MALAYSIAN METEOROLOGICAL DEPARTMENT
MINISTRY OF SCIENCE, TECHNOLOGY AND INNOVATION
(MOSTI)
Utilizing Remote Sensing in Analysis
of Sea Surface Temperature from
MODIS Data (MOD28)
Project Leader:
Rosita Galang
Researchers:
1. Rosmadinor Mohamad
2. Maqrun Fadzli Mohd Fahmi
Section/Division:
Meteorological Satellite Division
ABSTRACT:
Over the oceanic regions and also the rugged terrain and remote areas, only very
sparse information about the state of atmosphere and underlying surface is possible
by existing conventional observational network. The satellite derived information over
these data sparse regions is the only cost effective solution to provide input over
these regions. Satellites help in the study of climatology by the instruments on board
the satellites that can be used to obtain important factors such as sea surface
temperature which can, then, be used to analyze the climate on regional and global
scales. Therefore in this paper we will analyze and make verification of the sea
surface temperatures derived from MODIS (MOD28) data with the in situ sea surface
temperatures reported from ships and buoys during 2009 in the marine region
around Malaysia. MOD28 data is the sea surface temperature product from level 2
MODIS ocean product which provides ocean surface temperature in degree Celsius
( C).
-1-
1.0
INTRODUCTION
Sea surface temperature is one of the important parameter or variable
to a better understanding of the interactions between the ocean and the
atmosphere condition. Numbers of extended historical conventional observed
temperature analyses have been produced for use in climate studies and
climate monitoring, therefore the verification of the sea surface temperatures
derived from MODIS (MOD28) data in the marine region around Malaysia will
be very useful in climate studies and monitoring to overcome the shortage of
data that cannot be observed by the conventional method.
The specific objectives of this paper are as follows;
a)
to study on the utilization of the meteorological MODIS (MOD28) data
using HDFLook software which is a new multifunctional data
processing and visualization tool software created for HDF-EOS Land,
Ocean and Atmosphere MODIS Products.
b)
to analyze and verify the sea surface temperature from Terra MODIS
(MOD28) data with actual sea surface temperature that observed by
auxiliaries ship/boat.
2.0
DATA USED IN THE STUDY
True sea surface temperature (SST) is defined by Robinson, Well &
Charnock (1984) as the temperature of the first few millimeters of the ocean
surface (skin). Since this is exactly what a radiometer viewing the ocean
surface measure (Schluessel et al, 1990), it can be said that remotely sensed
values for SST constitute the true SST. However Robinson (1984) points out
that the temperature difference driving the heat flow is that between the sea
temperature few centimeters below the surface and the atmospheric
temperature. Thus conceptually the temperature below the skin may be the
most appropriate definition of SST as well as the most useful oceanographic
parameter.
In this study, two types of data that is used in this study are SST data
observations from ships and buoys and satellite SST data retrievals.
-2-
a)
In-situ data
The in-situ data are from observations from ships obtained
from the Malaysian Marine Meteorology and Oceanography Division
which centered at Labuan, Sabah. The data is derived almost entirely
from observations taken on Voluntary Observing Ship (VOS) scheme
and thus is concentrated in shipping lanes. The ships used hull contact
sensor and engine condenser intakes at depths of one to several
meters to obtain the SSTs. The ships observations time were usually
on 0000 UTC, 0600 UTC, 1200 UTC and 1800 UTC. But, for this study,
only 0600 UTC and 1800 UTC SSTs data were being used because
usually the time satellite EOS Terra & Aqua pass Malaysia region will
be around 0600 UTC and 1800 UTC.
The actual SSTs obtained from the in situ SSTs reported from
the on Voluntary Observing Ship (VOS) scheme for the month of
January untill December 2009 in the marine regions around Malaysia.
VOS program is always using selected ship to get the SSTs reading.
This kind of ship is equipped with certified meteorological instruments
for making observations. The observer on board the VOS will record
regular weather reports and observations in meteorological logbooks.
The VOS have a barometer, a thermometer for sea-surface
temperature, a psychrometer and a barograph instruments onboard.
b)
MODIS (MOD28) Satellite data
The satellite data were taken from Malaysian Meteorological
Satellite Division MODIS (MOD28) data and downloaded from the
website http://webmodis.iis.u-tokyo.ac.jp. These data were from Terra
MODIS (MOD28) data. The data are in hdf file format. The data were
taken referring to the available in-situ data observation time. Data that
observed near or on 0600 and 1800 UTC, are used in this study.
These data will be processed by using HDFLook software, which is one
of remote sensing software. Then only the SSTs data obtained ready to
be analysed.
-3-
3.0
ANALYSIS
3.1
STUDY AREA
The areas of interest for the scope of this paper are as follow and also
show in Figure 1:
Peninsular Malaysia (latitude: 1 N – 5 N; longitude: 100 E -105 E)
a)
b)
South Malacca Strait (South S.M)
Latitude
: 103 E – 104 E
Longitude
:1 N–2 N
North Malacca Strait (North S.M)
Latitude
: 101 E – 102 E
Longitude
:1 N–3 N
Figure 1: Identified Study Area
-4-
3.2
METHODOLOGY
Figure 2: Flow Diagram of Methodology for the scope of project
Based on Figure 2, first the MODIS (MOD28) data of SSTs around
Malaysia is downloaded from Satellite Meteorology Division archiving MODIS
System and from the website http://webmodis.iis.u-tokyo.ac.jp. These data
are derived from Terra MODIS Sea Surface Temperature (SST) and are in
hdf file format. Figure 3 shows the satellite images of sea surface
temperature from Terra MODIS satellite.
