Journal of Natural Disaster Science, Volume 24, Number 2, 2002, pp83-89

Application of a Method to Estimate Rainfall in Bangladesh Using GMS-5 Data
Md. Nazrul ISLAM1), A. K. M. Saiful ISLAM2), Taiichi HAYASHI3), Toru TERAO4) and Hiroshi UYEDA5)
Department of Physics, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh
2)Institute of Water and Flood Management, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh
3)
Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan
4)
Faculty of Informatics, Osaka Gakuin University, Osaka, Japan
5)
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan
1)(Received for 9. Dec., 2002)

ABSTRACTJapanese Geostationary Meteorological Satellite 5 (GMS-5) data were used to estimate rainfall in Bangladesh
over a period of 61 days from June 1, 1996. The 3-hourly digital data over 33 ground-truth stations throughout
Bangladesh obtained from GMS-5 were analyzed. Satellite rainfall was estimated by the Convective Stratiform
Technique (CST) algorithm. The amount of rainfall calculated by the CST was compared with that calculated
from the ground-truth data. That calculated by the CST was 1.1 times larger than that of the ground-truth rainfall.
The average daily rainfalls were 15 mm as calculated from satellite and 14.3 mm from the ground-truth data.
The correlation between the satellite and the ground-truth rainfall was significant at coastal stations but not atinland ones. Satellite and ground-truth data were used to derive a linear regression equation for estimating theamount of surface rain from satellite data. Data from stations throughout the country that showed good correlations gave a slope term of 0.84 and intercept constant of – 0.1 in the regression equation.

1. INTRODUCTION
Remote sensing data are now widely used for different purposes, and the use of satellite data for rainfall prediction has
increased markedly. Many attempts to estimate rainfall from satellite data have been made (Arkin and Meissner, 1987; Adler and
Negri, 1988; Arkin, 1989; Carn et al., 1989; Goldenberg et al.,
1990; Kikuchi and Uyeda, 1996; Jobard et al., 1996; Islam et al.,1998), but there is still no standard method for making such estimates. The estimation of rainfall for large coverage is essential,
especially for a country like Bangladesh, which is affected byflooding almost every year. Floods are one type of natural disasterand can not be entirely avoided, but damage and losses may bedecreased if we can predict flooding. For the proper prediction offloodwater in Bangladesh the estimated precipitation for large coverage is required because of the huge expanse of the GangesBrahmaputra-Meghna (GBM) basin in the north and northwest of
Bangladesh. Rainfall in the GBM basin is the main source offloodwater in Bangladesh. Although very important, few detailed
studies of estimated rainfall in Bangladesh have been based onsatellite data. There has been an attempt to estimate the rainfall in
Bangladesh from satellite data in a joint research project carriedout by Japan International Corporation Agency (JICA) and
Bangladesh University of Engineering & Technology (BUET).
As a result, a Geostationary Meteorological Satellite 5 (GMS-5)
receiving station was installed at the Institute of Water and FloodManagement (IWFM), BUET in 1996. Using GMS-5 data, Wahid
and Islam (1999) calculated the amount of rainfall by the Gross
Precipitation Index (GPI) technique. They calibrated the GPI

KEY WORDS: Rainfall; Floodwater, Convective; Stratiform; Satellite.

value with the raingauge rainfall recorded for Sylhet district ofBangladesh and suggested further study. Because much of the precipitation in both the mid-latitudes and tropics emanates frommeso-scale convective systems (Houze and Betts, 1981), it is necessary to distinguish between the convective and stratiform components of these cloud systems as the physics and dynamics of air
motion and precipitation growth in the convective and stratiform
regions differ fundamentally (Goldenberg et al., 1990). The GPI
method can not differentiate the convective and stratiform regionsof clouds, therefore it gives the average instead of the componential actual rainfall.
The amount of the componential rainfall from the convective
and stratiform cloud components also must be determined (Liu et al.,
1995; Islam et al., 1999). The Convective and Stratiform Technique(CST) was used by Adler and Negri (1988) in the Florida Area
Cumulus Experiment (FACE); by Goldenberg et al. (1990) in
Winter Monsoon Experiment (WMONEX); as well as by Kikuchi
and Uyeda (1996) and Islam et al. (1998) in Tropical Ocean Global
Atmosphere (TOGA) – Coupled Ocean Atmosphere Response
Experiment (COARE) convections. To apply the CST algorithm toBangladesh, certain modifications are needed in calculating theslope parameter, determining the stratiform threshold temperature,
and assigning convective and stratiform rain rates. The purpose of
our study was to apply the CST to Bangladesh in order to calculate
its rainfall from GMS-5 data. We also have quantified the amountof surface rain using both ground-truth and CST-calculated rainfall
data. Once the real-time rainfall for a large coverage is calculated a
prediction model can be developed that estimates the lead-time rainfall. This is very essential for a flood-affected country.

