METEOROLOGICAL APPLICATIONS
Meteorol. Appl. (2012)
Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/met.1327

Development of daily gridded rainfall dataset over the Ganga, Brahmaputra
and Meghna river basins
Venkatraman Prasanna,a * Juvy Subere,b Dwijendra K. Das,c Srinivasan Govindarajanc and Tetsuzo Yasunarid
a APEC Climate Center (APCC), Busan, South Korea
b School of Bio-agricultural Sciences, Nagoya University, Japanc Regional Integrated Multi-Hazard Early Warning System (RIMES), Bangkok, Thailandd Hydrospheric Atmospheric Research Center, Nagoya University, Japan

ABSTRACT: The India Meteorological Department (IMD) gridded rainfall dataset, the 47 Bangladesh gauge rainfall
observations and the Tropical Rainfall Measuring Mission (TRMM) 3B42V6 satellite data are used in the present analysis.
The nearest neighbour interpolation scheme is used, wherein the interpolated values are computed from a weighted sumof observations. The Bangladesh daily gauge measured rainfall is interpolated into regular grids of 0.5° × 0.5° resolution
every day from January 1988 to December 2007 and appended with the daily gridded dataset of the IMD over the Indian
region. A similar resolution dataset of 0.5° × 0.5° for the TRMM-3B42V6 data from January 1998 to December 2007 iscreated from the original data of 0.25° × 0.25° resolution. To produce a merged rainfall product, all the gridded datasets are
merged. The merging of datasets is done in such a way as to include the highest rainfall at each grid point from the three
products. Based on the three available sets of daily observations (IMD dataset (1° × 1° ), TRMM-3B42 (0.25° × 0.25° ) and46 daily station observations over Bangladesh), a dataset of 0.5° × 0.5° resolution on a daily scale is generated. The focusof this study is to compare the TRMM-3B42V6 rainfall data over the Ganga, Brahmaputra and Meghna (GBM) domain
with observed point gauge data, and assess the possibility of using them for application in real time flood forecasting aswell as to serve as a comparison tool for the baseline simulation of high resolution atmospheric models aimed at flood
forecasting and climate change projections. Copyright  2012 Royal Meteorological Society
KEY WORDS

gauge rainfall dataset; Bangladesh floods; TRMM 3B42V6; gridded rainfall dataset

Received 26 December 2010; Revised 14 March 2012; Accepted 4 April 20121.

Introduction

Bangladesh is among the most natural disaster prone (floodprone) countries in the world. It has suffered 170 large-scale
floods between 1970 and 1998. The frequency of flooding
episodes is growing, with catastrophic ‘once in a generation’floods occurring more regularly (Chowdhury, 1988, 1989).
This includes eight major floods between 1974 and 2004,
many of which are considered by hydrologists to be at asize expected only once in every 20 years. During the 2007
monsoon, Bangladesh experienced severe floods with serious
consequences for the national economy and the livelihoods ofmillions of people. A major cause of flooding in Bangladesh
is due to the fact that it is a low lying country, with 70% of
its land area being less than 1 m above sea level and 80% of
it floodplain. Moreover, Bangladesh receives large amounts ofwater passing through it with two major rivers (Ganges andBrahmaputra) converging and forming a huge delta as they
enter the sea. Both rivers also have large drainage basins,
which increases flood risk (Brammer, 1990a, 1990b). The
occurrence of a monsoon climate and the annual torrential rainsin Bangladesh often results in the rivers exceeding their capacityand flooding. Moreover, melting snow from the Himalayas
further increases the flood risks as torrents of melt water enter∗ Correspondence to: V. Prasanna, APEC Climate Center, 1463 UDong, Haeundae-gu, Busan 612-020, South Korea.
E-mail: Prasa arnala@yahoo.com
Copyright  2012 Royal Meteorological Society

the rivers at their source in the spring (Pant and Kumar,1997).
Each year, short-lived flooding occurs throughout the summer and early autumn with sufficient irregularity to produce
adverse social and agricultural impacts. Even in the normal
years, about one-fifth of the country can be inundated for days.
The timing of the flooding is critical for the harvesting of thewinter rice crop, planting of the summer rice, and viability ofother crops planted during the monsoons. As an example, during the summer of 1998 over 60% of Bangladesh is inundated
for nearly 3 months and in July 2004, widespread flooding
occurred as the Brahmaputra exceeded dangerous flood levels
over a 2 week period (Ali, 2007). In July and September 2007
the Brahmaputra flooded severely, with the resulting inundation
lasting for weeks (Saiful et al., 2010).
There is a considerable requirement for rainfall related
information for timely action to avert the river flood calamities
in Bangladesh. Such timely information can impact decisionmaking, specifically when to evacuate the flood-prone regions,ensure protection of food and water supplies and the safetyof livestock. If pre-emptive actions are taken, then thesecan significantly lower the cost of rehabilitation following
a period of flooding. Providing rainfall information for the
upstream areas in India in advance would provide better
warnings of severe flooding in the low lying areas near theGanges and Brahmaputra catchments of the India-Bangladesh
border. The dataset would provide an opportunity to map
low-lying areas prone to flooding and inundation during the

V. Prasanna et al.

Figure 1. (a) Map of Bangladesh Water Development Board Monitored Rainfall Stations. (b) Selected land domains over India and Bangladesh
for comparison of rainfall representation by gauge and TRMM 3B42V6. Domains are abbreviated as D. (c) River systems over and around GBM
domain and river basin map of GBM domain. Thick lines are GBM basin boundary, dotted lines are political boundary and dashed lines arerivers; thick dashed lines are GBM river system. (d) Topography over the GBM domain.

monsoon season. The dataset also serves as an observationalreference to compare with climate model outputs aimed at flood
forecasting. Efforts are in hand to validate global operational
climate forecast model results over this domain. It is envisaged
in this study to create a useful dataset for flood related
studies.