-5-
Figure 3: Satellite Images of Sea Surface Temperatures in Malaysia region
Then these MOD28 data will be processed using HDFLook software to
extract the sea surface temperature. The latitude and longitude of the in situ
observation is used to compare with the location of the SST being observed
in the satellite image to make the verification. From the HDFLook software,
the SST values can be obtained on the particular latitude and longitude which
are the same with the in situ data. Only after this process, the comparison
between the actual SST and the satellite SST values can be verified.
Then the graph of actual SST from in situ observation versus satellite
SST is plotted according to months and year of 2009. From the graph, the
correlation coefficient of these two data can be obtained where the correlation
coefficient will determined the relation between these two sea surface
temperature data.
3.3
DATA ANALYSIS
The following table shows the details of Sea Surface Temperature
(SST) according to date/time observation, latitude and longitude and the
temperature values from actual temperature and MOD28 data. These details
were collected along the Strait of Malacca from January until December 2009.
-6-
Table 1: Table shows the details of SSTs data along the Strait of
Malacca in January 2009
Date/time
Lat
Long
Actual
Mod28
Mod28-Act
01/01/2009/0600
2.3
104.9
28.0
26.6
-1.4
04/01/2009/0600
2.7
104.6
30.0
-26.2
-56.2
05/01/2009/1800
1.3
104.2
27.0
22.3
-4.7
06/01/2009/1800
1.3
104.2
26.0
24.9
-1.2
08/01/2009/1800
2.8
104.9
26.0
-10.9
-36.9
10/01/2009/1800
1.3
104.2
26.0
23.5
-2.5
12/01/2009/0600
1.9
102.2
28.0
26.8
-1.3
13/01/2009/1800
2.4
105.0
27.2
27.1
-0.1
15/01/2009/1800
3.1
100.7
29.0
28.6
-0.4
17/01/2009/0600
1.3
104.2
26.0
25.9
-0.1
18/01/2009/0600
1.3
104.2
25.0
24.1
-0.9
20/01/2009/1800
1.3
104.2
25.0
26. 6
-1.6
22/01/2009/0600
4.2
104.2
21.8
21.9
-0.1
Table 2: Table shows the details of SSTs data along the Strait of
Malacca in February 2009
Date/time
Lat
Long
Actual
Mod28
Mod28-Act
04/02/2009/1800
4.4
99.5
28.4
-7.7
-36.1
05/02/2009/0600
4.0
99.8
28.9
29.3
0.4
09/02/2009/1800
3.5
100.2
28.0
27.8
-0.2
16/02/2009/0600
3.4
100.2
28.2
30.7
2.5
18/02/2009/0600
1.3
104.1
27.0
27.1
0.1
19/02/2009/1800
2.7
101.2
28.5
22.1
-6.4
21/02/2009/1800
2.5
105.0
27.0
26.5
-0.5
23/02/2009/0600
4.1
99.5
29.0
23.5
-5.5
-7-
Table 3: Table shows the details of SSTs data along the Strait of
Malacca in March 2009
Date/time
Lat
Long
Actual
Mod28
Mod28-Act
14/03/2009/0600
1.3
104.2
28.0
26.9
-1.1
15/03/2009/0600
1.3
104.2
28.0
27.3
-0.7
17/03/2009/0600
1.3
104.2
28.4
29.7
1.4
18/03/2009/1800
1.3
104.2
28.3
29.4
1.1
27/03/2009/1800
1.3
104.1
28.0
29.4
1.4
28/03/2009/1800
1.3
104.1
28.0
29.2
1.2
Table 4: Table shows the details of SSTs data along the Strait of
Malacca in April 2009
Date/time
Lat
05/04/2009/1800
6.4
16/04/2009/0600
Long
Actual
Mod28
Mod28-Act
103.6
27.6
-12.6
-40.2
4.6
99.4
30.1
-2.3
-32.4
19/04/2009/0600
4.3
99.3
33.0
-19.6
-52.6
20/04/2009/1800
1.2
103.9
30.1
20.5
-9.6
22/04/2009/0600
1.2
103.8
30.2
28.7
-1.5
23/04/2009/1800
2.5
105.0
29.0
30.5
1.5
24/04/2009/0600
1.6
102.8
29.9
31.3
1.4
25/04/2009/1800
1.2
103.8
30.5
30.5
0.0
26/04/2009/0600
3.4
104.4
30.8
29.9
-0.9
28/04/2009/0600
4.2
99.2
29.0
22.3
-6.7
-8-
Table 5: Table shows the details of SSTs data along the Strait of
Malacca in May 2009
Date/time
Lat
Long
Actual
03/05/2009/0600
1.2
103.8
30.5
23.3
-7.2
05/05/2009/1800
1.2
103.8
30.4
0.0
-30.4
06/05/2009/1800
1.2
103.8
30.5
0.0
-30.5
11/05/2009/1800
1.6
104.6
30.0
26.6
-3.37
12/05/2009/1800
2.7
101.2
30.4
28.5
-1.9
14/05/2009/1800
1.7
104.9
28.0
26.1
-1.9
15/05/2009/0600
2.7
101.2
30.4
27.9
-2.5
16/05/2009/1800
2.7
101.2
30.5
29.9
-0.6
17/05/2009/1800
2.7
101.2
30.4
0.0
-30.4
19/05/2009/0600
2.3
101.7
31.6
31.1
-0.5
20/05/2009/1800