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M. N. ISLAM, A. K. M. S. ISLAM, T. HAYASHI, T. TERAO AND H. UYEDA

2.

DATA USED

TGround-truth and satellite data for a 61 days period starting
June 1, 1996 were used. The 3-hourly rainfall data collected fromthe 33 raingauges of the Bangladesh Meteorological Department
were as the ground-truth data used. These manual type raingauges
are located in different parts of the country (Fig. 1). Six types ofimages are sent by the GMS-5. Eight-bit data of the GMS-IR werecollected at IWFM, BUET, Bangladesh. These infrared images
have 800 pixels in the horizontal direction and 800 lines in the vertical one. A partial image, extending from 88°to 93°E and from20°to 27°N and covering Bangladesh was constructed from the
GMS image. As the satellite is located at 140°E on the equator,
the images received by it are deformed because they differ fromthe actual location. A geometrical correction was used to eliminate
this problem (Chowdhury et al., 1997).
3. ANALYSIS METHODS
Raingauge rainfall is compared with that calculated fromsatellite data. Satellite rainfall was calculated for a 50 km meshgrid box centered on the position of a raingauge. Calculation procedure of rainfall from satellite data is discussed below.
3.1. Convective stratiform technique (CST)The technique proposed by Adler and Negri (1988) uses satellite IR data to distinguish between convective and stratiform components of a meso-scale convective cloud system. They tested thealgorithm called the Convective Stratiform Technique (CST) on
data from the Geosynchronious Operational Environmental
Satellite (GOES) IR channel obtained over four days during thesecond Florida Area Cumulus Experiment (FACE). The CST
algorithm was modified by Goldenberg et al. (1990) in order toanalyze the horizontal precipitation structure of a cloud cluster
observed off the northern coast of Borneo during the WinterMonsoon Experiment (WMONEX). They used the IR TBB of the
GMS-1 to analyze the entire lifetime of the cluster. These two successful analyses made with the CST encouraged its use in the
TOGA-COARE domain. It also should be noted that Kikuchi and
Uyeda (1996) analyzed three cases by the modified CST(Goldenberg et al., 1990) during the TOGA-COARE Intensive
Observation Period (IOP). They tested the CST algorithm on datafrom the GMS-4 and compared the results with those of Doppler
radar data collected on Manus Island. There was a discrepancy
between the rainfall amounts as determined by the CST and radar,
but the convective and stratiform rainfall percentages determinedby the CST for case 2 of Kikuchi and Uyeda (1996) were goodenough to compare with other research results. Islam et al. (1998)
applied the CST to the TOGA-COARE domain and analyzed thehorizontal precipitation structure (area and rainfall amounts) of
cloud clusters based on GMS-4 IR data for the Intensive Flux
Array (IFA) region over the western Pacific warm pool. Theystrongly recommended adapting the CST for a particular region.
Islam et al. (1999) modified the CST algorithm for the TOGACOARE domain and used GMS-4 data to estimate the convective
and stratiform rainfall from satellite data. The steps of the CST
algorithm are as follows:a) Identification of convective cores and their locations on
GMS-IR imagery
To identify the locations of convective cores in each satellite
image, the IR TBB field is examined for relative minima (Tmin). It
may be a single pixel minima or the centroid of a multi-pixel minima in a square 50 km×50 km box. After identifying the pixel
location of Tmin, its strength is measured by calculating the slope
parameter (S). In this way, all points colder than their respective
environments are regions of enhanced convection. The strength of
the slope parameter determines whether the candidate Tmin pixel isconvective. The slope parameter adapted for the TOGA-COARE
region (Islam et al., 1999) is
S= k ( Ti-1, j + Ti+1, j + Ti, j-1 + Ti, j+1 – 4 Ti, j )(1)

