The Rainfall Estimation using Remote Sensing in Thailand
Miss Preeyaphorn Kosa
Ph.D. Student, Department of Water Resource Engineering, Kasetsart University, Bangkok 10900,
Thailand. E-mail: P.Kosa@cgiar.org
Dr. Kobkiat Pongput
Associate Professor, Department of Water Resource Engineering, Kasetsart University, Bangkok
10900, Thailand. E-mail: kobkiat.p@ku.ac.th
ABSTRACT: Rainfall consists of both temporal and spatial variability. Rain gauge supports
temporal resolution, on the other hand it is weakness in the quality of spatial resolution. However,
remote sensing provides good spatial resolution. According to the advantages of these
measurement methods, more precious rainfall data should be estimated from the combination of
existent rain gauge and remote sensing observation. The methodology of this paper is review the
measurement and evaluation of rainfall, which are well known, using both rain gauge and remote
sensing observation. The results are that infrared thermal and radar have the capability in order to
estimate rainfall for Thailand. Infrared thermal band is used for the estimation of rainfall by the
relationship of cloud top temperature from satellite image and rainfall from rain gauge. Radar band
is served to calculate rainfall from the relationship between radar-measure reflectivity and rain rate.
KEYWORDS: Rainfall Estimation, Infrared Thermal, Radar
1. Introduction
Both the temporal and the spatial variability are
the elements of rainfall estimation. Temporal
variability is caused by the climatic factors
governing rainfall: solar radiation, wind,
atmospheric humidity, land and seas surface
temperatures and global circulation patterns.
Ordinary spatial variability is because of (a)
landscape topography and (b) the climatic
variables listed above [13]. These climatic
parameters (including rainfall) can be recorded
by meteorological stations on the ground and by
various forms of remote sensing from satellites.
At present, rainfall is estimated by using rain
gauge and remote sensing observation. Rain
gauge supports temporal resolution on the
other hand it is weakness in the quality of
spatial resolution. However, remote sensing
provides good spatial resolution. Therefore,
the combination of rain gauges and remote
sensing should be used to estimate rainfall.
The purpose of this paper is so as to improve
rainfall estimation by using both rain gauge
and remote sensing observation. Since this
paper is literature review of rainfall
estimation, the results from this paper are
guideline to estimate rainfall. These results
have to be utilized in study area to modify
the results. Thereafter, the detail of these
results may be improved if they are not
suitable for rainfall estimation in Thailand.
2. Methodology
The methodology of this paper is review the
measurement and evaluation of rainfall,
which are well known, using both rain gauge
and remote sensing observation. Thereafter,
they are analyzed to create methods that are
related to existent rain gauge and
meteorological satellites. The detail of review
is explained as following.
2.1 The Nature of Rainfall Zone
There are three climatic zones on the world:
polar, equatorial, and the mid-latitude zone.
First, the polar zone locates between 600N to
900N and 600S to 900S. Second, the
equatorial zone is from 300N to 300S. The
tropical zone ranges between 23.50N to
23.50S of the equator. Last, the mid-latitude
zone or temperature zone locates between
300N to 600N and 300S to 600S. [5], [12],
[14], [19]
Patterns of rainfall change with season,
latitude, and the movement of the sun. In
temperate zones, there is the annual rainfall
about 100 inches or 2,500 millimeters on
western facing coastlines, and decreases
within the interiors of major landmasses. The
tropical zone, which has strong heating from
the sun, has the highest rainfall. Since twothirds of global rainfall occurs in the tropical
zone, this zone has great importance for the
global hydrological cycle. The process of
rainfall, which releases the latent heat of
condensation, transfers heat energy to the
atmosphere.
Three-fourths
of
the
atmosphere’s heat energy comes from the
tropical zone. The total of average annual
rainfall is usually more than 100 inches or
2,500 millimeters, and can increase to 400
inches or 10,000 millimeters if it is
influenced by the monsoons.
Since Thailand locates on the tropical
zone, annual average rainfall is more than
1,000 mm. In the rainy season, heavy
rainfalls often occur. The duration and
volume of heavy rainfalls are often unknown.
Then, flood damage can be high in some
areas, because flood forecasting has not been
implemented for the whole area of Thailand.
For this reason, rain gauges should cover all
of Thailand and should have high efficiency
for rainfall estimation. Remote sensing
technology can play a key role in improving
rainfall estimates.
