Post on 07-Apr-2019
Data Processing Method for Geomagnetic Data Observation of
MAGDAS/CPMN System
SITI NOOR AISYAH AHMAD1, MOHAMAD HUZAIMY JUSOH
1, MOHD KHAIRUL MOHD
SALLEH1, MHD FAIROS ASILLAM
2 and MAGDAS/CPMN Group
3
1Faculty of Electrical Engineering
Universiti Teknologi MARA
Shah Alam, Selangor
MALAYSIA
2National Space Agency of Malaysia (ANGKASA)
Malaysia Space Centre
Banting, Selangor
MALAYSIA
3 International Center for Space Weather Science and Education (ICSWSE)
Kyushu University
JAPAN
Email: snaisyah12@yahoo.com; huzaimy@salam.uitm.edu.my
Abstract: - Space weather study has increasingly attracts the attention of many scientists to explore the
interaction between solar activity and geomagnetic activity. During the previous Space Weather initiative
program, called as International Space Weather Initiative (ISWI) period (2010-2012), International Center for
Space Weather Science and Education (ICSWSE), Kyushu University, Japan in collaboration with National
Space Agency of Malaysia (ANGKASA) and local universities has installed a magnetometer at National
Observatory Langkawi. In this paper, we will briefly discuss the data processing methods involve in order to
analyze the geomagnetic data observed by magnetometer from Langkawi station (LKW). The explanation of
the processing methods is based on the 24-hour data extracted during quiet and disturbed day.
Key-Words: - Magnetometer, geomagnetic data, magnetic pulsation and data processing method
1 Introduction
Magnetic pulsations or called ultra-low frequency
(ULF) pulsations is electromagnetic waves
generated in the magnetosphere. Its frequency
range is between 1 mHz and 1Hz. The generation
of magnetic field is defendant on solar and
processes in the magnetosphere. Earth’s magnetic
field observations play important role in the
understanding of the Earth’s electromagnetic
environment. Many experiments done by previous
researchers found that the variations in magnetic
fields are caused by the dynamo action in the upper
atmosphere. Daily variation (24 hours period) of
geomagnetic field components was first observed by
G. Graham in London [1]. The variations are then
observed as magnetic pulsations on the ground and
recorded in the range of Ultra Low Frequency
(ULF) with periods of 0.2 - 600 sec [2].
2 MAGDAS Instrumentation and
Geomagnetic Field Variation
International Center for Space Weather
Science and Education, ICSWSE, Kyushu
University, Japan has introduced a real-time
Magnetic Data Acquisition System of Circum-
pan Pacific Magnetometer Network, i.e.
MAGDAS/CPMN for space weather study and
application, which was deployed for the
International Heliophysical Year (IHY; 2007-
2009) [3]. By using this system, ICSWSE
conducted real-time monitoring and modelling
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of (1) global 3-dimensional current system, (2)
plasma mass density, and (3) penetrating
process of polar electric fields into the
equatorial ionosphere, in order to understand
the Sun-Earth coupling system and the
electromagnetic and plasma environment
changes [4]. To date, MAGDAS/CPMN
consists of three (3) unique chains of magnetic
observatories; the most magnetometers were
densely installed at 210° magnetic meridian, on
African longitude-sector and the other one is on
the sector along the magnetic equator (with total
of 71 stations worldwide), as shown in Figure 1.
From the magnetometer, we can extract
the ambient magnetic field, expressed by H
(Geomagnetic Northward), D (Geomagnetic
Eastward) and Z (Vertical Downward)
components.
Figure 1 Map of magnetometers installed under
MAGDAS/CPMN
2.1 Magnetometer
MAGDAS-9 (MAG-9) unit which was installed
at National Observatory Langkawi (LKW
station) consists of 3-component ring-core
fluxgate type magnetic sensor (magnetometer)
with 7 meter cable, pre-amplifier (preamp),
GPS (Global Positioning System) antenna with
cable, data logger for data control and 70 meter
cable. The main components of the
magnetometer system are main unit, pre-
amplifier and sensor as illustrated in Figure 2.