3.1
104.4
31.0
30.4
-0.6
21/05/2009/0600
3.0
101.3
30.9
0.0
-30.9
22/05/2009/1800
2.4
101.5
33.4
0.0
-33.4
24/05/2009/1800
3.0
101.3
31.1
0.0
-31.1
25/05/2009/1800
3.0
101.3
31.1
0.0
-31.1
28/05/2009/0600
2.7
101.2
31.1
31.3
0.2
30/05/2009/1800
3.0
101.3
31.1
0.0
-31.1
31/05/2009/0600
3.0
101.3
31.1
0.0
-31.1
-9-
Mod28
Mod28-Act
Table 6: Table shows the details of SSTs data along the Strait of
Malacca in June 2009
Date/time
Lat
Long
Actual
Mod28
Mod28-Act
01/06/2009/0600
3.0
101.3
31.2
-163.8
-195.0
03/06/2009/1800
3.0
101.3
31.2
-163.8
-195.0
05/06/2009/1800
2.7
101.2
28.0
26.4
-1.6
06/06/2009/0600
3.6
100.0
30.0
-163.8
-193.8
12/06/2009/0600
1.3
104.3
31.1
28.5
-2.6
13/06/2009/0600
4.7
104.0
31.0
31.2
0.2
15/06/2009/0600
4.8
99.2
31.0
31.1
0.1
17/06/2009/0600
2.5
101.5
30.0
28.4
-1.6
19/06/2009/0600
1.6
102.5
29.0
30.4
1.4
22/06/2009/0600
3.0
101.3
31.2
-163.8
-195.0
24/06/2009/0600
1.2
103.6
28.5
-40.7
-69.2
26/06/2009/0600
4.1
104.1
30.6
30.0
-0.6
29/06/2009/0600
2.0
102.1
32.0
31.8
-0.2
- 10 -
Table 7: Table shows the details of SSTs data along the Strait of
Malacca in July 2009
Date/time
Lat
Long
Actual
Mod28
04/07/2009/0600
4.5
99.8
34.0
31.1
-2.9
06/07/2009/0600
3.9
99.6
30.0
27.7
-2.3
06/07/2009/1800
3.2
104.6
27.0
30.4
3.4
08/07/2009/0600
3.3
99.4
26.0
24.3
-1.7
10/07/2009/0600
1.5
102.9
30.5
-2.3
-32.8
10/07/2009/1800
2.2
104.8
27.0
-52.4
-79.4
12/07/2009/0600
3.6
100.1
29.0
-1.5
-30.5
15/07/2009/1800
2.4
101.6
30.0
-33.9
-63.9
17/07/2009/0600
4.5
99.0
30.2
26.7
-3.5
18/07/2009/0600
6.2
103.2
29.5
-3.3
-32.8
19/07/2009/0600
3.1
104.3
29.0
27.7
-1.3
20/07/2009/0600
2.2
104.9
28.0
24.0
-4.0
21/07/2009/0600
1.4
102.3
30.0
31.8
1.8
21/07/2009/1800
3.3
100.6
28.3
-11.2
-39.5
22/07/2009/0600
3.2
100.5
29.8
33.3
3.5
22/07/2009/1800
1.1
103.6
29.0
-4.4
-33.4
23/07/2009/1800
3.9
99.9
30.0
-31.2
-61.2
24/07/2009/1800
2.3
101.7
29.5
-69.1
-98.6
27/07/2009/0600
2.8
105.0
31.3
-53.6
-84.9
- 11 -
Mod28-Act
Table 8: Table shows the details of SSTs data along the Strait of
Malacca in August 2009
Date/time
Lat
Long
Actual
Mod28
Mod28-Act
02/08/2009/0600
3.4
100.3
30.0
31.3
1.3
18/08/2009/1800
6.8
102.5
30.0
27.2
-2.8
20/08/2009/1800
3.3
100.4
30.0
-19.6
-49.6
24/08/2009/0600
2.6
104.9
29.0
-72.8
-101.8
26/08/2009/0600
4.6
100.0
28.5
29.8
1.3
29/08/2009/0600
1.2
103.8
32.1
-28.0
-60.1
31/08/2009/0600
1.2
103.8
31.8
-12.7
-44.5
Table 9: Table shows the details of SSTs data along the Strait of
Malacca in September 2009
Date/time
Lat
Long
Actual
05/09/2009/0600
2.3
104.9
30.1
24.1
-6.0
15/09/2009/1800
5.7
99.1
29.0
-63.6
-92.6
17/09/2009/1800
1.3
104.3
32.6
-27.2
-59.8
19/09/2009/0600
1.3
104.3
32.2
-17.9
-50.1
21/09/2009/0600
3.0
100.7
33.0
30.7
-2.3
22/09/2009/0600
1.3
104.3
33.7
-50.1
-83.7
23/09/2009/0600
1.3
104.3
33.2
31.6
-1.6
24/09/2009/0600
1.3
104.3
33.7
30.4
-3.3
25/09/2009/1800
2.1
101.9
30.0
-18.8
-48.8
26/09/2009/0600
3.2
100.4
30.2
-18.1
-48.3
26/09/2009/1800
1.2
104.1
29.7
-26.9
-56.6
28/09/2009/0600
1.3
104.3
34.5
28.7
-5.8
29/09/2009/1800
1.3
104.3
33.7
-6.8
-40.5
30/09/2009/0600
1.3
104.3
34.5
26.4
-8.1
30/09/2009/1800
1.3
104.3
33.9
-25.9
-59.8
- 12 -
Mod28
Mod28-Act
Table 10: Table shows the details of SSTs data along the Strait of
Malacca in October 2009
Date/time
Lat
Long
Actual
Mod28
Mod28-Act
02/10/2009/0600
1.3
104.3
33.8
30.9
-2.9
02/10/2009/1800
3.5
100.2
30.9
-44.4
-75.3
03/10/2009/0600
1.3
104.3
33.6
21.1
-12.5
05/10/2009/0600
1.3
104.3
34.0
-50.9
-84.9
07/10/2009/0600
1.3
104.3
33.8
-6.1
-39.9
10/10/2009/0600
1.6
104.7
28.8
24.3
-4.5
11/10/2009/1800
1.4
104.6
33.9
24.8
-9.1
12/10/2009/1800
2.1
104.9
29.1
27.3
-1.8
14/10/2009/0600
4.1
99.7
30.1
-24.2
-54.3
15/10/2009/0600
3.9
99.6
30.1
30.9
0.8
16/10/2009/0600
1.8
104.4
33.5
27.7
-5.8
18/10/2009/0600
1.8
104.4
30.0
27.8
-2.2
20/10/2009/0600
3.7
104.6
29.4
21.8
-7.6