Copyright  2012 Royal Meteorological Society

The paper is organized into five sections. Section 2 describes
the different datasets used for constructing the precipitation
products, and the methodologies adopted for gridding andmerging the precipitation products are also described. Section 3
discusses the results and provides details of some specificflooding cases over Bangladesh. Lastly, Section 4 concludes,

Meteorol. Appl. (2012)

Development of daily gridded rainfall dataset over GBM basins
Table 1. Data obtained from Flood Forecasting and Warning Center of Bangladesh Water Development Board Monitored Rainfall Stations.
Sl.no

Station nameLatitude (° )Longitude (° )No. of available observation
days (days)

Missing no. of observation
days (days)

Percentage of available
data (%)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46

Bandarban
Barguna
BarisalBhairab Bazar
Bogra
Chandpur
Chapai-Nawabganj
Chilmari
Chittagong
ComillaDalia
Dewanganj
Dhaka
DinajpurDurgapur
FaridpurGaibandha
Habiganj
JessoreKanairghat
KauniaKhulnaKurigram
Kushtia
Lama
Manu Rly Bridge
Moulvi Bazar
Mymensingh
Nakuagaon
NaogaonNarayanhat
Noakhali
Pabna
Panchagarh
Parshuram
Patuakhali
Rajshahi
RamgarhRangamati
Rangpur
Satkhira
SheolaSirajganj
Sunamganj
SylhetTangail

22.22
22.13
22.68
24.06
24.83
23.23
24.61
25.56
22.34
23.47
26.16
25.16
23.70
25.68
24.45
23.60
25.32
24.38
23.16
24.99
25.76
22.84
25.84
23.90
21.81
24.40
24.49
24.76
25.17
24.89
22.80
22.85
23.98
26.40
23.24
22.34
24.38
23.00
22.67
25.76
22.71
24.89
24.39
25.06
24.9
24.28

92.19
90.11
90.33
90.96
89.46
90.66
88.24
89.68
91.83
91.16
89.02
89.73
90.48
88.62
88.76
89.81
89.55
91.43
89.20
92.25
89.40
89.54
89.61
89.12
92.19
91.79
91.70
90.47
90.23
88.91
91.71
91.13
89.30
88.52
91.42
90.35
88.49
91.74
92.12
89.28
89.08
92.17
89.73
91.44
91.88
89.92

6662
6800
6655
7072
6854
6820
6548
5608
5967
7119
5579
6252
6665
6978
5613
7007
5154
6934
6886
5570
6856
6612
7056
7012
6949
5681
6942
7074
6496
6912
6966
6461
5182
6561
7004
5836
6615
6673
5789
5747
6792
6989
6986
6669
7110
6599

635
497
642
225
443
477
749
1689
1330
178
1718
1045
632
319
1684
290
2143
363
411
1727
441
685
241
285
348
1616
355
223
801
385
331
836
2115
736
293
1461
682
624
1508
1550
505
308
311
628
187
698

91.20
93.09
91.10
96.81
93.83
93.36
89.64
76.77
81.68
97.45
76.37
85.59
91.24
95.52
76.84
95.92
70.55
94.92
94.26
76.25
93.85
90.51
96.59
95.99
95.13
77.77
95.03
96.84
88.93
94.62
95.36
88.45
70.94
89.82
95.88
79.89
90.55
91.35
79.25
78.67
92.98
95.67
95.63
91.29
97.33
90.34

Period of available station data for the calendar dates, starting date: 1 January 1988; ending date: 31 December 2007.

noting the advantages of using precipitation products andhighlights some of their limitations.

for comparison with the merged product. A brief description ofeach of these datasets and their respective sources are described
below

2.

2.1.

Data and methods

The six different datasets used in the present study are given
below, of which three are primary datasets (Bangladesh rain
gauge dataset, India Meteorological Department (IMD) gridded dataset and Tropical Rainfall Measuring Mission (TRMM)
3B42V6 dataset) used for constructing the merged product,
whereas the secondary datasets (Global Precipitation and Climatology Project (GPCP) data, Global Precipitation and Climatology Centre (GPCC) data and Aphrodite data) are used
Copyright  2012 Royal Meteorological Society

Datasets

2.1.1. Bangladesh rain gauge data
Daily precipitation fields over Bangladesh are derived froma dense network of 46 (individual station observations) rain
gauges. Rain gauge data are obtained from the Flood Forecasting and Warning Center (FFWC) of Bangladesh Water
Development Board (BWDB) from 1988 to 2008. The spatial distribution of stations used for the merged dataset over the
Meteorol. Appl. (2012)

V. Prasanna et al.

Figure 2. (a) Monsoon seasonal mean rainfall and (b) standard deviation (June, July, August, September) from daily observations of IMD and
BWDB gridded data (IMD + BWDB) and (c) monsoon season mean rainfall and (d) standard deviation (June, July, August, September) fromdaily observations of TRMM.

study area is shown in Figure 1(a). A simple statistical analysis on the data distribution is shown in Table 1. The quality
controlled gauge observations are then gridded to a 0.5° × 0.5°
grid box template using an inverse distance weighted interpolation. The observations are carried out once a day at 0600 LST(LST = UTC + 6 h).

(1968) interpolation technique for gridding data from individual
stations, while GPCC uses the Willmott et al. (1985) method
for interpolation. The IMD product uses gauge data from 1803
stations to estimate the accumulated rainfall in 24 h ending
0830 IST (IST = UTC + 5.5 h).

