Indian Journal of Science and Technology, Vol 9(25), DOI: 10.17485/ijst/2016/v9i25/96627, July 2016
ISSN (Print) : 0974-6846
ISSN (Online) : 0974-5645
Analyses of Cloud Characteristic during
Malaysian 2014 Flood Event
Atikah Balqis Basri*, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar,
Nuurul Hudaa Mohd Sobli, Khairayu Badron, and Mohammad Kamrul Hasan
Department of Electrical and Computer Engineering, Kulliyyah of Engineering, International Islamic University
Malaysia (IIUM), Jln. Gombak, Selangor, Malaysia;
atikahbalqis32@gmail.com, af_ismail@iium.edu.my, haziq@iium.edu.my, hudaa@iium.edu.my,
khairayu@iium.edu.my, and hasankamrul@ieee.org
Abstract
Floods refers to the condition of great overflow of water over a dry land. In malaysia, monsoon flood which cause by the
heavy rain in monsoon seasons regularly hits the country. A criteria of the cloud based on horizontal and vertical profile
of radar reflectivity have been analyzed in this paper to estimate flood event. The values of the thickness and the size of
the cloud are estimated from the analysis. In this paper, we analyze the river basin data and radar data in the duration of
flooding time, T for the specific area covered by meteorological radar and rain gauge data. The procedure was applied to 14
days precipitation phenomenon observed in Kota Bharu, Kelantan (Malaysia) from 13 December 2014 until 26 December
2014. The objective of this paper is to analyse the distinctiveness of the cloud during flood events. The result shows that
during the critical time of flood disaster, the cloud shows largest size of 13070.6 km2 and the thickness appeared to be the
largest at almost 10.2 km during the beginning of the rain fall. The analysis helps us to understand the cloud characteristics
hence in future flood estimation model can be constructed and modelled.
Keywords: CAPPI, Flood Estimation Model, Flood Model, Radar, RHI
1. Introduction
Flood hazard has become a major problem across the
world. In Malaysia, flood is considered as one of the
problems that regularly occur every year and known as
one of the worst flood-affected countries in the world1.
During the end of the year 2014, Malaysia experienced
an overwhelming flood disaster and the most affected
area is in Kelantan as shown in Figure 1 the recent flood
happened in Kota Bharu, Kelantan.
In Malaysia, flood has been categorized into two
categories by the Malaysian Drainage and Irrigation
Department which are known as flash flood and
monsoon floods. From the hydrological perspectives,
flash flood took only few hours to last while the monsoon
flood can last for about one month. Malaysia is among
the countries that solemnly vulnerable to the flood due
to high precipitation rates throughout the years especially
during the monsoon seasons3.
* Author for correspondence
Figure 1. Recent flood disaster in Kota Bharu2.
In tropical region, there are difficulties in estimating
the flood due to lack of information regarding type
of tropical precipitation and climate characteristics
such as rainfall intensity and the cloud size. In order to
analyze this tropical precipitation, direct measurement
approach is used by using rain gauge. Together with
this direct measurement approach, radar is being used
Analyses of Cloud Characteristic during Malaysian 2014 Flood Event
as the indirect measurement method to evaluate the
precipitation and the cloud size4. According to Tam et al.5
methods of estimating rainfall technique can be divided
into two groups: indirect and direct method. Bouilloud
et al.6 proposed a method for rainfall estimation based on
post eventcreated on a digital terrain model of the region
of interest characterized by dry weather clarifications and
simulations.
now casting method in order to forecast the motion of
precipitation areas by using a nonlinear extrapolation to
estimates the intensity and size of the raindrop. While
the other method known as Storm Cell Identification
and Tracking algorithm (SCIT) employs a mass centroid
method with seven reflectivity thresholds. These paper
highlights useful radar data information to estimate the
thickness and cloud size for the development of tropical
flood estimation technique9.
The details of the data collection from the radar data
for cloud characteristics are briefly explained in section
2. Afterwards, the data analyses and results for the cloud
characteristics are discussed in section 3. Finally, section
4 draws some conclusion and future works to improve the
techniques discussed.
2. Data Collection
Radar data from a radar sensor located at PangkalanChepa,
Kelantan, Malaysia were bought from Malaysia
Meteorological Department for the use in this research.
Whilst rain gauge data and river basin data were acquired
from the Malaysian Drainage and Irrigation Department
(DID). The location of these station is shown in Figure 3.
