VOLUME 4
JOURNAL OF HYDROMETEOROLOGY
OCTOBER 2003
Daily Rainfall for the Indian Monsoon Region from Merged Satellite and Rain Gauge
Values: Large-Scale Analysis from Real-Time Data
A. K. MITRA,* M. DAS GUPTA,
AND
S. V. SINGH
National Centre for Medium Range Weather Forecasting, New Delhi, India
T. N. KRISHNAMURTI
Department of Meteorology, The Florida State University, Tallahassee, Florida
(Manuscript received 24 July 2002, in final form 24 December 2002)
ABSTRACT
A system for objectively producing daily large-scale analysis of rainfall for the Indian region has been
developed and tested by using only available real-time rain gauge data and quantitative precipitation estimates
from INSAT-1D IR data. The system uses a successive correction method to produce the analysis on a regular
latitude–longitude grid. Quantitative precipitation estimates from the Indian National Satellite System (INSAT)
operational geostationary satellite, INSAT-1D, IR data are used as the initial guess in the objective analysis
method. Accumulated 24-h (daily) rainfall analyses are prepared each day by merging satellite and rain gauge
data. The characteristics of the output from this analysis system have been examined by comparing the accumulated monthly observed rainfall with other available independent widely used datasets from the Global
Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation
(CMAP) analyses. The monthly data prepared from the daily analyses are also compared with the subjectively
analyzed India Meteorological Department (IMD) monthly rainfall maps. This comparison suggests that even
with only the available real-time data from INSAT and rain gauge, it is possible to construct a usable largescale rainfall map on regular latitude–longitude grids. This analysis, which uses a higher resolution and more
local rain gauge data, is able to produce realistic details of the Indian summer monsoon rainfall patterns. The
magnitude and distribution of orographic rainfall near the west coast of India is very different from and more
realistic compared to both the GPCP and CMAP patterns. Due to the higher spatial resolution of the analysis
system, the regions of heavy and light rain are demarcated clearly over the Indian landmass. Over the oceanic
regions of the Arabian Sea, Bay of Bengal, and the equatorial Indian Ocean, the agreement of the analyzed
rainfall at the monthly timescale is quite good compared to the other two datasets. For NWP and other model
verification of large-scale rainfall, this dataset will be useful. In the field of rainfall monitoring within weather
and climate research, this technique will have real-time applications with data from current (METSAT ) and future
(INSAT-3A and INSAT-3D ) Indian geostationary satellites.
1. Introduction
Rainfall is the most important meteorological parameter affecting India’s economy and other activities of
strategic importance. Observations of rainfall are needed to support a range of services extending from the
real-time monitoring and prediction of severe weather
to climatological studies of drought. Precipitation also
plays an important role in the global energy and water
* Current affiliation: Department of Meteorology, The Florida State
University, Tallahassee, Florida.
Corresponding author address: Dr. A. K. Mitra, Visiting Research
Associate, Department of Meteorology, The Florida State University,
Tallahassee, FL 32306.
E-mail: akmitra@earl.met.fsu.edu
q 2003 American Meteorological Society
cycle. One of the most critical issues in modeling the
global atmosphere and climate by general circulation
models (GCMs) is the simulation and initialization of
precipitation processes. GCM results need to be verified
based on observational precipitation data to monitor the
progress made in models and observation techniques
(Janowiak 1992). The global and regional climate modeling community will need regional rainfall data for
verifying the regional details derived from model simulations. The Asian monsoon system, in terms of the
circulation and rainfall, is of great importance to climate
researchers for understanding and predicting the general
circulation and its variability at different space and time
scales. In order to better understand and predict the
monsoon, different atmospheric models and coupled
ocean–atmosphere models are being used to simulate
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both the seasonal mean rainfall patterns and their interannual variability. It is also felt that the interannual
variation of the Asian monsoon is linked to intraseasonal
variations through various feedback mechanisms.
Therefore, realistic daily and monthly rainfall datasets
will be useful for studying the intraseasonal and interannual variations in the Indian summer monsoon (Kripalani et al. 1991). Observational and model simulations
of the Madden–Julian oscillation (MJO), which includes
large-scale organization and propagation (eastward and
northward) of convective cloud clusters within the equatorial waveguide during the northern summer monsoon,
are challenging scientific problems related to the understanding of the monsoon system. Daily analyses of
rainfall data will be required to study these aspects of
the monsoon.