2.1.2. IMD gridded rainfall dataset

2.1.3. Tropical Rainfall Measuring Mission (TRMM)
precipitation productThe daily gridded rainfall data with a resolution of 1° × 1° fromthe IMD (Rajeevan et al., 2006) are used in the study. Thereexist some differences in the preparation of different datasets
such as IMD, GPCC and TRMM merged analysis 3B42V6
daily rainfall products over India. The IMD uses the Shepard

The TRMM is a joint space mission between NASA and
the Japan Aerospace Exploration Agency (JAXA) designed
to monitor and study tropical rainfall. The TRMM is part ofNASA’s Mission to Planet Earth, a long-term, co-ordinatedresearch effort to study the Earth as a global system. The

Copyright  2012 Royal Meteorological Society

Meteorol. Appl. (2012)

Development of daily gridded rainfall dataset over GBM basins

Figure 3. (a) RMSE (1998–2007) and (b) BIAS (1998–2007) for entire period between daily observations of IMD and BWDB Gridded data
(IMD + BWDB) and TRMM. (c) RMSE (June, July, August, September) and (d) BIAS (June, July, August, September) for monsoon season
between daily observations of IMD and BWDB gridded data (IMD + BWDB) and TRMM.

algorithm used in the TRMM 3B42V6 precipitation productprovides a combination of TRMM’s merged passive microwave(HQ) and microwave calibrated IR (VAR). The current scheme
is a simple replacement of each grid box with the merged
passive microwave (HQ) value if available; otherwise thecalibrated IR (VAR) value is used. Thereafter, in each gridbox all of the multi-satellite (SSM/I, AMSR, AMSU) values
in the month are summed and combined with monthly gauge
data in order to produce the Version 6 TRMM Data product.
Finally, for each grid box, all the 3 h values are scaled toapproximately sum to the 3B43 monthly value. This system
Copyright  2012 Royal Meteorological Society

is developed to produce the TRMM and other data estimatesby applying new concepts in merging quasi-global precipitation
estimates and taking advantage of the increasing availabilityof fine-scale input datasets. The overall system is referred
to as Version-6 TRMM Multi-Satellite Precipitation Analysis(TMPA) (Huffman et al., 2007).
2.1.4. GPCP rainfall dataset
The GPCP precipitation product is a widely used product forclimate research studies (Huffman et al., 1997, 2001; Adler
et al., 2000, 2003). The GPCP Combined Product Version 2
Meteorol. Appl. (2012)

V. Prasanna et al.
(a)Rain (mm day−1)

12

8

9

2.2. Methodology for creating a merged precipitation product

6
6
4
3

2
0

0

J F MAM J J A S O N D(c)Rain (mm day−1)

J F MAM J J A S O N D
(d)

20

15
12

15

9
10
6
5
0

3
0

J F MAM J J A S O N D(e)Rain (mm day−1)

based on Spheremap (Willmott et al., 1985) for gridding thestation observations (Yatagai et al., 2009).

(b)

10

(f)

10

10

8

8

6

6

4

4

2

2

0

J F MAM J J A S O N D

0J F MAM J J A S O N D

J F MAM J J A S O N D

MonthRain Gauge

Month
TRMM

GPCP

Figure 4. Monthly annual cycle of rainfall from rain gauge (IMD+
BWDB), TRMM 3B42V6 and GPCP for selected land domains overIndian region. (a) Domain 1, (b) Domain 2, (c) Domain 3, (d) Domain
4, (e) Domain 5, (f) Domain 6.

is a near real-time product, including the monthly precipitation
data from global telecommunication system (GTS) stations, i.e.
the synoptic weather and climate stations. The basic difference
between the GPCC and the gauge data is that in the GPCC,
the IR and microwave data are used for rainfall estimation
along with the rain gauge calibration. Moreover, the GPCP and
the TRMM merged products use very similar procedures withdifferent initial inputs, thus simplifying the intercomparison.
2.1.5. GPCC precipitation product
The GPCC dataset is a global land-surface precipitation analysis
on monthly timescale based on in situ observation data (Becket al., 2005). It comprises near real-time weather and climate
data (Synop and CLIMAT) and non-real-time precipitation
observations provided by World Meteorological Organisation(WMO) member countries.
2.1.6. Aphrodite precipitation dataset
The Asian Precipitation Highly Resolved Observational Data
Integration Towards the Evaluation of Water Resources(APHRODITE; Kamiguchi et al., 2010) is a rain gauge-based
daily precipitation dataset, which is also used for comparisonin this study. APHRODITE uses a new weighted mean method
Copyright  2012 Royal Meteorological Society