Figure 2. Typical weather radar5.
Weather radar as shown in Figure 2 function by
measuring the electromagnetic radiation backscattered
by cloud raindrops hence radar is possible to estimate
the rainfall. Raincloud that backscattered higher
electromagnetic radiation contains of larger droplets
of rain and as the result, this type of cloud can possibly
produce more rain6. Likewise, radar provides wider
spatial coverage. Due to the limited number of rainfall
measuring stations, and insufficient available data to
characterize the highly variable rainfall rate especially
applicable for developing countries like Malaysia, the use
of weather radar as a measuring mechanism, is needed.
Radar can accurately analyze the information in order
to properly interpret the measured quantities7. Tsonis et
al.8encompassed the single development of cells into a
2
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Figure 3. Radar and rain gauge location.
The radar is positioned at 102.28°E, 6.17°N coordinate
system. This radar has the displacement distance of
22.03km from the reference rain gauge station located at
Salor, Kota Bharu. This type of radar functions as a single
polarization radar operating at S-Band frequency and the
radar are able to scan for the distance of 300km range. The
radar is operating in a 360° azimuthal volume scan mode
with steps in elevation angles from 0.0° to 32° during
period of precipitation to happen. 313 samples of radar
data from 13rd December until 26th December 2014 were
Indian Journal of Science and Technology
Atikah Balqis Basri, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and
Mohammad Kamrul Hasan
used in this research. In order to extract the information
from this particular data format, IRIS Vaisala Software
was used to produce the reflectivity data and ProductX
was employed to generate bin values in Microsoft® Excel.
The measurement setup is shown in Figure 4 while the
Table 1 tabulated the radar specifications.
occurred every day for the duration of 14 days. Further
observation shows that the longest rain event occurs
at 17th December 2014 happened for 17hours and 30
minutes (14:30:00 to 8:00:00). From this evaluations, the
time event is used as the point of reference for the analyses
of the radar data.
Since we need the verification of the flood event took
place during this time, river basin data from Kelantan
river station JetiKastam, Kota Bharu, is needed to
confirm the condition of heavy rain occurred and the
subsequently sudden high river flows as these will be the
major causes of flooding. The distance from rain gauge
station at Salor to Jeti Kastam is about 20.6 km. According
to 9 only data from Jeti Kastam river basin are available
during the disaster while the other station has been sink
by the flood. Figure 5 demonstrate the river basin data
from DID showing the rapid increasing of the water level
from 0:00:00 (17/12/2014) to 23:00:00 (18/12/2014). The
water level increased from 4.2 m to 7.1 m in few hours
duration.
Figure 4. Measurement setup.
Table 1. Radar specifications
Coordinate (Lat , Lon)
Radar type (Model)
Elevation height
Tower height
Wavelength
Frequency
Receiver
IF frequency
Antenna size (diameter)
Type of antenna
Beam width
Specifications
(6° 9’ 48.29 N, 102°18’
51.18 E)
EEC WSR 74
13 m
19.52 m
10 cm
2750 MHz
Solid state
60 MHz
4.3 m
Polarimetric
1.9 degree max. on axes
3. Data Analysis and Results
In order to analyze the flood event, rain gauge station
located at Salor is used to find the longest rain event and
the time duration for the most critical event. Based on
the rain gauge data, we are able to identify that the rain
Vol 9 (25) | July 2016 | www.indjst.org
Figure 5. River basin level.
It is also proved that the water level exceeds the danger
level, which more than five meter during the 17hours and
30min rain period according to DID standardization. The
sudden change of the water level during that particular
hour represent the heavy flood characteristic where the
volume of water flowing in a river basin system exceeds
its total water holding capacity10.
Radar parameter can explicate the cloud characteristic
involving its thickness and size. By identifying the size of
the cloud during the flood event we broaden the analysis of
clouds thickness using the spatial and temporal variation
of radar vertical profile, focusing during the most intense
rainfall at 17 December 2014.
Figure 6 shows the illustration of the cloud at 1-km
Indian Journal of Science and Technology
3
Analyses of Cloud Characteristic during Malaysian 2014 Flood Event
Figure 6. Reflectivity profile as shown in IRIS Vaisala Software on 18th December 2014 at 06:32:25 for 1 km
height.
height of horizontal characteristic which can be explained
using the radar product Constant Altitude Plan Position
Indicator (CAPPI) viewed from the Kota Bharu, Kelantan.