From the daily data, it is easy to produce monthly
data for a longer period, which will aid in studying the
seasonal cycle and the interannual variability of the
monsoon. These daily and monthly representative datasets of the observed rain will be ultimately used for
different model verifications. Various major operational
weather forecasting centers in the United States, Europe,
Australia, and Japan have started issuing extended range
(monthly to seasonal) dynamical predictions of rainfall
for different geographical regions of the world. Considerable effort is put into refining the dynamical seasonal to interannual predictions by using ensemble
methods, including multimodel ensembles. Research on
these aspects of climate variability and predictability on
seasonal to interannual timescales and the interaction of
seasonal effects with monsoonal flow, is continuing. The
dynamical, seasonal prediction study with the objective
of assessing the predictability of the mean circulation
and rainfall up to a season in advance is carried out. In
order to make progress in the area of dynamical extended range prediction of monsoon rainfall, the first
requirement is obtaining observed rainfall data (used in
verifying the models) and understanding the processes
involved. Due to the previously mentioned reasons, in
recent years there has been considerable interest among
different scientific groups in the preparation and intercomparison of gridded rainfall datasets (Huffman et
al. 1997; Xie and Arkin 1997; Janowiak and Xie 1999;
Gruber et al. 2000; Beranger et al. 2001; Grassl et al.
2000). Rainfall prepared under the Global Precipitation
Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation
(CMAP) are the most widely used and helpful datasets
for various scientific research conducted in the area of
weather and climate. These datasets are derived from
remote sensing observations and in situ measurements
from rain gauges. Short-range forecasts from atmospheric numerical models are used in the CMAP analysis. However, the contribution from a forecast model
may be marginal. These new continuous sets of data
are able to produce new climatologies of rainfall for
different geographical regions of the world. In spite of
VOLUME 4
differences in terms of their magnitudes, the seasonal
cycle and interannual variability are produced adequately by these datasets. But there are still regions
where the disagreement between the datasets may be
of high order. The Asian monsoon region is one such
important area where more detailed comparisons are
required to assess the strengths and weaknesses of
these popular datasets.
In India, different types of numerical weather prediction models are being used for operational and research purposes by various organizations. Rainfall prediction by such numerical forecast models for different
Indian domains is of crucial importance to activities like
agriculture and other economic activities. Observed
rainfall datasets are needed on latitude–longitude grids
to verify the rainfall forecasts from these operational
numerical models. Various research scientists and organizations also require these verifying rainfall datasets
in order to continuously improve the forecast models—
by improving model resolution, physical parameterizations, and improved satellite data usage during data
assimilation. At the Indian National Centre for Medium
Range Weather Forecasting (NCMRWF), a global operational T80/L18 analysis/forecast system is being run
daily to produce real-time medium-range weather forecasts. These are disseminated to different agro-meteorological field units located in different agro-climatic
zones of the country for agriculture-related operations.
The global forecast model produces large-scale rainfall
forecasts 5 days in advance, in roughly 1.58 by 1.58
latitude–longitude grid boxes. Verification of rainfall
forecasts from global weather forecast models requires
suitable observations, upscaling, and interpretation techniques (Cherubini et al. 2001). Local observations play
an important role in preparing such verifying datasets
(Mills et al. 1997; Weymouth et al. 1999). To find out
the performance of the NCMRWF global numerical
model in terms of the rainfall for different weather
events, it was necessary to develop a rainfall analysis
system on a 1.58 latitude–longitude grid, representative
of the model’s resolvable scales. In this study, with
available real-time rainfall data only from satellite and
rain gauges, daily objectively analyzed rainfall datasets
have been prepared on regular latitude–longitude grids
for the full summer monsoon season of 2001 (June–
September) for the Indian region. The monthly accumulated rainfall from this daily analysis is then compared with the other available popular datasets for the
monsoon region. Inferences about the quality of these
merged data are made in relation to the existing popular
datasets.
2. Method and data
In this section, the daily rainfall analysis procedure
using the available real-time data is described. Because
the monthly datasets are compared with GPCP and
CMAP analyses, the data used and the analysis proce-
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MITRA ET AL.
dures of GPCP and CMAP are briefly mentioned, along
with their characteristics as reported by some recent
studies.
a. Analysis procedure and data used
The first guess used in the successive correction method for rainfall analysis is taken from the satellite meteorology division of the India Meteorological Department, New Delhi. The estimated rainfall from the Indian
National Satellite System geostationary satellite INSAT1D (positioned at 748E longitude) IR data is based on
the Geostationary Operational Environmental Satellite
(GOES) precipitation index (GPI) technique (Arkin and
Meisner 1987; Arkin et al. 1989). The Very High Resolution Radiometer (VHRR) onboard INSAT-1D included a visible channel operating in the spectral wavelengths of 0.55–0.75 mm and an infrared channel operating at 10.5–12.5 mm. The spatial resolution of the
visible and infrared channels are 2.75 and 11 km, respectively. The domain selected for the rainfall analysis
is from 408 to 1188E longitude and 398S to 398N latitude,
cast in a 1.58 3 1.58 grid. This domain was chosen
because the daily 24-h accumulated (valid at 0300 UTC)
rainfall estimates from INSAT were available in real
time for the defined region on 18 3 18 regular grids.