Data logger acts as a main unit to control
the power supply to the unit and communication
process. Magnetic field digital data (H + δH, D
+ δD, Z +δZ) are obtained with the sampling
rate of 10 Hz, and then 1 second and 1 minute
averaged data are recorded and transferred from
the oversea stations to the ICSWSE, Japan in
real-time [5]. The ambient magnetic field
components are digitized by using the field-
cancelling coils for the dynamic range of ±
70,000nT/32bits. The magnetic variations (δH,
δD, δZ) data are further digitized by the A/D at
preamp by 24 bits and 10 Hz resolution and
sampling frequency respectively. The long-term
inclinations (I) of the sensor axes are measured
by built in digital tilt meter with 0.1 arc-sec
resolution at calibrated accuracy ± 0.25 degree
(± 900 sec. degree). The temperature (T), are
also measured at both sensor and preamp with
resolution 0.01°C. The system synchronizes the
time of acquisition of the A/D conversion and
the GPS clock transmitted a pulse of 1 PPS
from the GPS module. These data are logging in
the Compact Flash Memory Card of 2 GB.
Figure 2 Diagram of MAGDAS system
2.2 Geomagnetic Data
Geomagnetic data (extracted from
magnetometer) is used in this study to monitor
ambient magnetic activity. The
MAGDAS/CPMN magnetometer is a ring core-
type fluxgate magnetometer that measures the
three components of the geomagnetic field;
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Horizontal component (H), Declination
component (D), and the Vertical component (Z)
as shown in Figure 3.
Figure 3 Geomagnetic field components; [F]
Total intensity of the geomagnetic field,
Horizontal component (H), Declination
component (D) and Vertical component (Z)
The 1-sec resolution data from horizontal
component were extracted to examine the
geomagnetic pulsations, Pc3, Pc4 and Pc5 as
classified by International Association of Geomagnetism and Aeronomy (IAGA) as shown in
Table 1. The raw data from MAGDAS/CPMN
stations was first bandpass-filtered before we plotted
the dynamic power spectra density to identify the
occurrences of ultra-low frequency (ULF) at Pc3,
Pc4 and Pc5.
Table 1 IAGA classification of ULF waves in
1964
ULF
pulsations Period
(sec)
Frequency
(mHz)
Continuous
Pc1 0.2-5 200-500
Pc2 5-10 100-200
Pc3 10-45 22-100
Pc4 45-150 6.7-22
Pc5 150-600 1.7-6.7
Irregular
Pi1 1-40 25-200
Pi2 40-150 6.7-25
3 Data Processing
The flow of the data processing is shown in Figure
4. The observed geomagnetic which stored in data
cards should be processed to be convenient for end
user of data in research work. The data processing
method needs to be implemented to ensure the
quality of data and the processed data is useful for
scientific research. As current procedures, Matlab
programming language is used to process the raw
data covering the processes for data availability
screening, ambient noise check-up, plotting, band-
pass filtering, power spectra density and Fast
Fourier Transform (FFT) analysis.
Figure 4 Flowchart of process MAGDAS data
At low latitudes, the horizontal component
is the major part of the total field and the
vertical component is significantly affected by
the geological and geographic surroundings of
the station [6]. Due to this fact, for LKW station,
we only demonstrate the processing method of
horizontal component of the geomagnetic field.
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ISBN: 978-960-474-372-8 128
3.1 Raw Data
The raw data were analyzed based on the quiet and
disturbed days which are on 15 March 2010 and 12
March 2010 respectively. Figure 5 and Figure 6
show raw data of the horizontal magnetic field
components observed at LKW station. Both figures
show that H component recorded higher amplitude
at time 0000 to 1000 UT (Universal Time). This is
due to day time effect where Local Time (LT) for
LKW station is + 8 UT. The local H component
afterwards maintained at 4.12 x 104
nT during night
time from 1000 till 2300 UT. Other than that, one
can see clearly the H component recorded on
disturbed day (12 March 2010) is distracted as
compared to H variation recorded on quiet day (15
March 2010).
Figure 5 Raw data of H (nT) magnetic components
on 15 March 2010
Figure 6 Raw data of H (nT) magnetic components
measured on 12 March 2010
3.2 Band-pass Filter
Raw data from LKW station was band-pass filtered
to classify the geomagnetic pulsations; either Pc
(continuous pulsation) or Pi (irregular pulsation)
pulsations. Figure 7 a), b) and c) show the Pc3, Pc4
and Pc5 respectively, on quiet day. The Pc3, Pc4
and Pc5 on disturbed days are shown in Figure 8 a),
b) and c) respectively. The ULF pulsations observed
during disturbed day (Figure 8 b) and c)), mainly on
Pc 4 and Pc 5 ranges show higher fluctuation as
compared to other Pc during quiet day.