Where S is the slope parameter, and i and j refer to the position of
the pixel for which S is being calculated. Factor k depends on data
resolution. Here k = 0.25 for the four surrounding pixels of GMS4 (11.1 km x 11.1 km) data resolution.
The calculated pairs of Tmin and S are compared by use of anempirical discrimination line that separates Tmin values associatedwith convective cores. This condition, used to distinguish convective cores from those identified with thin cirrus by Goldenberg etal. (1990), is given byS≧exp [ 0.0826 ( Tmin –207 ) ]Fig. 1

Raingauge stations in Bangladesh.

In our analysis, equation (2) was used on the GMS-5 IR data.

(2)

APPLICATION OF A METHOD TO ESTIMATE RAINFALL IN BANGLADESH USING GMS-5 DATA

According to Adler and Negri (1988), the convective precipitation area (Ac ) is directly proportional to cell top height, as indicated by the cloud top temperature. We used the Adler and Negri
relationship (1988) given byln (Ac ) = a Tci + b

4. MODIFICATIONS
Some simple and essential modifications were made to the
CST algorithm in order to apply it to Bangladesh and to be able touse GMS-5 data to estimate rainfall for a large coverage.

(3)

where a and b are constant, and index i refers to the i-th core. The
Tmin of Adler and Negri (1988) is replaced by the variable Tci.
Because the a and b values calculated by Adler and Negri (1988)
for FACE and the those of Goldenberg et al. (1990) for WMONEX
are nearly the same, the values of a = -0.0492 and b = 15.27 wereused in our analysis.
b) Estimation of stratiform regions
After assignment of areas to selected convective cells, stratiform precipitation areas are identified. The stratiform thresholds
temperature, Ts , is given by
Ts (K) =Tmod + X

85

(4)

where Tmod is the modal cloud-top temperature, and χ is a variable.
According to Goldenberg et al. (1990)(5)
where n is the number of pixels in the modal grid, and TBB, j is the
IR temperature of the j-th pixel in that grid. The variable X wasintroduced by Goldenberg et al. (1990), in their case X=7 K. The
necessity of introducing this variable depends on whether radarreflectivity is compared with the CST analysis results. Adler and
Negri (1988) excluded all echoes < 25 dBZ on Miami radar,whereas Goldenberg et al. (1990) included all echoes 1dBZ on the
MIT land-based radar. The need for the addition of X=7 K was
found by Goldenberg et al. (1990) for their case as opposed tousing the X=0 K of Adler and Negri (1988). Islam et al. (1999)
excluded all echoes < 20 dBZ on Keifu Maru radar (weather radarof the Japan Meteorological Agency). In their study X=4 K was agood fit for short-term (<1 day) analysis and X=7 K a good fit for
long-term (10 days) analysis. In this study of Bangladesh, we haveno radar data and the X value is assigned based on raingauge data
as described in subsection 4.2.

4.1. Slope parameterIn our study six surrounding pixels rather than four (Adler and
Negri, 1988; Islam et al., 1999) were involved in calculating theslope parameter. The variation in pixel number depends on resolution of the satellite data used. Here the slope parameter is given by
S= k ( Ti-2, j + Ti-1, j + Ti+2, j +Ti+1, j +Ti, j-1 +Ti, j+1 -6Ti, j )(6)