There is a number of efforts to compare and validate different satellite rainfall products with other rainfall measurements
on global and regional scales. Many of these studies focusedon validation and intercomparison of TRMM standard products as well as TRMM Multisatellite Precipitation Analysis(TMPA) products for various regions, e.g., Africa (Adeyewa
and Nakamura, 2003; Nicholson et al., 2003) and south andsoutheast Asia (Chokngamwong and Chiu, 2007). Reviews of
the other validation studies are comparisons by Chiu et al.
(2006) of TRMM retrievals (the TRMM Precipitation Radar(PR) 2A25, TRMM Microwave Imager (TMI) 2A12, TRMM
Combined Instrument (TCI) 2B31, TRMM 3B42 V5 & V6 andTRMM 3B43 V5 & V6) with Water District (WD) Rain-Gauge
rates over New Mexico. The WD data showed high biases forsatellite-only algorithms, whereas the merged satellite-gauge
products (3B43 or 3B42) and the WD gauge data are better
correlated. Bowman (2004) used TRMM TMI and PR data
retrievals for comparison with 25 rain gauges on an ocean buoyin the tropical Pacific. Agreement of gauge data with TRMM
TMI is found to be better than with TRMM PR over the tropical Pacific Ocean. Adeyewa and Nakamura (2003) validated the
TRMM PR and 3B43 rainfall products with the Global Precipitation Climatology Centre (GPCC) global precipitation analysis
rain gauge data over the major climatic regions of Africa.
Significant seasonal and regional differences are observed. One
major conclusion drawn from their assessment is that TRMM
PR data do not appear to be adequate to serve as a standalone
satellite product. The best agreements are achieved with the
TRMM 3B43 data. Nicholson et al. (2003) who used gauge datafrom a network of 920 stations over West Africa to evaluate the
TRMM (PR, TMI, 3B43) rainfall products for 1998 obtained asimilar result. While the TRMM PR and TMI products showeda net tendency to overestimate gauge measurements, the 3B43
merged product showed excellent agreement with gauge measurements on monthly to seasonal timescales. Chokngamwong
and Chiu (2007) compared gauge data from more than 100
rain gauges over Thailand with the TRMM level 3 rainfall
products (TRMM 3B42, 3B43 for V5 and V6), results revealed
that the 3B42-V6 data correlated best with the gauge data. The
above studies for different regions of the globe reiterates that
the TRMM Multi-Satellite Precipitation Analysis (TMPA) canbe used for flood forecasting studies.
As a first step, station observed precipitation data overBangladesh from FFWC and BWDB are gridded to the resolution of the IMD rainfall analysis grids for the entire period January 1988 to December 2007. From 1998 to 2007, the TRMM
3B42V6 satellite rainfall measurement is merged with other two
datasets to provide a merged product of 0.5° × 0.5° resolution.
Daily station datasets obtained from the BWDB are shown in
Figure 1(a). The domains selected for comparison between rain
gauge and TRMM 3B42V6 are shown in Figure 1(b). Similarly,
river systems in the Ganga, Brahmaputra and Meghna (GBM)
domain, the GBM river basin and the complex topography overthe GBM domain are shown in Figure 1(c) and (d), respectively.
Statistics of station observations for the merging period startingfrom January 1998 to December 2007 are given in Table 1. Stations with at least 70% of observations for the entire durationand exceeding 95% during the analysis period (1998–2007) areused in the analysis, in total 46 Bangladesh rainfall stations are
Meteorol. Appl. (2012)CC: 0.63

CC: 0.53

CC: 0.57

CC: 0.73

CC: 0.62

−1

30
25
1998
20
15
10
5
400
1999
30
20
10
400
2000
30
20
10
500
2001
40
30
20
10
300
25
2002
20
15
10
5
500
40
2003
30
20
10
400
2004
30
20
10
500
40
2005
30
20
10
500
40 2006
30
20
10
600
50
2007
40
30
20
10
0
1May

Rain (mm day )−1

Rain (mm day )Development of daily gridded rainfall dataset over GBM basins

CC: 0.66

CC: 0.71

CC: 0.69

CC: 0.28

CC: 0.73

1June

1Jul

1Aug

1sept

1 Oct

50
40
1998
30
20
10
0
70
60
1999
50
40
30
20
10
40
0
2000
30
20
10
0
40
2001
30
20
10
40
0
30
2002
20
10
0
40
30
20
10
40
0
2004
30
20
10
40
0
2005
30
20
10
60
0
50
2006
40
30
20
10
40
0
2007
30
20
10
0
1 May

CC: 0.66

CC: 0.60

CC: 0.72

CC: 0.82

CC: 0.73

CC: 0.75
2003
CC: 0.80

CC: 0.80

CC: 0.47

CC: 0.67

1 June

1 Jul

IMD+BWDB TRMM

−1

Rain (mm day )1 Aug

1 sept

1 Oct

Day

Day
Merged

80
1998
60
40
20
80
0
1999
60
40
20
80
0
60
2000
40
20
80
0
2001
60
40
20
80
0
60 200240
20
80
060 2003
40
20
80
0
60
2004
40
20
80
0
60
2005
40
20
80
0
2006
60
40
20
80
0
2007
60
40
20
0
1May

IMD+BWDB

TRMM

Merged

CC: 0.64

CC: 0.63

CC: 0.62

CC: 0.77

CC: 0.70

CC: 0.61

CC: 0.74

CC: 0.72

CC: 0.74

CC: 0.73

1June

1 Jul

1 Aug

1 sept

1 Oct

Day
IMD+BWDB TRMM

MergedFigure 5. (a) Daily evolution of rainfall from gauge IMD + BWDB (thick line) and TRMM 3B42V6 (vertical bar) for corresponding years
from 1998 to 2007 (10 years) over domain-1, the dashed line represents the merged product. Correlation co-efficient values between rain gauge(IMD + BWDB) and TRMM are shown for each year in the top right corner of each panel. b) Same as Figure 5(a), but for domain-2. (c) Sameas Figure 5(a), but for Domain 3.

used. The Bangladesh station datasets are made into gridded
datasets of uniform grid boxes equivalent to the native gridsof the Indian daily gridded dataset. The TRMM 3B42V6 satellite observation datasets with a high temporal resolution of 3 h

Copyright  2012 Royal Meteorological Society

and a spatial resolution of 0.25° from the space platform aredownloaded and processed into daily native grids of the IMD
observed rain gauge datasets with a resolution of 0.5° × 0.5°
latitude and longitude boxes. The seasonal mean rainfall pattern

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V. Prasanna et al.