21/10/2009/0600
1.8
104.4
30.0
25.9
-4.1
21/10/2009/1800
1.8
104.4
28.0
28.2
0.2
22/10/2009/1800
2.7
104.3
30.0
-9.2
-39.2
23/10/2009/0600
1.8
104.4
30.0
27.8
-2.3
23/10/2009/1800
1.8
104.4
28.0
-9.1
-37.1
25/10/2009/0600
1.8
104.4
30.0
21.0
-9.1
26/10/2009/0600
1.8
104.4
30.0
30.2
0.2
27/10/2009/0600
1.8
104.4
30.0
31.1
1.1
27/10/2009/1800
2.8
101.2
29.6
-1.5
-31.1
28/10/2009/0600
1.8
104.4
30.0
22.1
-7.9
- 13 -
Table 11: Table shows the details of SSTs data along the Strait of
Malacca in November 2009
Date/time
Lat
01/11/2009/0600
1.4
03/11/2009/1800
Long
Actual
Mod28
Mod28-Act
104.6
30.2
29.9
-0.3
1.8
104.4
30.0
24.9
-5.1
04/11/2009/0600
1.8
104.4
30.0
19.4
-10.6
04/11/2009/1800
1.8
104.4
30.0
-14.8
-44.8
06/11/2009/0600
4.3
99.1
29.0
-15.3
-44.3
10/11/2009/1800
1.8
104.4
30.0
26.5
-3.5
13/11/2009/0600
3.2
100.5
28.0
-15.0
-43.0
21/11/2009/0600
1.6
104.7
31.0
-17.5
-48.6
23/11/2009/0600
2.0
102.0
28.8
-33.1
-61.9
Table 12: Table shows the details of SSTs data along the Strait of
Malacca in December 2009
Date/time
Lat
Long
Actual
Mod28
Mod28-Act
02/12/2009/0600
3.5
104.4
27.8
-20.6
-48.4
04/12/2009/1800
2.8
101.2
27.0
-8.6
-35.6
05/12/2009/1800
2.2
104.8
28.0
-7.0
-35.0
13/12/2009/0600
4.2
99.3
29.5
30.8
1.3
14/12/2009/0600
1.3
104.6
27.8
29.3
1.5
14/12/2009/1800
3.2
100.5
31.0
23.2
-7.9
20/12/2009/0600
1.3
104.3
28.0
28.2
0.2
30/12/2009/1800
3.7
100.0
31.5
27.2
-4.3
- 14 -
From the tables above, we can see that there were no satellite SST data that
absolutely similar with the actual SST. There were only almost similar data,
underestimated or overestimated satellite SST and negative data values. There were
no problem with the satellite SSTs data which almost near to the actual SSTs as
there were just a slightly difference between them. However, the problem comes if
the satellite SSTs have big difference compared to actual SSTs. For example, on 14
December 2009, the satellite SST was 23.2ºC meanwhile the actual SST was
31.0ºC. The difference between these two SSTs is 7.9ºC and this will lead to
underestimated data.
Moreover, there were also negative values on the data. For example, on 23
November 2009, the satellite SST derived from HDFLook software was -33.1 ºC
while the actual SST was 28.8 ºC. The negative values recorded by the MODIS
satellite in the above tables are because of the cloud cover which the MODIS
satellite sensor cannot penetrate thru. Therefore the negative values represent the
temperature of the cloud, not the SST value. For example, on that 23 November
2009 at 0600 UTC at latitude 2.0 ºN and longitude 102 ºE there were cloud covers.
The Figure 4 shows that on that particular observation time (0600 UTC), the clouds
cover blocking the satellite sensor to detect the SST value at that location.
Cloud
s
Figure 4: Figure above shows the presence of clouds (white color)
- 15 -
From table 6, there are repeating negative data with the same value which is 163.8ºC. During the processing of MOD28 data to derive the sea surface
temperature (SST) value, it shows that the black pixel will always show the value 163.8ºC. This value shows that the area of interest is out of satellite coverage area
(satellite footprint) (see figure 5).
Figure 5: Black pixel shows the negative degree SST
The red pixel in the Figure 6 below, shows the positive value represent the
SST which detected by the MODIS satellite sensor.
Figure 6: Red pixel shows the positive value that represents SST
- 16 -
From the sea surface temperature legend in Figure 7, black color represents
no data, green color represent temperature in range of 21.0ºC to 24.0ºC meanwhile
yellow color represent temperature in range of 26.0ºC to 28.0ºC. For orange color,
the temperature is between the range of 29.0ºC and 30.0ºC meanwhile red color
represent temperature larger than 30.0ºC.