CAPPI radar data provide spatial variability information
composed of the shape and size of the cloud or known as
cloud characteristics. The reflectivity values can be further
justifying in the attempt to classify the type of cloud11.
Once the CAPPI product created, the ProductX was
employed to generate bin values in Microsoft® Excel.
Figure7 below is the example of the ProductX with all the
information generated. Using the information given we
are able to interpret radar reflectivity of the horizontal
profile.By using Microsoft® Excel, the size of cloud can
be estimated by calculating the bin that representing the
definite dimension (347.22m x 347.22m) with reflectivity
values associated with the cloud.
Reflectivity is correlated to intensity of rainfall12.
Values below 15 dBZ are typically associated with
clouds13. In explaining precipitation characteristic, when
the dBZ value reaches 15, light precipitation is present13.
The size of the cloud is measured by calculating the bin’s
that have values less than 15dBZ. Therefore, the size of the
cloud at the particular time during the flood event can be
estimated based on each ProductX generated file.
Table 2 below shows the calculated cloud size
during the duration of longest time of rainfall occur at
4
Vol 9 (25) | July 2016 | www.indjst.org
17/12/2014 from 14:32:21 to 18/12/14 at 7:32:21 for 1km
height. Figure 8 illustrate the result.
Table 2. Size of cloud from radar data
Time
17/12
/2014
cloud size (km2)
14:32:21 9300.5
cloud
size
(km2)
23:32:24 10007.9
15:02:21
15:32:21
16:32:19
17:02:23
17:32:24
18:02:22
18:32:20
19:02:24
19:32:19
20:02:23
20:32:21
21:02:21
21:32:23
22:02:20
22:32:24
23:02:24
0:02:25
0:32:24
1:02:19
1:32:22
2:02:24
2:32:22
3:02:22
3:32:24
4:02:22
4:32:22
5:02:23
5:32:21
6:02:21
6:32:25
7:02:23
7:32:21
8657.4 18/12 /2014
8932.5
9907.5
10186.0
9504.0
9623.2
13045.4
13070.6
12891.2
11679.5
10783.8
10754.8
12343.5
9201.5
9227.8
9760.9
Time
9433.1
9878.7
9976.4
8609.3
8981.1
9107.0
9830.8
7916.8
7973.5
7968.8
7718.2
6647.7
6300.2
6491.5
7199.1
7400.0
Indian Journal of Science and Technology
Atikah Balqis Basri, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and
Mohammad Kamrul Hasan
Figure 8. Size of the cloud for 1 km height.
In order to identify the thickness of the cloud, the
product of the Range Height Indicator(RHI) is exploited.
The thickness of the cloud for the whole duration of
longest rain is measured. After the reflectivity value of
more than 15dBZ is removed the cloud thickness can be
estimated based on the conceptual model of the vertical
profile develop to identify the bright band thickness as
shown in Figure 9 14.
Figure 9. Vertical profile for radar reflectivity at station salor
on 17 December at 17:32:24 before and after eliminating
dBZ>15.
The approach used to extract the vertical profile for
this event is by creating across section (XSect Product),
a vertical slice through the volume scan of the radar. This
product is similar to an RHI except that it is constructed
from PPI data collected from elevation angle14. The
resulting picture is a two-dimensional vertical slice as
shown in Figure 10.
Once the XSect product is generated ProductX was
Figure 7. ProductX for Cappi.
Vol 9 (25) | July 2016 | www.indjst.org
Indian Journal of Science and Technology
5
Analyses of Cloud Characteristic during Malaysian 2014 Flood Event
employed to generate bin values in Excel. Figure 11 is
the example of the ProductX with all the information
generate. With the information given we are able to
produce the vertical profile for radar reflectivity15. Table
3 displays the thickness of the cloud associated with Salor
station during the longest rain event. Figure 12 presented
the information in a chart. The thickness of the cloud is
proportionally decrease with the diameter (range) of the
cloud at any height shown.
Figure 10. XSect of station salor.
Figure 11. ProductX from excel.