2.2 Rainfall Estimation Using Rain Gauge
Rain gauge is applied for the estimation of
average daily rainfall. Ideally, an adequate
density and distribution of rain gauges have
to be in any study area, but it is rare in many
part of the world. Spatial interpolation
techniques are utilized for spatial averaged
rainfall
over
a
well-gauged
area.
Interpolation techniques can allow the
estimation of rainfall in an un-gauged area,
which is surrounded by sufficiently wellsited rainfall stations, but, due to insufficient
density and distribution of rain gauges, the
estimation of average rainfall is often in
error. [3]
As far as possible, rain gauges are sited in
representative locations although it is
common to find significant variation even
over a separation distance of a few
kilometers [2]. There are three main types of
rain gauges: non-recording gauges, weighing
gauges, and tipping bucket gauges. First,
non-recording gauges are obtained by a small
graduated wedge or cylinder that is placed at
some height above the ground. These gauges
have to be read manually once or twice per
day. Second, a weighing gauge includes a
collection bucket, and the depth of water is
estimated from the weight in the bucket.
Therefore, rainfall is calculated from the
change of water depth. Last, a tipping bucket
gauge consists of a funnel and a tipping
bucket that is calibrated so that a number of
tips correspond to 1 mm of rainfall (typically
4 – 10). This gauge estimates rainfall by
using the number of tips in a given period of
time and is usually equipped with a built in
data logger [7]
2.3 Rainfall Estimation Using Remote
Sensing
Remote sensing is new technology for
rainfall estimation, because rainfall is
estimated by rain gauge from past to present.
The number of technologies has been created
to estimate rainfall, and most of these
technologies are based on the detection of
rain clouds and determine rainfall using a
variety of more or less refined algorithm.
At present, wavelengths of microwave
and infrared are often applied for rainfall
estimation. In microwave channel, groundbased radar can measure rainfall information
at spatial and temporal scales suitable for
such applications. Ground-based radar has
become an important tool for meteorologists
over the past 20 years. Radar can
continuously scan an area about 200 km in
radius day and night and in all weather
conditions. The major limitation is that its
field of view can be blocked by high terrain.
There are three parts to work: (1) an antenna
or receiver, (2) computers that process the
raw radar data, and (3) an interactive
workstation the forecasters can use to display
the processed radar data. Ground-based radar
detects information at different scanning
elevations and patterns, in addition to varying
pulse frequencies. Radar sends out a pulse,
and listens for a return signal. The return
observed in a radar sounding will be
computed from the shape of the radar
antenna, the wavelength, frequency and
energy transmitted. Radar based reflectivity
images are normally presented by color in
dbZ, which is a logarithmic scale of the
target’s reflectivity. The reflectivity, which is
received by radar, can present precipitation
type, storm physics, and a good sense of the
intensity of storm patterns. The properties of
Z-R relationship (where radar-measured
reflectivity is Z and rain rate is R) relates to
the reflectivity of radar, so this relationship is
also suitable for analysis rainfall using
ground-based radar [6], [9], [10].
To estimate rainfall, near infrared and
thermal infrared are utilized in infrared
channel. Rain drops cannot be sensed directly
using near infrared wavebands. This
wavelength is completely filtered by cloud,
but cannot detect aerosols easily. However,
the thermal infrared bands provide very good
detection of cloud and aerosols (and are
therefore used in cloud filtering and
correction algorithms for NIR bands). The
thermal band will of course give good
estimates of the temperature at the top of a
cloud. Thus, one needs to relate the
independent measurements of rainfall to the
properties of a cloud measured by infrared
remote sensing. Cloud temperatures are used
for rainfall calculation. For the infrared
channel, cloud and the earth’s surface reflect
in this channel, and the intensity of radiance
depends on the temperature of the radiation
source. The higher the temperature, the more
intense the infrared radiation. High clouds
are invariably very cold, while low clouds are
much warmer [4]. Therefore, the intensity of
the infrared radiation in this channel can infer
both the temperature and the height of the
cloud top.
The characteristic of cloud can be classified
by shape and cloud height. Stratus and
cumulus are the types of cloud which are
grouped by shape of cloud. The characteristic
of a stratus cloud is being layered or sheetlike, while a cumulus cloud is puffy and
heaped. Cloud height can be classified in
three levels - high, middle, and low [4], [15].
The temperature of the atmosphere above the
Earth decreases with altitude, at a rate of - 7
degrees Celsius per kilometer. A cloud’s
altitude is given as “ceiling”, which is the
measurement at the top of cloud and is part
of the set of upper air measurements [18].