Figure 7: Sea Surface Temperature legend
Therefore in this paper, all the negative values have to be rejected because
these values were not representing the SST values from the MODIS satellite sensor
as it could be the cloud temperature values. Zero (0) value also need to be rejected
as it represent no data.
3.4
GRAPH ANALYSIS
Based on Tables 1 - 12, the monthly data graphs are plotted. The x-axis
shows the date and time which is in Coordinated Universal Time (UTC). Meanwhile
the y-axis shows the temperature values in degree Celsius (ºC). To plot these graphs
missing data from ship observations and MODIS data which corresponding to the
missing ship data are removed and the negative value from satellite which is not
representing the MOD28 SST data are also omitted.
- 17 -
Figure 8: Graph for January 2009 SST data
Figure 8 shows the actual SST and Terra (MODIS SST) in January 2009 of oceans around Peninsular Malaysia. From
the graph, almost similar patterns of trend are shown between actual SST and satellite SST data. These lines show that there were
almost equal value of increase and decrease of SSTs values. This means that, when actual SST value is decrease, the satellite
SST value also decreases.
- 18 -
Figure 9: Graph for February 2009 SST data
Figure 9 shows the actual SST and Terra (MODIS SST) on 5,9,18 and 21 February. The graph lines of actual SST and
satellite SST are almost the same as some of the values are overlay to each other. Therefore it is proven that the satellite SST data
are reliable in the month of February.
- 19 -
Figure 10: Graph for March 2009 SST data
Figure 10 shows that the plotted graph for the month of March 2009, obviously shows underestimated and overestimated
satellite SST data. But, the pattern of the graph line is almost similar on the 17, 18, 27 and 28 March 2009.
- 20 -
Figure 11: Graph for April 2009 SST data
Figure 11 shows that the plotted graph line is almost in the same pattern. Therefore it is proven that the satellite SST data
are reliable for the month of April 2009.
- 21 -
Figure 12: Graph for May 2009 SST data
Figure 12 shows almost similar graph pattern for the observation in the month of May 2009. Therefore it is proven that the
satellite SST data are reliable for the month of May 2009.
- 22 -
Figure 13: Graph for June 2009 SST data
Figure 13 shows, almost similar graph pattern for the observation in the month of June 2009. The value of actual SST and
satellite SST were very close to each other for the identified observations in the month of June 2009.
- 23 -
Figure 14: Graph for July 2009 SST data
Figure 14 shows almost similar trend of graph pattern for the actual SST and satellite SST value. Therefore it is proven that
the satellite SST data are reliable for the month of July 2009.
- 24 -
Figure 15: Graph for August 2009 SST data
Figure 15 shows that not enough satellite SST data available as the satellite sensor was block by the cloud cover. Not a
good graph pattern can be plotted for actual SST and satellite SST comparison.
- 25 -
Figure 16: Graph for September 2009 SST data
Figure 16 shows that the actual SST and satellite Terra MODIS SST in September 2009. From 5 to 23 September, the
pattern of the graph is almost in similar trend for same actual SST and satellite SST value. Therefore it is proven that the satellite
SST data are reliable as it is having almost the same value as the actual SST.
- 26 -
Figure 17: Graph for October 2009 SST data
Figure 17 show that there are two difference graph patterns for the actual SST and satellite SST value.
- 27 -
Figure 18: Graph for November 2009 SST data
Figure 18 shows that there is not enough satellite SST data available as the satellite sensor was block by the cloud cover.
Therefore not a good graph pattern can be plotted for the actual SST and satellite SST comparison.
- 28 -
Figure 19: Graph for December 2009 SST data
Figure 19 shows that there is not enough satellite SST data available as the satellite sensor was block by the cloud cover.
Therefore not a good graph pattern can be plotted for the actual SST and satellite SST comparison.
- 29 -
Figure 20: Graph show the actual SST and satellite Terra (MODIS) SST for 2009.
Figure 20 shows that there is almost similar graph trend pattern for the satellite SST and actual SST for the month of January,
February, Mac, May, June and September. But, there is also underestimated satellite SST data compared to actual SST data. For
the earlier observation, the graph shows almost similar pattern. However, starting from July 2009, the pattern of the graph obviously
different from each other. This is mainly because there is not enough satellite SST data due to cloud cover over our study area of
interest which caused the satellite Terra (MODIS) sensor unable to detect the sea surface temperature (SST) value.
- 30 -
4.0
RESULTS AND DISCUSSIONS
4.1
CORRELATION COEFFICIENT
The Pearson product-moment correlation coefficient (r) is a measure
of the correlation (linear dependence) between two variables X and Y,
giving a value between +1 and −1 inclusive. It is widely used in the
sciences as a measure of the strength of linear dependence between two
variables. The correlation coefficient ranges from −1 to 1. A value of 1
implies that a linear equation describes the relationship between X and Y
perfectly, with all data points lying on a line for which Y increases as X
increases. A value of −1 implies that all data points lie on a line for which
Y decreases as X increases. A value of 0 implies that there is no linear
correlation between the variables.
4.2
COEFFICIENT OF DETERMINATION, r 2 or R2
The coefficient of determination, r 2, is useful because it gives the
proportion of the variance (fluctuation) of one variable that is predictable
from the other variable. The coefficient of determination is a measure of
how well the regression line represents the data. If the regression line
passes exactly through every point on the scatter plot, it would be able to
explain all of the variation. The further the line is away from the points, the
less it is able to explain.