6
Vol 9 (25) | July 2016 | www.indjst.org
Indian Journal of Science and Technology
Atikah Balqis Basri, Ahmad Fadzil Ismail, Muhamad Haziq Khairolanuar, Nuurul Hudaa Mohd Sobli, Khairayu Badron, and
Mohammad Kamrul Hasan
Table 3. Thickness and diameter of the cloud
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
17/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
18/12/2014
14:32:21
15:02:21
15:32:21
16:32:19
17:02:32
17:32:24
18:02:22
18:32:22
19:02:24
19:32:19
20:02:23
20:32:21
21:02:21
21:32:23
22:02:20
22:32:24
23:02:24
23:32:24
0:02:25
0:32:24
1:02:19
1:32:22
2:02:24
2:32:22
3:02:22
3:32:24
4:02:22
4:32:22
5:03:23
5:32:21
6:02:21
6:32:25
7:02:23
7:32:21
Thickness (km)
10.1
8.1
9.5
6.5
4.5
6.8
5.6
3.6
3.6
4.2
3.7
4.4
4.3
3.8
5.8
4.2
4.2
2.8
2.2
1.3
1.7
2.0
1.0
0.7
1.3
3.2
3.9
4.6
2.1
3.6
3.2
1.4
1.9
3.7
Diameter for 1km height
136.1
135.9
140.2
120.5
118.1
104.5
102.6
101.6
166.5
182.5
189.8
182.0
167.1
156.6
118.1
114.6
123.8
117.2
114.8
108.0
111.5
99.4
88.9
68.9
56.6
57.2
56.2
62.3
97.8
108.6
103.2
96.1
82.3
69.0
Diameter for 2km height
159.8
168.7
184.1
174.1
146.9
138.9
130.8
139.3
217.8
248.3
254.7
223.8
208.6
217.8
226.8
235.9
216.7
232.5
214.5
203.8
207.6
184.6
193.3
169.6
110.9
99.8
57.9
108.1
151.3
179.6
146.8
109.2
113.1
107.4
Di a meter for 2km hei ght
Thi cknes s (km)
12
10
8
6
4
2
0
Thickness (km)
800
700
600
500
400
300
200
100
0
14:32:21
15:02:21
15:32:21
16:32:19
17:02:32
17:32:24
18:02:22
18:32:22
19:02:24
19:32:19
20:02:23
20:32:21
21:02:21
21:32:23
22:02:20
22:32:24
23:02:24
23:32:24
0:02:25
0:32:24
1:02:19
1:32:22
2:02:24
2:32:22
3:02:22
3:32:24
4:02:22
4:32:22
5:03:23
5:32:21
6:02:21
6:32:25
7:02:23
7:32:21
Diameter (km)
Di a meter for 1km hei ght
Di a meter for 3km hei ght
Diameter for 3km height
205.4
208.9
221.1
215.2
171.0
197.5
272.9
279.2
306.6
285.7
281.1
262.8
210.1
261.1
274.8
239.7
248.6
261.1
246.8
247.7
259.7
244.6
225.6
283.2
282.7
276.7
165.8
141.5
24.1
116.0
112.0
161.5
118.8
109.0
12/17/2014
12/18/2014
Time (00:00:00)
Figure 12. Graph of thickness and diameter of the cloud.
Vol 9 (25) | July 2016 | www.indjst.org
Indian Journal of Science and Technology
7
Analyses of Cloud Characteristic during Malaysian 2014 Flood Event
4. Conclusions
The analyses observed in this research allow the estimation
of the cloud characteristic based on rain gauge, river basin
data and radar data during Malaysia worst flood in 2014.
Details estimation is using horizontal profile and vertical
profile of radar. Based on the observation, the cloud
appears to be extreme in size during the flood event. The
expectancy of the future work is basically to derive the
coefficient for an algorithm or prediction technique that
allows us to predict incoming flood so that we will be
notified if the similar behaviour of the cloud exist. This
will create an alarm system. Another set of radar data
from the previous flood disaster in 2013 will be bought
from Malaysia Meteorological Department (MMD) for
algorithm formulation and validation.
5. Acknowledgement
The authors recognize the Research Management Centre
of the International Islamic University Malaysia (IIUM)
and The Malaysian Ministry of Education for the financial
assistance and support to the researcher. The reported
research outcome is part of the deliverables for the research
funded under IIUM’s Research University Initiatives. This
research is being sponsored under Fundamental Research
Grant Scheme (FRGS) Research Project by Malaysian
Ministry of Education.
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Indian Journal of Science and Technology