Table 1 Relationship between cloud height
and cloud temperature.
Table 1 Relationship between cloud height
and cloud temperature
Cloud Height
(meters)
0
1,250
2,500
3,750
5,000
6,250
7,500
8,750
10,000
11,250
Temperature (0
Celsius)
15
8
-1
-6
-13
-24
-34
-41
-50
-57
Satellite image is represented by various
shapes of gray after satellite measures the
temperature of clouds. Whiter areas are the
coldest part, while darker areas are the
warmest part. The apparent temperature of
clouds depends on many factors that include
cloud height, its size and make-up of the
particles that form the cloud [8].
2.4 Rainfall Measurement in Thailand
Presently, rainfall is measured by rain gauge,
while remote sensing technology is used to
detect meteorological information for
weather forecasting. However, researchers
are attempting to create suitable method for
rainfall estimation using remote sensing
observation.
The equipment used at a climate station
includes: Thermometer screen, MaximumMinimum Thermometer, Dry-Wet Bulb
Psychrometer,
Thermo-Hygrograph,
American Class A Evaporation Pan, and Rain
gauge. Many rainfall stations have some of
these instruments as well.
Daily rainfall is measured by the Thailand
Meteorological Department (TMD), Royal
Irrigation Department (RID), and Royal
Forest Department (RFD) [1]. RID and RFD
normally use rain gauges, while TMD uses
rain gauges to measure rainfall and uses
ground-based radar to collect intensity of
rainfall and meteorological information. For
ground based radar, X-Band, C-Band, and SBand are used to measure the intensity of
rainfall. The frequencies of these bands are
6,200-10,900 MHz, 3,900-6,200 MHz, and
1,550-3,900
MHz,
respectively.
The
wavelengths of these bands are 2.75-4.84 cm,
4.84-7.69
cm,
and
7.69-19.3
cm,
respectively. TMD’s ground-based radar
stations are located in Chiang Rai, Mea Hong
Son, Chiang Mai, Phitsanulok, Sakon
Nakhon, Khon Khaen, Ubon Ratchathani,
Surin, Don Muang, Rayong, Hua Hin, Sum
Porn, Surat Thani, Phuket, Ranong, Trung,
Song Kha, Nara Thiwas, and Thailand
Meteorological Department. There is also a
mobile radar unit. These stations nearly cover
all of Thailand. Ground-based radar is
measured every hour during regular weather
conditions and every 30 minutes in unusual
weather conditions. Also, it is used to
determine cloud movement, and the location
of rainfall [16].
Meteorological satellites, which are used by
TMD, include GMS-5. This satellite is used
for meteorology information by remote
sensing and direct broadcast, such as cloud
top
temperature,
cloud
movement,
temperature, sea surface temperature and
Earth surface temperature, radiation from
sun, moisture, ozone, and transfer
information to member or user. The bands of
GMS 5 include water vapour band (6.5 – 7.0
micron) and infrared band (10.5 – 11.5 and
11.5 – 12.5 micron) [16].
3. Result
According to the review of rainfall estimation
using rain gauge and remote sensing in
Thailand, the study results are that rain gauge
and remote sensing technology, which is in
the wavelengths of infrared and radar, should
be combined to estimate rainfall. Rain gauge
measures rainfall in the point of rain gauge
station, and rainfall between rain gauge
stations are interpolated by using spatial
interpolation technology that errors often
occur. Normally, rain gauges are located in
everywhere except in mountain and forest,
where are primary water resource. While
remote sensing technology help estimate
rainfall overall area, there are less errors than
spatial interpolation technology.
Infrared thermal band is used for the
estimation of rainfall by a non-linear
equation ( P  aT b ), where T is cloud top
temperature (K) from GMS satellite image
and P is rainfall (mm) from rain gauge. In the
target area, cloud top temperature and rainfall
are collected, and they are plotted to find a
and b coefficients. Thereafter, the equation
between P and T is created for each
characteristic area. This equation is used to
predict rainfall in that area when cloud top
temperature is determined from GMS
satellite imagery. The confidence of this
equation can be defined from the coefficient
of determination, or r 2 . The selected a and b
parameter are importance for the accuracy of
created equation. Addition to the a and b
parameter, P and T are also significance,
because all of these parameters have
influence to the coefficient of determination.