The coefficient of determination is such that 0 < r
2
< 1, and
denotes the strength of the linear association between x and y. The
coefficient of determination represents the percent of the data that is the
closest to the line of best fit. For example, if r = 0.9, then r
2
= 0.8, which
means that 85% of the total variation in y can be explained by the linear
relationship between x and y. The other 15% of the total variation in y
remains unexplained.
- 31 -
4.3
CORRELATION COEFFICIENT RESULT
To show the relation between actual SST and satellite SST, the
regression line charts for actual SST vs Terra (MODIS) SST for the year of
2009 were plotted as shown in Figure 21 to Figure 29.
Figure 21: Regression line charts for actual SST vs Terra (MODIS) SST
for the January 2009
Figure 22: Regression line charts for actual SST vs Terra (MODIS) SST
for February 2009.
- 32 -
Figure 23: Regression line charts for actual SST vs Terra (MODIS) SST
for March 2009.
Figure 24: Regression line charts for actual SST vs Terra (MODIS) SST
for April 2009.
Figure 25: Regression line charts for actual SST vs Terra (MODIS) SST
for May 2009.
- 33 -
Figure 26: Regression line charts for actual SST vs Terra (MODIS) SST
for June 2009.
Figure 27: Regression line charts for actual SST vs Terra (MODIS) SST
for July 2009.
Figure 28: Regression line charts for actual SST vs Terra (MODIS) SST
for September 2009.
- 34 -
Figure 29: Regression line charts for actual SST vs Terra (MODIS) SST
for October 2009.
Missing data in ship observations are removed. Days corresponding to the
missing ship data days are also removed from the MODIS data. The negative
data from satellite are also omitted. Here the regression line charts for the months
of August, November and December are not plotted as not enough data are
available.
After the plotting of regression line charts, the table of correlation
coefficient values was obtained. Table 13 shows the values of the correlation
coefficient for the months of January to July, September and October 2009.
- 35 -
Table 13: The monthly correlation coefficient of actual SST and satellite SST
Month
Coefficient of Determination Correlation Coefficient
(R²)
(r)
January
0.476
0.690
February
0.029
0.170
March
0.346
0.589
April
0.069
0.263
May
0.322
0.567
June
0.510
0.714
July
0.254
0.504
September
0.252
0.502
October
0.001
0.026
.
Based on Table 13, all the considered Correlations Coefficient (r) shows a
positive value except for the month of October. Positive values indicate a
relationship between x and y variables such that as values for x increases, values
for y also increasing. For the month of October 2009, there was no correlation as
the linear correlation is 0.026. This means that there is nonlinear correlation
between actual SST and Terra (MODIS) SST on that month.
The moderate correlation can be seen in January, March, May, June and
September as in these months, the linear correlations are in range of below 0.8
and above 0.5. This represents an excellent match between what is detect by the
MODIS sensor and the observation at the sea surface.
- 36 -
4.4
Temperature Anomaly
The sea surface temperature anomaly is the difference between the
day’s temperature and the long term average. Positive numbers mean the
temperature warmer than average meanwhile the negative numbers mean
the temperature is cooler than average. The formula to calculate the SST
anomaly is:
SST anomaly = Actual SST – Average SST
Or
Satellite SST – Average SST
After the anomaly been calculated, the average monthly anomaly
has to be derived. This can be done by total up the monthly SST anomaly
and then divide the value to the number of observations of that month. For
example:
January 2009
∑SST anomaly for actual SST (x) = -6.9
Number of observations (y)
= 11
January actual SST anomaly
=x÷y
= -6.9 ÷ 11
= -0.6
Tables 14 - 22 show the sea surface temperature (SST) anomaly for each
observation time in the year 2009.
- 37 -
1) January 2009. Note that the average SST in this month is 26.9ºC.
Table 14: SST anomaly for January 2009
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
01/01/2009/0600
28.0
26.6
1.1
-0.3
05/01/2009/1800
27.0
22.3
0.1
-4.6
06/01/2009/1800
26.0
24.9
-0.9
-2.1
10/01/2009/1800
26.0
23.5
-0.9
-3.5
12/01/2009/0600
28.0
26.8
1.1
-0.2
13/01/2009/1800
27.2
27.1
0.3
0.2
15/01/2009/1800
29.0
28.6
2.1
1.7
17/01/2009/0600
26.0
25.9
-0.9
-0.9
18/01/2009/0600
25.0
24.1
-1.9
-2.9
20/01/2009/1800
25.0
26.6
-1.9
-0.3
22/01/2009/0600
21.8
21.9
-5.1
-5.0
2) February 2009. Note that the average SST in this month is 27.9 ºC.
Table 15: SST anomaly for February 2009
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
05/02/2009/0600
28.9
29.3
1.0
1.4
09/02/2009/1800
28.0
27.8
0.1
-0.1
16/02/2009/0600
28.2
30.7
0.3
2.8
18/02/2009/0600
27.0
27.1
-0.9
-0.8
19/02/2009/1800
28.5
22.1
0.6
-5.8
21/02/2009/1800
27.0
26.5
-0.9
-1.5
23/02/2009/0600
29.0
23.5
1.1
-4.4
- 38 -
3)
March 2009. Note that the average SST in this month is 27.9 ºC.
Table 16: SST anomaly for March 2009
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
14/03/2009/0600
28.0
26.9
0.1
-0.9
15/03/2009/0600
28.0
27.3
0.1
-0.6
17/03/2009/0600
28.4
29.7
0.5
1.9
18/03/2009/1800
28.3
29.4
0.4
1.5
27/03/2009/1800
28.0
29.4
0.1
1.5
28/03/2009/1800
28.0
29.2
0.1
1.3
4)
April 2009. Note that the average SST in this month is 29.7 ºC.