and the standard deviations for June to September from the daily
averaged TRMM 3B42V6 datasets are compared with the gridded gauge datasets (IMD + BWDB) for the period 1998–2007.
Good agreements are achieved between the two datasets, as
shown in Figure 2.
2.2.1. Gridding the rainfall datasets
To create a rainfall data over the GBM basin, the nearest neighbour interpolation scheme is used. In this method, interpolated
values are computed from a weighted sum of the observations.
Given a grid point, the search distance is defined as the distance from that point to a given station. The interpolation isrestricted to the radius of influence. For search distances equal
to or greater than the radius of influence, the grid point value
is designated a missing code when there is no station locatedwithin this distance.
A simple inverse distance weighted average method (Shepard, 1968) is based on weights that are inversely proportional tothe power of the distance from the centre (Xe) of the searchingradius (Rs ).
The general formula for simple inverse distance weighted
average is given below. For a point Xe, the estimated value Ze
is defined as:
Ze =

n

{(Z(i) × W (R(i))}

(1)

i=1

where Z (i) is the sample value at point X (i); Rs is the searchradius about Xe; R (i) the distance of Xe –X (i) within Rs , andn the number of data samples within Rs .
The weight function W (i) at X (i) is calculated by:



1
R(i)P

W (i) =  n
 1
R(i)P
i=1

(2)

where R (i), represents the distance between the estimate andthe ith sample location, and p is the inverse-distance exponent.
The simple inverse distance weighted average method is aneffective method for creating geospatial averages of irregularly
distributed station observations (Weber and Englund, 1994).
The search radius is confined to twice the grid size throughout
the gridding process. The inverse-distance exponent (P ) value
is kept at 2 in this analysis.
There are a few advantages as well as disadvantages ofgridding station datasets. One of the advantages is the areal
coverage of instantaneous rain at a given location can be
estimated. The observed station provides information for the
nearby areas (i.e., areas without an observational station).
On the other hand, a few disadvantages are, in case of
an instantaneous flash flood at a given point, the intensity
information would be reduced due to gridding and also thatthe sphere of influence (search radius Rs ) does not take thetopography of the region into account.
2.2.2. Merging different precipitation products
A simple merging technique is adopted in this study. It isnoted that discrepancies exist between two observations of
the same variable from different platforms (Figures 4 and
5(a)–(c)). The satellite observations have a unique advantage
over the point observations, as they have a much larger aerial
Copyright  2012 Royal Meteorological Society

Table 2. Statistics for area average on the entire 10 years data (daily
data from 1998 to 2007).
Gauge versus
TRMM

Domain 1

Domain 2

Domain 3

Correlation
RMSE (mm day−1 )BIAS (mm day−1 )

0.59
3.76
0.05

0.69
3.89
0.11

0.68
7.17
0.69

Mean in gauge(IMD + BWDB)(mm day−1 )Mean in TRMM
(mm day−1 )

1.5

2.9

6.2

1.5

2.8

5.5

Standard deviation in
gauge(IMD + BWDB)(mm day−1 )Standard deviation in
TRMM (mm day−1 )

4.1

5.0

9.3

4.2

4.9

8.6

coverage compared to the point observations. However, theintensity of rainfall may not be captured accurately by spaceborne instruments, which is one of the major limitations ofspace borne observations. A significant portion of the IndiaBangladesh border showed a negative bias in the rain gauge
observations, due to the scarce observational network as wellas high mountain terrain (hills around the Shillong Plateau), as
shown in Figure 3(b) and (d).
To exploit the advantages of both the systems, a simple
merging technique is used to capture the heavy rainfall events.
The correlation between the two products (rain gauge andTRMM 3B42V6) for each year is significantly higher at the
99% level over the near core regions of the GBM basin(Domains 1, 2 and 3) in Figure 5(a)–(c). Therefore, merging
these two products at each grid point provides additional
information to the grids where observations are sparse. The
merging is done in such a way that the highest rainfall at eachgrid point from both products is included. The merged productwould be of use to climate risk management groups and flood
forecasters.

3. Results
Satellite-based precipitation products provide high temporal(3 h) and spatial resolutions (order of a few kilometres), but
satellites do have biases and random errors that are caused
by various factors such as sampling frequency, non-uniform
field-of-view of the sensors, and uncertainties in the rainfall
retrieval algorithms. It is therefore essential to validate thesatellite-derived products with more conventional rain estimatesto quantify the direct usability of the products.
3.1. Validation
The spatial mean rainfall and standard deviations for theJune to September monsoon season from the rain gauge
observations (IMD + BWDB) and the TRMM3B42V6 havesome differences (Figure 2(a)–(d)). The spatial patterns of
RMSE and BIAS between rain gauge and TRMM 3B42V6 areshown for the entire period (Figure 3(a) and (b)) as well as forthe monsoon season only (Figure 3(c) and (d)). The high valuesof RMSE are noted along the west coast and northeast India,
Meteorol. Appl. (2012)

Development of daily gridded rainfall dataset over GBM basins(b) 70(a) 506040TRMM (mm day−1)

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Figure 6. (a) Scatter plot for daily area averaged rainfall over Domain 1 between rain gauge (IMD + BWDB) and TRMM observations for theentire period (1998–2007). The no. of points used is 3652 and the correlation value is 0.6. The equation of fit is given by y = 0.603x × +0.5796.
(b) Same as Figure 6(a), but for Domain 2. The no. of points used is 3652 and correlation value is 0.68. The equation of fit is given by
y = 0.675x + 0.8369. (c) Same as Figure 6(a), but for Domain 3. The no. of points used is 3652 and correlation value is 0.64. The equation of
fit is given by y = 0.634x + 1.5759. (d) Frequency distribution of daily area averaged rainfall for Domain 3 between rain gauge (IMD + BWDB)
and TRMM observations for the entire period (1998–2007).