Thereafter, this equation should be calibrated
by using rain gauge. When temperature from
GMS satellite is applied with P-T equation,
rainfall from this equation is calculated.
Then, rainfall from rain gauge and this
equation are compared. If there are many
different values, the a and b coefficients have
to be adjusted.
There are two researches about above
equation in Thailand. First, the cloud top
temperature from GMS 4 and rainfall from
ground-based rainfall stations (20 stations)
are used to calculate a non-linear regression
equation of relationship between cloud top
temperature (T) and rainfall (P) in Northern
Thailand.
The
equations
are
58 24.80
2
P  4.3932 10 T
( r = 0.97) in the
dry season (March – May 1993), and
P  1.8462 10 22 T 8.93 ( r 2 = 0.62) in the
rainy season (August – September 1994)
[17]. Secondary, the equations for Northern,
Northeastern, Eastern, and Southern Thailand
from January to December 1994 are:
P  1.1102 1013 T 4.8288 ,
P  4.4905 1010 T 3.8012 ,
P  5.2384 1012 T 4.6683 ,
and
11 4.0840
P  2.4916 10 T
, respectively, with
coefficients of determination ( r 2 ) being
0.71, 0.73, 0.70, and 0.71, respectively [11].
On the other hand, radar band or groundbased radar can be used to analyze rainfall by
using Z-R relationship, which represents
rainfall intensity. The equation between Z
and R is created for each ground-based radar
station, and this equation is representation for
that station. The duration of rainfall is
measured to apply volume of rainfall.
Therefore, this relationship can be used for
rainfall estimation when the duration of
rainfall is not unknown.
After these two equations are created for
the target area, rainfall can be estimated from
P-T relationship and/or Z-R relationship
depending on the suitable information in
requirement time of rainfall estimation. If
it’s raining when there is not GMS satellite
information, Z-R relationship has to use to
estimate rainfall. On the other hand, it’s
raining when GMS satellite passes the target
area, so P-T relationship and/or Z-R
relationship can be utilized.
Finally, maps of rainfall and rainfall
intensity are created, and more accurate
rainfall is profit for many relative fields.
Also, rainfall estimation will be developed to
produce more efficiency.
4. Conclusion
The characteristic of rainfall includes spatial
and temporal variability. At present, rainfall
is estimated by using rain gauge and remote
sensing observation. Rain gauge supports
temporal resolution, on the other hand it is
weakness in the quality of spatial resolution.
However, remote sensing provides good
spatial resolution. According to the
advantages of these measurement methods,
more precious rainfall data should be
estimated from the combination of existent
rain gauge and remote sensing observation.
In Thailand, there are more than 600
rainfall stations operated by TMD, RID, and
RFD. Now, meteorological satellites and
ground-based radar are used together with
rain gauges to make more comprehensive
rainfall measurements. TMD uses rain
gauges to measure rainfall and uses
meteorological satellite and ground-based
radar to collect meteorological information.
GMS satellites is used by TMD and collect
cloud top temperature, cloud movement,
temperature, sea surface temperature, Earth
surface temperature, radiation from sun,
moisture, and ozone. At the moment this
satellite is not used to directly estimate
rainfall in Thailand. At the same time, the
ground-based radar collects the meteorology
information for air transportation and
weather forecasting. Since the ground-based
radar can present rainfall intensity, cloud
movement, and location of rainfall, it is
important that ground-based radar relates to
rainfall measurement.
Infrared thermal and radar band have
capability to estimate rainfall, while rain
gauge should be utilized for calibration.
Infrared thermal band is used for the
estimation of rainfall by the relationship of
cloud top temperature from satellite image
and rainfall from rain gauge. Radar band is
served to calculate rainfall from the
relationship
between
radar-measure
reflectivity and rain rate. Since there are
more 600 rain gauge stations in Thailand,
data from these stations are enough for the
calibration of estimated rainfall. Therefore,
the accurate equation is produced to estimate
rainfall.
Since the relationship is usually a nonlinear equation, coefficients of the equation
are estimated using accurate ground data.
The estimated rainfall can be calibrated by
rain gauge. Rain gauges are normally used to
calibrate rainfall (mm). Finally, the results of
rainfall estimation using ground-based data
and remote sensing observations provide an
accurate rainfall map.
Acknowledge
We thank The Royal Golden Jubilee Ph.D.
Program (RGJ) for supporting budget and Dr.
Hugh Turral from IWMI (International Water
Management
Institute)
for
useful
suggestions.
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