Table 17: SST anomaly for April 2009
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
20/04/2009/1800
30.1
20.5
0.4
-9.3
22/04/2009/0600
30.2
28.7
0.5
-0.9
23/04/2009/1800
29.0
30.5
-0.7
0.9
24/04/2009/0600
29.9
31.3
0.2
1.6
25/04/2009/1800
30.5
30.5
0.8
0.8
26/04/2009/0600
30.8
29.9
1.1
0.2
28/04/2009/0600
29.0
22.3
-0.7
-6.9
- 39 -
5)
May 2009. Note that the average SST in this month is 30.3 ºC
Table 18: SST anomaly for May 2009
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
03/05/2009/0600
30.5
23.3
0.2
-7.0
11/05/2009/1800
30.0
26.6
-0.3
-3.7
12/05/2009/1800
30.4
28.5
0.1
-1.9
14/05/2009/1800
28.0
26.2
-2.3
-4.2
15/05/2009/0600
30.4
27.9
0.1
-2.4
16/05/2009/1800
30.5
29.9
0.2
-0.4
19/05/2009/0600
31.6
31.1
1.3
0.8
20/05/2009/1800
31.0
30.4
0.7
0.1
28/05/2009/0600
31.1
31.3
0.8
1.0
6) June 2009. Note that the average SST in this month is 30.8 ºC.
Table 19: SST anomaly for June 2009
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
05/06/2009/1800
28.0
26.4
-2.8
-4.395
12/06/2009/0600
31.1
28.5
0.3
-2.265
13/06/2009/0600
31.0
31.2
0.2
0.38
15/06/2009/0600
31.0
31.1
0.2
0.31
17/06/2009/0600
30.0
28.4
-0.8
-2.44
19/06/2009/0600
29.0
30.4
-1.8
-0.36
26/06/2009/0600
30.6
30.0
-0.2
-0.8
29/06/2009/0600
32.0
31.8
1.2
1.0
- 40 -
7)
July 2009. Note that the average SST in this month is 30.0ºC.
Table 20: SST anomaly for July 2009
8)
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
04/07/2009/0600
34.0
31.1
4.0
1.1
06/07/2009/0600
30.0
27.7
0.0
-2.3
06/07/2009/1800
27.0
30.4
-3.0
0.4
08/07/2009/0600
26.0
24.3
-4.0
-5.7
17/07/2009/0600
30.2
26.7
0.2
-3.3
19/07/2009/0600
29.0
27.7
-1.0
-2.3
20/07/2009/0600
28.0
24.0
-2.0
-6.0
21/07/2009/0600
30.0
31.8
0.0
1.8
22/07/2009/0600
29.8
33.3
-0.2
3.3
September 2009. Note that the average SST in this month is 29.8 ºC.
Table 21: SST anomaly for September 2009
Date
Actual(ºC)
Mod28(ºC)
Act-Avg
Mod-Avg
05/09/2009/0600
30.1
24.1
0.3
-5.7
21/09/2009/0600
33.0
30.7
3.2
0.9
23/09/2009/0600
33.2
31.6
3.4
1.8
24/09/2009/0600
33.7
30.5
3.9
0.6
28/09/2009/0600
34.5
28.7
4.7
-1.1
30/09/2009/0600
34.5
26.4
4.7
-3.4
- 41 -
9)
October 2009. Note that the average SST in this month is 29.8 ºC.
Table 22: SST anomaly for October 2009
Date
Actual(ºC)
Mod28(ºC)
02/10/2009/0600
33.8
30.9
4.0
1.1
03/10/2009/0600
33.6
21.1
3.8
-8.7
10/10/2009/0600
28.8
24.3
-1.0
-5.5
11/10/2009/1800
33.9
24.8
4.1
-5.0
12/10/2009/1800
29.1
27.3
-0.7
-2.5
15/10/2009/0600
30.1
30.9
0.3
1.1
16/10/2009/0600
33.5
27.7
3.7
-2.1
18/10/2009/0600
30.0
27.8
0.2
-2.0
20/10/2009/0600
29.4
21.8
-0.4
-8.0
21/10/2009/0600
30.0
25.9
0.2
-3.9
21/10/2009/1800
28.0
28.2
-1.8
-1.6
23/10/2009/0600
30.0
27.8
0.2
-2.0
25/10/2009/0600
30.0
21.0
0.2
-8.8
26/10/2009/0600
30.0
30.2
0.2
0.4
27/10/2009/0600
30.0
31.1
0.2
1.3
28/10/2009/0600
30.0
22.1
0.2
-7.7
- 42 -
Act-Avg
Mod-Avg
Table 23: Monthly SST anomalies in the year of 2009
Month
Actual SST Anomaly
Satellite SST Anomaly
January
-0.6
-1.6
February
0.2
-1.2
March
0.2
0.8
April
0.2
-1.9
May
0.1
-1.9
June
-0.5
-1.1
July
-0.7
-1.4
September
3.4
-1.2
October
0.8
-3.4
According to Table 23, actual SST in the months of January, June and
July are cooler than average while for the other months the temperatures are
warmer than average. For satellite SST, most of the months have temperatures
that are cooler than average SST except for March which the SST is warmer than
average. This is because the temperature anomaly on that month is a positive
value while others are negative values.