around 10–30 mm day−1 (heavy rainfall regions) (Figure 3(a)and (c)). The bias between the two datasets is not large except
for a few patchy regions along the west coast and northeast
hilly regions and regions near Bangladesh (Figure 3(b) and (d)).
Negative bias exists near the India-Bangladesh border in therain gauge estimate around the hilly regions of the Shillong
Plateau, as shown in Figure 3(b) and (d).
As a simple validation check, arbitrary domains are selected(Figure 1(b)) over the Indian land area covering Bangladesh
Copyright  2012 Royal Meteorological Society

and the representation of the monthly annual cycle (Figure 4)
and daily rainfall evolution over Bangladesh (Domain 3) fromgauge observation and satellite measurement (Figure 5(c)) areverified. It is noted from Figure 4 that the evolution of monthly
mean annual cycle over the Domains (1, 2, 3 and 4) near
the GBM basin is similar in representation from both rain
gauge observation and the TRMM 3B42V6 product. The mean
monthly cycle is similar over Domains 1, 2, 3 and 4, unlike thedomains south of it (Domains 5 and 6). The TRMM 3B42V6
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V. Prasanna et al.

Copyright  2012 Royal Meteorological Society(a) 500

TRMM (mm day−1)

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100

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data are also compared with one of the most widely used
datasets over land and ocean, the GPCP dataset. The results
show that the differences are not large over India between
the TRMM 3B42V6 and the GPCP estimate (Figure 4) sinceboth use the Global Precipitation Climatology Centre (GPCC)
gauge analysis. Nevertheless, some differences are noted among
the three domains for the entire data period between the rain
gauge observations and the TRMM 3B42V6 (Table 2). An
encouraging and salient point here is that the RMSE values(random errors) are well below the natural variability (standard
deviations of the entire data) of IMD + BWDB rain gauge andthe TRMM 3B42V6 observations.
The daily evolution of mean precipitation from 1998 to2007 over the domains near and around the GBM basin area
is shown in Figure 5(a)–(c). The correlation co-efficient (CC)value between the rain gauge observation (IMD + BWDB) andthe TRMM 3B42V6 for the respective years is shown on
the right hand corner of each panel. It is noted that the CC
values are significantly higher over Domain 3 in comparison to
Domains 1 and 2 (Figure 5(a)–(c)). It is noteworthy that CC
values over Domain 3 are consistently higher than for the othertwo domains for all years, wherein all the CC values are above
the 99% significance level. The results provide confidence formerging both the products. Rahman et al. (2009) discussed
in detail the comparison of the TRMM products (3B42V5,
3B42V6) with the IMD rain gauge datasets over India. There
are also a few studies which deal with validation and use ofboth rain gauge and satellite products over India (Krishnamurti
et al., 2009; Mitra et al., 2009).
The scatter plots of daily area averaged rain estimate overthe nearest three domains around the GBM basin are shownin Figure 6(a)–(c). From the scatter plots it is evident that
the TRMM underestimates over most of the domains, but
captures the heavy rainfall to some extent. The equations offit for each plot are also shown in the figure caption. The
comparison of frequency distribution of daily area averaged
precipitation interval between the gauge observation and the
TRMM 3B42V6 over Domain 3 is shown in Figure 6(d). It
can be easily identified from the distribution that the TRMM
slightly underestimates heavy precipitation. The correlation coefficient between the gauges and the TRMM is significantly
higher at the 99% level of significance over the domains,
0.59 (Domain 1), 0.69 (Domain 2) and 0.68 (Domain 3) inFigure 6(a)–(c). The result is satisfactory, but not enough toform a basis for merging both rain gauge and the TRMM
3B42V6 rainfall estimate to catch the heavy rainfall over eachgrid in the GBM domain.
A similar scatter plot and distribution of precipitation interval
between the TRMM 3B42V6 and the rain gauge observations
is performed from individual grid points over the domain 3 andis shown in Figure 7(a) and (b). The correlation co-efficient
between the rain gauge observation (IMD + BWDB) and the
TRMM 3B42V6 is found out to be 0.42 from 4.77 × 105 grid
points (above the 99% significance level). The equation of fit is
likewise given in the figure caption. The frequency distributionof gridded rainfall over domain 3 between the rain gauge
and the TRMM observations for the entire period is shownin Figure 7(b). The results reveal that the TRMM 3B42V6
overestimates light rainfall, in the range of 0–2 mm day−1
and underestimates moderate and moderately high rainfalls,
in the ranges 2–25 and 25–75 mm day−1 , respectively. It is
encouraging to see that the TRMM 3B42V6 is close to the gridobservation in estimating the heavy rainfalls, >75 mm day−1 .

0.4
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2–25

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>75

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Figure 7. Scatter plot from each grid point over domain-3 between rain
gauge (IMD + BWDB) and TRMM observations for the entire period(1998–2007). The no. of points used is 477 787 points and correlation
value is 0.43. Equation of fit is given by y = 0.439x + 3.0247.
(b) Frequency distribution of grid rainfall for domain-3 between rain
gauge (IMD + BWDB) and TRMM observations for the entire period(1998–2007).