The satellite-derived rainfall estimates are interpolated
bilinearly to the 1.58 analysis grid and are used as the
first guess in the objective analysis procedure. The main
reason for performing analysis on a 1.58 grid is that at
NCMRWF, a global T80/L18 (equivalent horizontal resolution of 1.58) model is used for issuing medium-range
predictions in real time. The model rain can now be
verified against this merged rain analysis, which has the
same resolution. The other data used are the 24-h accumulated (valid at 0300 UTC) rainfall values from rain
gauge observations available through the global telecommunication system (GTS) network in real time for
the same domain. Figure 1a shows the geographical distribution of such gauge observations on a typical day.
Figure 1b shows the amount of daily available data from
such sources during the monsoon season of 2001. All
the data used here are available in real time and the
present study is aimed at testing the technique for realtime rainfall analysis production in operational use for
the Indian region. In Fig. 1b, the solid line indicates the
availability of data for the whole analysis region, and
the dashed line indicates data for the Indian region (58–
408N, 658–1008E). On average, 257 observations were
available per day in the whole analysis domain, and out
of that, 212 per day were from the Indian box. India
being surrounded by oceans, we notice that most of the
real-time data for the full analysis domain comes from
the Indian box. We can say that on average 82% of the
total rain gauge data were from the Indian box region.
The objective analysis technique for rainfall used here
is based on the successive correction method of Cressman
(Tripoli and Krishnamurti 1975; Krishnamurti et al. 1983;
Mitra et al. 1997). This involves the successive modification of an initial guess field (satellite estimates) based
on observed station data (rain gauge). Presuming that the
gauges are perfect, the error (bias) correction for the
satellite estimate at each grid point is derived. First, the
satellite estimates are interpolated to station locations to
form a first guess. Their differences from the observed
station values provide an error estimate at the station
locations. This set of irregularly spaced values is used to
derive corrections at the desired grid point using successive iterative corrections. Consider the guess field (satellite rainfall in mm day 21 ) rgij(n) defined at each grid point
(i, j), on the y th iterative guess. From the two-dimensional initial guess field array, the guess value at each
station location s is computed by a standard nine-point
Lagrangian interpolation formula. The Cressman weight
function used in the analysis is defined by
vi j 5
R2 2 d2
for d , R,
R2 1 d2
vi j 5 0
and
for d . R,
(2.1)
where R is the influence radius and d is the distance of
the station from the grid point. During the successive
correction, we used four scan radii of 1.58, 1.48, 1.28,
and 1.08. Since the intention is to represent the observed
large-scale monsoon rainfall at a 1.58 grid box, we intentionally select the scan radii slightly larger than the
grid box size to accommodate and account for the continuity of the large-scale rainfall in relation to the processes happening in the neighboring grids. Next, the
error (observation minus guess at station location) E (n)
s
between the interpolated value rg s(n) and the actual observation (station value) r s(n), given by
(n)
E (n)
2 rg s(n) ,
s 5 rs
(2.2)
is computed. The value of the E gives a correction to
a nearby grid point for the (new 1 1) iterative value.
This correction is given by
(n)
s
OW E
5
OW
(n)
s
C i(n11)
j
(n)
s
s
(n)
s
,
(2.3)
s
where W (n)
s is a weighting factor defined as
(n)
W (n)
(d)bg
s 5 v
(2.4)
Here, v (n) in Eq. (2.4) is the Cressman weighting function at the iteration level (new) as described earlier. The
radius of influence R is a function of the iteration, which
decreases with each scan, from 1.58 in first scan to 18
in the fourth scan. The time weighting function b is
kept equal to one as all the rain gauge data used in this
study belong to the same valid 24-h time window ending
at 0300 UTC. The reliability estimate of each observation type, described by g , is also kept as 1 as only
one type of observation (rain gauge) is used now. In
the future, different data types (rain radar, other satellite
estimates) appearing in the same grid box can be as-
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FIG. 1. (a) Distribution of available real-time rain gauge data stations in the analysis region
on a typical day. (b) Time series indicating number of daily available rain gauge data during
122 days (1 Jun–30 Sep) of the 2001 monsoon season. Solid line is data from the full region;
dashed line is data from Indian region.
signed different reliability estimates within the respective weights.