and k=0.17 for the six surrounding pixels in the GMS-5 (8.5 km×16.5 km) data resolution.
4.2. Best x value fit
As there was no radar data available for assigning the X value
in our study, we used raingauge data. The X value proposed is forraingauges located in a different part of Bangladesh (Fig. 1). To
assign the x value, the number of stratiform cloud pixels for eachvalue of X from 0 to 15 K was calculated. The correlation coefficient for the raingauge rainfall and the number of stratiform cloud
pixels for each X value then was calculated at each station. Fig. 2
shows the X value at stations Feni and Comilla. To set the X value
for a particular raingauge the following criterion was used:“Choose the X value corresponding to the maximum point on the
curve nearest X=7 K found by the modified CST algorithm (Islamet al., 1999)”. Application of the above criterion gives an X valuec) Assignment of convective and stratiform rain rates
Adler and Negri (1988) used an arbitrary startiform rain ratein their CST analysis. They calculated the convective rain rateusing the CST itself and a one-dimensional model (Adler andMack, 1984). Goldenberg et al. (1990) followed Adler and Negri(1988) in calculating the convective rain rate and assumed thestratiform rain rate from radar data. Islam et al. (1997), however,showed there is a difference between the rain rates determined byradar and by the CST and clearly indicated the need to assign theconvective rain rate rather than taking it from the CST algorithm.
They used a convective rain rate of 25 mmh-1 and a stratiform rain
rate of 3.5 mmh-1 for the tropical region. In our study both the convective and stratiform rain rates are assigned from the ground-truth
rainfall and satellite data analysis, as discussed in subsection 4.3.
Fig. 2x Values at stations Feni（a）and Comilla（b）.

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M. N. ISLAM, A. K. M. S. ISLAM, T. HAYASHI, T. TERAO AND H. UYEDA

of 6 K for both Feni and Comilla (Fig. 2). The X value ranged
from 5 to 7 K for all the raingauge stations (Table 1). The average
X value was 6 K for the entire country.
4.3. Assignment of the rain rate
The stratiform rain rate can not be evaluated by the CST algorithm. Adler and Negri (1988) made an arbitrary assignment of 2
mmh-1 based on the rate being one-tenth of the mean convective
rain rate (20 mmh-1). Goldenberg et al. (1990) also used 2 mmh-1
as the average, the lower value being 1 mmh-1 and the upper one 3
mmh-1. Islam et al. (1999) used 3.5 mmh-1 as the stratiform rain
rate for the tropical region, which value is consistent with
Rappaport’s (1982) 3.3 mmh-1 and comparable to Gamache andHouze’s (1983) 2.6 mmh-1.
To assign the convective rain rate, 10 to 30 mmh -1 wasassumed in our study, and for each case the average percent deviation of the CST rainfall was calculated from the raingauge rainfall.

Table 1.

x Values at the raingauge stations throughout Bangladesh.

A minimum deviation of 4.5% was found for the convective rain
rate of 20 mmh-1 (Fig. 3). In our study, the stratiform rain rate
was checked from 2 to 5 mmh-1 and 3.5 mmh-1 assigned to for all
raingauges.
5. CALCULATION OF COMPONENTIAL RAINFALL
5.1. Convective rainfall
For the regional grid data the daily average convective rainfall
is given by
Convective rainfall (mm) = C (Ac / A) T Rc

(7)

where C = number of convective cells within a grid; Ac = convective rain area from equation (3); A = average area covered by eachpixel in km2 (8.5 km × 16.5 km); T = length of the average period,in hours; and Rc = convective rain rate in mmh-1 (20 mmh-1).
5.2. Stratiform rainfall
For a regional grid data the daily average stratiform rainfall isgiven by
Stratiform rainfall (mm) = S (As/A) T Rs

(8)where S = number of stratiform cells within a grid; As = stratiform
rain area in that grid; A = area covered by the grid; T = length of
the average period in hours; and Rs = stratiform rain rate in mmh-1(3.5 mmh-1).
6. RESULTS AND DISCUSSION
The rainfall value obtained in the above analysis was compared with the ground-truth rainfall. Fig. 4 shows the comparison
between the daily average rainfall obtained by the CST and that forraingauge data. CST values are shown for both the convective andstratiform components. At all 33 stations the respective convective
and stratiform rainfall amounts were 432.31 and 62.63 mm for 61
days starting June 1, 1996. The convective component was 87%
and the stratiform one 13%. These percentages differ from thoseof Islam et al. (1998) whose calculated values were convective
64% and stratiform 36%. For all the stations the CST calculation

Fig. 3

Determination of rain rates.