3.2. Percentile calculation
In order to see the discrepancies between the rain gauges(IMD + BWDB) and the TRMM 3B42V6 in heavier rainfall
estimates clearly, the 95-percentile rank is calculated. The
estimated above 95-percentile values from the rain gauges(IMD + BWDB) and the TRMM 3B42V6 are plotted in ascatter diagram (Figure 8).
The percentile rank is calculated using the following formula:
n=

1
P
×N +
100
2

(3)

where n is rank; P is percentile probability and N is thenumber of samples. The 95-percentile rank for the rain gauges(IMD + BWDB) yielded a value of 30.265 mm day−1 and the95-percentile rank for the TRMM 3B42V6 resulted in a valueof 30.463 mm day−1 . The scatter diagrams are made basedon the 95-percentile rank for rain gauges (IMD + BWDB)(Figure 8(a)), TRMM 3B42V6 (Figure 8(b)) and for bothobservations (Figure 8(c)). The overestimation of precipitationin the rain gauge compared to the TRMM is evident fromthe scatter plots (Figure 8(a)–(c)). The total rainfall grids forthe entire period (1998–2007) over Domain 3 correspond to
4.77 × 105 points. The 95-percentile rainy grids correspond to
24 091 grid values in IMD + BWDB and 24 073 grid values
in the TRMM, whereas only 1.5% of the total grid points
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Development of daily gridded rainfall dataset over GBM basins
(a)
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gauges or the satellites, local climatology is constructed for the1998 monsoon from the IMD + BWDB gauges (Figure 10(a))
and the GPCC data (Figure 10(b)), and the difference between
those two is shown in Figure 10(d). Since the GPCC gridded climatology values are added to the satellite sensor values(TMI and PR estimates in TRMM) to create the final precipitation analysis (TRMM 3B42V6), the difference between
GPCC and the gauges highlights the problems with the gauge
observations. From Figure 10, the discrepancies are mostly
between the gauge estimates (IMD + BWDB) and the GPCC
estimates. A similar discrepancy (Figure 10(e)) is also foundwhen the recently launched Asian gridded precipitation product APHRODITE (Figure 10(c)) is considered. The notion oftaking the greatest of several estimates intentionally biases theresult: this might be acceptable for applications such as flood
forecasting since the resulting increase in false alarms increases
the probability of detection as well.
3.4.

Case studies

(c)
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Figure 8. (a) Scatter plot from each grid point over Domain 3
between rain gauge (IMD + BWDB) and TRMM observations basedon TRMM 95 percentile threshold. The equation of fit is given
by y = 0.221x + 14.992. (b) Scatter plot from each grid point overDomain 3 between rain gauge (IMD + BWDB) and TRMM observations based on rain gauge (IMD + BWDB) 95 percentile threshold. The
equation of fit is given by y = 0.232x + 49.534. (c) Scatter plot fromeach grid point over Domain 3 between rain gauge (IMD + BWDB)
and TRMM observations based on TRMM 95 percentile threshold. The
equation of fit is given by y = 0.1495x + 56.04.

(7356 points) capture heavy rainfall above the 95-percentile inboth platforms on a corresponding day basis. Out of 24 073
grid values in the TRMM estimating heavy rainfall (>95percentile), less than one-third (7356) coincide with the IMD +BWDB grids estimating heavy precipitation (>95-percentile).
Moreover, the rain gauges and the TRMM show large bias
in estimating heavier precipitation, nevertheless the correlation
between the rain gauges (IMD + BWDB) and the TRMM does
not fall below the 95% significance level, which is encouraging
and forms the basis for merging both the datasets. The equationsof fit for the scatter diagrams are given in the figure caption.
3.3.

Merged product

The merged product is a combination of both gauge (IMD +BWDB) observations and the TRMM 3B42V6 product with aheavier estimation of rainfall at each grid point. It is noted in
Figure 9 that the rainfall intensity over the major grid points ishigher in the merged product, especially over northeast India,
near the border of Bhutan with India, India and Bangladesh andthe GBM domain in particular.
In order to represent correctly where the difference in the precipitation comes from in the estimates, the GPCC datasets, theCopyright  2012 Royal Meteorological Society

The basis for merging is to obtain a higher estimate of rainfall
during floods and this is validated for a few cases, one such caseis in the 1998 monsoon year (Figure 9). During the summerof 1998, over 60% of Bangladesh is inundated for nearly
3 months. The seasonal mean rainfall (July to September)in the rain gauge observations (Figure 9(a)) near the IndiaBangladesh border areas is not heavier. This might be due tosparse observations over the border regions. By merging the
TRMM 3B42V6 information, the merged product gives a higher
estimate over these grids (Figure 9(d)), which is additional
information for flood forecasters in Bangladesh.
3.4.1. Case study 1: July to August 2004
To check for heavy flood events during recent years, aknown heavy flood during the week of 27 July to 3 August
2004 (widespread flooding over Bangladesh) is considered for
examination. The rain estimates shown in Figure 11(a) are themean accumulated rainfall between 27 July and 3 August 2004.
It can be noted from Figure 11(a) that the heavy floods overBangladesh are not captured well in the rain gauge observation,
whereas the TRMM (Figure 11(b)) captures some heavy rainnear the India-Bangladesh border. In the bottom panel itis seen that the rainfall underestimation is between 10 and
20 mm day−1 in the rain gauge near the India Bangladesh
border (Gauge – TRMM) (Figure 11(c)). It is obvious that thegauge and the TRMM merged product gives a higher estimate
as shown in Figure 11(d).
3.4.2. Case study 2: August 2007
Similarly, there were heavy floods during the week of
3–13 August 2007, wherein by 13 August 2007 the confirmed
death toll in Bangladesh was 405. This event is considered
as case 2. The rain estimates shown in Figure 12(a) is themean accumulated rain between 3 and 13 August 2007. The
heavy rainfall west and northwest of the India-Bangladesh
border is not captured well in the rain gauge observations, whereas the TRMM 3B42V6 captures this feature(Figure 12(b)). In the bottom panel underestimation in thegauge observation is between 5 and 10 mm day−1 over theseareas (Gauge – TRMM) (Figure 12(c)). The merged productproduces a higher estimate around the Bangladesh region(Figure 12(d)).
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V. Prasanna et al.