- 43 -
Temperature Anomaly ( C)
Figure 30 was plotted from Tables 14-22 to show the relation between the actual SST anomaly and satellite SST anomaly.
Figure 30: The SST anomalies for Actual SST and Terra (MODIS SST) from January 2009 to December 2009
- 44 -
From Figure 30, we can see that there is almost similar cyclic pattern
during the period of 2009. When the actual SST is increases, the satellite SST
also increases. This goes same when the actual SST is decreases. Although the
pattern is same, the difference between actual and satellite SST still occur.
These differences show that the satellite retrievals had average negative
anomalies more than positive anomalies. This means that satellite SST derived
cool temperature more than warm temperature.
SST anomalies are nothing more than the difference from average value
and how much temperatures depart from what is normal for that time of year.
This makes sense; we might say that we had a warm temperature even though it
was still much colder than summer. What we mean is that it was warmer than a
normal temperature; in our parlance, we would say that it was a "positive
anomaly". An unusually cold temperature would be a "negative anomaly". So
that, from the graph, satellite SST derived almost all cold temperature in period
2009 as it shows most negative anomalies compared to positive anomalies.
However, actual SST shows warm temperatures more than cold
temperatures. This is because the anomalies given from actual SST are more in
positive values. This situation shows that there is a different between actual SST
and satellite derived SST in context of anomaly. It shows that satellite derived
SST tends to give lower values as compared to the observed values.
- 45 -
4.5
DISCUSSIONS
From the data tables in chapter 4, we can see that there are missing data
or no data being analyzed on certain date. This was mainly because of some
factors like no ships observations, no Malaysia satellite image or data on that
particular observations time/date and HDFLook software unable to read the
latitude and longitude that have been setup.
There are also negatives data in the tables. The negative values were
resulted from satellite hardware failures and interpretation difficulties which
related to cloud cover (cloud detection algorithm fails to function) and
atmospheric aerosols. Another cause that leads to the negative result is the
temperature of cloud tops are mixed with SSTs. If the cloud covers the sea
surface, the temperature values might be in negative values. But, if there were
no obstacle, we can get the almost similar satellite SST compared to the actual
SST.
In this study, data that contain negative values have been omitted. So that,
the plotted graphs in figure 8 to 20 shows all the positive SSTs values. For the
month which has not enough data, it is being omitted in this study. Next, a group
of regression line charts also been analysing to show the relation between actual
SST and satellite SST which have been plotted. Thus, August, November and
December data are omitted.
A correlation coefficient between the actual and satellite SST for each
month shows that both data have positive correlation except for the month of
October as it has no correlation. The anomalies of actual and satellite SST also
been computed and the plotted graph of anomalies shows that there is almost
same pattern/trend between these two data types. The anomaly shows that
satellite SST are cooler than the mean SST. There is also slightly different value
between the actual and satellite SST with the mean SST.
- 46 -
These differences occurred due to the satellite data that derived from
MODIS is a polar orbiting satellite, therefore it is common with almost all sunsynchronous polar orbiting satellites, on a given day there will be a significant
gaps in the measured data over the tropics resulting from the orbital path.
Moreover, the satellite cannot take measurements of the data below the thick
clouds. Hence a large number of missing data points exists over cloudy areas.
Furthermore, the sources of the differences between actual and satellite
SST are mainly due to the satellite captures data from above therefore it is
reasonable if the satellite transmits and receives unwanted interferences that
caused the satellite SST values to be differed from the actual SST. The
interferences are included the sampling errors like the skin effect (see figure 31).
Figure 31: Temperature Gradients at the Air-Sea Interface. This figure shows the
skin effect that affected the temperature reading.
- 47 -
5.0
CONCLUSION
In this study, details have been presented of an SST which blends
both actual and satellite data. The actual SSTs are used as benchmark
temperature while satellite SSTs analysis is used to define the shape of
final field between the benchmark. From the computation of these two
data, we can obtain the bias and anomaly of SSTs. Anomalies in sea
temperatures can also lead to anomalies in weather. In this way, SST
anomalies can serve as a kind of early warming system for weather
phenomena.
From this study, it can be concluded that Terra MODIS SST data is
reliable and can be used in meteorological application. It shows that
satellite derived SST tends to give lower values as compared to the
observed values. But, in order to estimate SST more accurately from
satellite
observations
in
the
oceans
around
Malaysia,
detailed
characteristics of the vertically or horizontally complicated temperature
structures in the ocean surface layer needs to be investigated. Besides,
the improvement of satellite observations on additional environmental
parameters such as wind, currents, cloud cover, and solar radiation are
needed. In addition to these, the high spatial resolution and accuracy of
the current data sets need to be improved. To produce high resolution
SST analyses, careful examination of the satellite algorithms, the bulk and
skin SST difference, and utilize multiple sensors have to be counted. The
improvement of in-situ observations also should be noticed to get the
perfect SSTs analysis. New efforts are required to carefully monitor the
observations from selected ships.
Lastly, this study opens the opportunity of using high resolution
Terra MODIS SST satellite data for long term weather monitoring and to
provide a case study that can be used at a much wider geographical
extent as these are reliable as one of the important inputs for ocean, fish
forecasting, numerical weather prediction, seasonal and climate models.
- 48 -
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Schluessel,P., Emery, W.J., Grassi, H & Mammen, T. (1990). On the Buls-Skin
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http://Mathbits/TISection/Statistics2/correlation.html
6.
http://coralreefwatch.noaa.gov/satellite/education/tutorial/crw21_anomaly.html.
7.
http://webmodis.iis.u-tokyo.ac.jp.
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