APPLICATION OF A METHOD TO ESTIMATE RAINFALL IN BANGLADESH USING GMS-5 DATA

was 494.95 mm and the raingauge value 470.57 mm for the 61
days. The average difference in daily rainfall between the CST
and raingauge amounts was 0.7 mm; i. e., as expected the CST
value was 0.7 mm/day larger than the ground-truth value. Groundtruth values were larger than CST values in all the coastal regions
except Sylhet, Feni, and Rangpur. Otherwise, the CST values
were larger than the ground-truth ones. On the average, the calculated CST was 1.051 times the ground-truth value, indicative ofgood agreement between the two values.
Fig.5 shows the correlation between the ground-truth rainfall
and that calculated by the CST at the different stations. The correlation coefficients (r) are between 0.068 (Rajshahi) and 0.756(Khepupara). They are significantly high for the stations Barisal,
Bhola, Cox’s Bazar, Rangamati, Comilla, Chittagong, Khepupara,
Kutubdia, Mongla, Patuakhali, Syedpur and Teknaf, but insignificantly for the rest stations. The average it is 0.32, indicative thateven the average correlation is low and that the amount of rainfall
calculated by the CST is very close to that found from ground-truth
data (Fig. 4). This also shows that the CST is reliable for estimating regional rainfall from GMS-5 data.
Fig.6 shows the relationship between the CST (CST) and

Fig. 4ground-truth (RAIN) rainfalls. For all 33 stations the linear regression equation takes the form
RAIN = (0.422) CST + 8.016(9)Whereas omitting the very poorly correlated stations, equation (9)becomes
RAIN = (0.840) CST + 0.104(10)

If the coefficient of CST is given by m and the constant term by c,equation (10) becomes
Surface rainfall = m (Satellite rainfall) + c(11)This means that the surface rain value was 0.84 times larger
than the satellite rain value plus the constant value of 0.1 used inour study. With equation (11) the surface rain can be estimatedfrom satellite data. Linear regression equations were obtained forindividual stations, and the m and c values are shown in Fig. 7.
Note that the amount obtained with equation (11) may not be theRainfall amounts calculated form the CST and raingauge data：convective, stratiform and raingauge rainfalls.

Fig. 587

Correlation between rainfall amounts calculated from the CST and raingauge data for all the stations.

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M. N. ISLAM, A. K. M. S. ISLAM, T. HAYASHI, T. TERAO AND H. UYEDA

Fig. 6

Relationships between rainfall amounts calculated from the CST and raingauge data：for all stations（a）
and omitting poorly correlated stations（b）. The solid line is the regression line.

Fig. 7m and c values in equation（11）throughout Bangladesh：m value（a）and c value（b）.

actual rainfall amount for all the seasons, but this procedure isapplicable to long time data and provides a reliable technique for
estimating rainfall from satellite data. It therefore should be usefulfor estimating floodwater amounts in the large GBM basin, whichis essential for flood prediction in Bangladesh.
7. CONCLUSIONS
Analysis of 61 days of 3-hourly GMS-5 data of over 33 raingauge stations in Bangladesh provided the following conclusions:● The CST method can be used when radar data is not available.
● Rain rates can be assigned from ground-truth data
● Correlation between the satellite and ground-truth rainfall
values is significant for coastal but not inland stations.
● The convective component is 87% and the stratiform com-ponent 13%.
● Surface rainfall is 1.1 times larger than the ground-truth
rainfall.
● Surface rain can be estimated from satellite data.
● The CST algorithm can be successfully used to estimate
rainfall in Bangladesh from GMS-5 data.
These encouraging results indicate that calculation of the surface rain for a large coverage from satellite data is possible.
Knowing the rainfall over a large coverage is essential for flood
forecasting and for determining the surface water budget. To use
this procedure in determining surface rain from satellite data further study of long time datasets is required. This is now inprogress.

APPLICATION OF A METHOD TO ESTIMATE RAINFALL IN BANGLADESH USING GMS-5 DATAACKNOWLEDGMENTS
We thank Dr. Md. Rezaur Rahman of the Institute for Water
and Flood Management, Bangladesh University of Engineering &Technology, the Japan International Cooperation Agency, and the
Bangladesh Meteorological Department for providing the necessary data.
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