Figure 9. (a) Monsoon seasonal mean rainfall (June, July, August, September) for the year 1998 from daily observation of IMD and BWDB
gridded data (IMD + BWDB) and (b) TRMM. (c) Difference in seasonal mean rainfall for June, July, August, September and (d) merged productof the above two products.

In the above two cases, the flooding is severe in the
Brahmaputra river. In both cases, underestimation is seen in therain gauge data compared to the TRMM-3B42V6 data along
the border areas, therefore merging the satellite information
provides additional rain estimates in these regions which will
help the flood forecasters and also help in correcting the biases
in the model climate forecasts aimed at flood forecasting overBangladesh.
Bookhagen and Burbank (2010) presented a detailed hydrological budget over the Himalayas. A similar hydrologicalbudget would be needed for flood forecasting over the GBM
Copyright  2012 Royal Meteorological Society

domain. The GBM domain has a complex terrain due to thepresence of the high Himalayas on its northern periphery as
well as the presently available datasets pose major limitations
on modelling the hydrological budget over the GBM domain.
Despite using the high temporal resolution remote sensing
datasets and a large network of gauge observations, this study
has several shortcomings. The study has only focussed on the
precipitation from the TRMM, rain gauges and gridded precipitation products from Bangladesh and India, respectively,
but does not include either snowmelt or runoff from the highHimalayan Mountains or river discharge measurements from
Meteorol. Appl. (2012)

Development of daily gridded rainfall dataset over GBM basins

Figure 10. (a) Monsoon seasonal mean rainfall (June, July, August, September) for the year 1998 from daily observation of IMD and BWDB
gridded data (IMD + BWDB). (b) Monsoon seasonal mean rainfall (June, July, August, September) for the year 1998 from daily observationof GPCC and (c) monsoon seasonal mean rainfall (June, July, August, September) for the year 1998 from daily observation of Aphrodite data.
(d) Difference in seasonal mean rainfall for June, July, August, September between GPCC and rain gauge and (e) difference in seasonal mean
rainfall for June, July, August, September between Aphrodite and rain gauge.

the major rivers to quantify the inundation or flooding overBangladesh. Nevertheless, the data serves as additional information to the flood forecasters and climate modelers aimed atflood forecasting.

4.

Conclusion

This paper has focused extensively on the Ganga, Brahmaputra and Meghna (GBM) river basin domain precipitationand the usability of multi-source precipitation datasets. The
efforts in comparing multi-source precipitation dataset over the
Copyright  2012 Royal Meteorological Society

GBM domain are more complicated due to its geographicallocation and steep topography, but its potential application isenormous. Although there are discrepancies in the observations among different platforms, the aim to achieve a higher
probability rainfall estimate over the GBM could be acceptable
for applications such as flood forecasting, since the resulting
increase in false alarms increases the probability of detection
as well. The merged rainfall dataset (0.5° × 0.5° resolution)
developed using the Indian gridded dataset, the Bangladesh
rain gauge station observations and the TRMM 3B42V6 products would serve as a useful precipitation product for climate
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V. Prasanna et al.

Figure 11. (a) 27 July to 3 August 2004 (flood) from daily observation of IMD and BWDB Gridded data (IMD + BWDB) and (b) TRMM.
(c) Difference in mean rainfall between Gauge and TRMM and (d) merged product of the above two products.

risk management groups involved in flood forecasting activities, water modelling and climate change adaptation of heavy
floods. The data would also serve as a validation tool for thehigh-resolution climate model simulation over the GBM basin.
The data development is aimed at providing additional information on rain estimates over the upstream areas and low-lying
areas near the Ganges and Brahmaputra catchments of the IndiaBangladesh border. Based on the results from this study the
TRMM-3B42V6 dataset is reasonable in capturing the spatial
and temporal rainfall distribution over the GBM region. Also, it
was found that by merging the satellite and the rain gauge measurements, additional information over data sparse regions in
the GBM domain is obtained. The GBM domain has one of themost complex topographies in the globe and observations overthe domain are very limited. The hydrological budget over thisdomain is very dynamic and complex to model (Bookhagen
and Burbank, 2010). Given the above-mentioned limitations,this paper only provides additional information by merging therain gauge observations with remote sensing observations and
Copyright  2012 Royal Meteorological Society

does not provide a complete hydrological budget, which would
be desirable for flood forecasting.

Acknowledgements
We thank A. R. Subbiah for providing facilities to carry out thiswork. This work is supported by ADPC, Bangkok, Thailand.
The work is carried out as part of the DANIDA supported
project being implemented by IWM, Bangladesh, which is
gratefully acknowledged. We wish to acknowledge the BWDB,FFWC of Bangladesh for providing the station rainfall datasetsand the IMD of India for providing gridded rainfall data andNASA/GSFC for archiving and providing the 3B42-V6 datafrom their ftp site. The authors specially like to thank Dr.
George Huffman for his comments in improving the manuscript
and also like to thank the editor Prof. P. J. Burt and two otheranonymous reviewers for their constructive comments. Free
Software packages like Fedora, Intel Fortran compiler; GrADS
and Xmgrace have been used for data analysis and graphics.
Meteorol. Appl. (2012)

Development of daily gridded rainfall dataset over GBM basins

Figure 12. Same as Figure 11 but for 6 August to 13 August 2007.

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