HON WMQ

sum归一

0

0. 01

0. 02

0. 03

0. 04

0. 05

0. 06

0. 07

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

SJ G MMB CLF HON WMQ

sum归一

0

0. 01

0. 02

0. 03

0. 04

0. 05

0. 06

0. 07

0. 08

0. 09

0. 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

SJ G MMB CLF HON WMQ

H/X component

D/Y component

1

02

3

4

5

6

7

6

8

9

1011

1213

12

1415

16

17

18

1918

20

21

2223

24

SDDD-peak-aver

1

02

3

4

5

6

76

8

9

1011

1213

12

1415

16

17

18

1918

20

21

2223

24

SDDH-peak-aver

Outline

• Introduction

• Data Processing

• Results and Discussion

• Conclusion

Conclusion

• Vr is an index that can be used to detect geomagnetic disturbances from the magnetosphere. Vr has clear physical meaning.

• Vr can detect geomagnetic disturbances not only those Kp and ap detect but also some small disturbances that Kp and ap can not detect. So Vr is more sensitive to the geomagnetic activities than Kp and ap.

• Vr can be easily calculated by individual observatory so it can be used as a regional index and can serve people in quasi real time.

END

Thanks for your attention