a)
b)
c)
Figure 7 ULF pulsations a) Pc3, b) Pc4 and c) Pc5
on 15 March 2010
a)
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b)
c)
Figure 8 ULF pulsations a) Pc3, b) Pc4 and c) Pc5
on 12 March 2010
3.3 Power Spectra Density
To further confirm the occurrences of geomagnetic
pulsations, Power Spectra Density (PSD) method
has been applied into the process of analysis. The
PSD for significant ULF range (0 – 100 mHz) was
plotted based on color spectrum which corresponds
to the algorithm of the power in nT2/Hz. The
calculation was implemented based on Hanning
window through the data and Fast Fourier
Transform (FFT) on the subset of the signal within
the window.
Figure 9 shows PSD plot for quiet day where
no clear Pc (all ranges) can be observed and only 2
Pi events detected at around time 1400 and 1900
UT. For PSD plot on disturbed day as shown in
Figure 10, Pc 4 and Pc 5 events can be observed,
which occurred at time around 0200 to 0700 UT.
Figure 9 Power Spectra Density on 15 March 2010
Figure 10 Power Spectra Density on 12 March 2010
4 Data Analysis and Discussion
In this work, we have analyzed horizontal
component of geomagnetic data at LKW station,
which located at low latitude region. The data were
divided into 2 categories; quiet and disturbed period,
available at Data Analysis Center for Geomagnetism
and Space Magnetism, Kyoto University, Japan
(WDC). The selection of the quietest days (Q-days)
are derived from the magnetic activity indices by
index ranges through 0 to 9 with 0 is being quietest
or most disturbed day and 9 being least of both.
Furthermore, the selection of the most disturbed
days (D-days) are derived from the magnetic
activity indices by index ranges through 1 to 5 with
1 is being quietest or most disturbed day and 5 being
least. To further compare with solar wind events, we
have plotted the solar wind speed and solar wind
input energy. Solar wind speed events and other
parameters (proton density [cm-3
], magnetic field in
x, y and z-direction [nT]) on March 2010 were
obtained from the Space Physics Data Facility
(SPDF) based at NASA’s Goddard Space Flight
Center. While solar wind input energy need to
calculate using Akasofu epsilon, ɛ [7] as equation
(1):
epsilon, ɛ = Vsw B2 F(θ) Io
2 (Watt or erg/s) (1)
Where Vsw is solar wind speed [km/s], B is total
magnetic field [nT], Io is Earth’s radius [km]
and F (θ) is a function of the angle, θ (By/Bz) The occurring of ULF pulsations can be
determined by referring to the solar wind parameters
(solar wind speed and solar wind input energy).
Figure 11 shows a solar wind speed and solar wind
input energy from 11-16 March 2010. Solar wind
speed and solar wind input energy reached a higher
peak level on disturbed day (12 March 2010) as
compared to the quiet day which occurred 3 days
later on 15 March 2010.
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ISBN: 978-960-474-372-8 130
Figure 11 Solar Wind Speed (top) and Solar Wind
Input Energy (bottom) from 11-16 March 2010
5 Conclusion
The data processing method of geomagnetic data
recorded by magnetometer from earth station
Langkawi (LKW) has been discussed based on the
24-hour data extracted during quiet and disturbed
day. The occurrence of ULF pulsations is influenced
by the solar wind parameters (solar wind speed and
solar wind input energy). The ability of the
MAGDAS/CPMN magnetometer to measure ULF
pulsations is important to understand the space
weather using geomagnetic field data. By applying
the aforementioned data processing methods, it is
possible to extract and investigate the possible
relationship of space weather and the activities on
the lithosphere. However, further analysis and
evaluation involving extension of observational data
with advanced statistical analysis method are needed
to ensure the relationship of space weather and
geomagnetic activity can be comprehensively
explained.
Acknowledgement: The authors are grateful to the
MAGDAS/CPMN Group by International Center
for Space Weather Science and Education
(ICSWSE), Kyushu University, Japan for providing
the geomagnetic data at Langkawi station and
National Space Agency for maintaining the
equipment. The authors also want to thank
OMNIWeb Data Explorer, Space Physics Data
Facility from NASA for providing the data of solar
wind parameters. This project is funded under the
Ministry of Higher Education (MOHE) Malaysia
grants (600-RMI/DANA 5/3/PSI (175/2013) and
600-RMI/ERGS 5/3 (81/2012).
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ISBN: 978-960-474-372-8 131