The next iterative guess is obtained by the formula,
rg ij(n11) 5 rg ij(n) 1 aC (n11)
,
ij
(2.5)
where a is a smoothing factor related to the center of
gravity of the influencing data. The smoothing factor is
used to force the resultant field to press smoothly into
the guess field while approaching data-void regions. Accepting the first guess field over data-void regions is
believed far more reliable than extrapolation of distant
existing data.
b. GPCP and CMAP analyzed data
GPCP (Huffman et al. 1997) and CMAP (Xie and
Arkin 1997) are the two published and widely used
datasets that contain analyses of global precipitation derived by merging rain gauge estimates with multisatellite estimates of precipitation. The GPCP version 2 final
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FIG. 2. (a) Different aspects of variability of observed rainfall during the 2001 monsoon season.
Solid dark line is daily maximum rain reported by rain gauge in mm day 21 ; solid light line is
daily variance of rain computed from reported rain gauges; dashed line is daily computed rmse
of analyzed rain in mm 3 10. (b) Daily variance of rainfall in observed data and final analysis
during the 2001 monsoon season. Dashed line is daily variance computed from rain gauge data
only; solid light line is daily variance computed from analyzed rain data; solid dark line is ratio
of variance of analyzed and observed rain expressed in percentage.
analysis (on a 2.58 latitude–longitude global grid) uses
oceanic satellite estimates of precipitation derived from
the Special Sensor Microwave Imager (SSM/I) emission
algorithm. Over land, the SSM/I scattering algorithm
estimates are used. Then the magnitudes of the IR-based
estimates are adjusted in relation to the microwavebased (SSM/I) precipitation estimates (Adler et al.
1994). Poleward of 408 latitude where IR-based estimates are not usable, remotely sensed estimates from
the Television Infrared Observation Satellite (TIROS)
Operational Vertical Sounder (TOVS) are incorporated.
This combination of SSM/I, IR, and TOVS forms the
multisatellite GPCP analysis. Over land, the bias in the
multisatellite analysis is adjusted by rain gauge analyses, but no adjustment is made over ocean. In the final
step, the multisatellite and rain gauge analyses are
merged using an inverse error-weighting scheme to form
the final GPCP analysis. The temporal and spatial scales
of the CMAP datasets are the same as for GPCP. In the
CMAP analysis, the first step is to combine the various
satellite estimates based on IR, SSM/I (scattering algorithm over land and ocean, emission algorithms over
ocean), and Microwave Sounding Unit (MSU) data using a maximum likelihood approach in which the
weighting coefficients are inversely proportional to the
squares of the individual random errors. These errors
are assessed over land areas by comparing the satellite
estimates with rain gauge analyses and over oceans by
comparing them with rain gauge precipitation over atolls
that are located in the western Pacific. The latter are
assumed representative of open ocean conditions. Over
land, the resulting satellite-based analyses are then
merged with rain gauge analyses via a blending technique in which the satellite estimates provide the field
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FIG. 3. IMD monthly observed rainfall during the 2001 monsoon season.
shape, which is anchored to the available rain gauge
data. Over ocean, the gauge adjustment is a bulk scheme,
with latitude dependence tapering away from the equator. CMAP analyses use the NWP rainfall forecasts,
which are found to be useful for middle and higher
latitudes.
While there is good large-scale agreement between
the two datasets, considerable regional differences also
exist (Gruber et al. 2000; Janowiak et al. 2001). Since
both datasets use similar gauge stations, their agreement
is good over land. Over ocean, large-scale regional disagreements are reported. Over many of the oceanic tropical areas, CMAP is 20%–50% wetter compared to
GPCP. In contrast, GPCP is wetter than CMAP over all
oceanic areas poleward of about 458. The differences
that are noted are due primarily to differences in the
input data and methodologies by which the various input
data are merged. Although regional differences occur
in the mean fields, the analyses compare well in terms
of monthly anomalies. Seasonal variations are rather
similar (correlation . 0.7) in most of the Tropics and
midlatitudes. It has been shown (Adler et al. 2001) that
merged precipitation datasets such as GPCP and CMAP
provide superior results compared to other published
and available products.
3. Performance of the analysis system during
summer monsoon 2001
2001 had a normal monsoon, as discussed in a special
issue report from the India Meteorological Department
(IMD; 2001b). Rainfall for the country as a whole was
92% of its long-period average. During this year, the
rainfall was more evenly distributed compared to the
previous year. During the 2001 monsoon season (June–
September), up to the third week of August, the rainfall
was above normal and thereafter it was persistently below normal. Figure 2a indicates different aspects of the
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MITRA ET AL.
775
FIG. 4. GPCP monthly observed rainfall during the 2001 monsoon season (contour interval is 10 cm; shading
from 5 to 10 cm).
observed rainfall variability during the monsoon season
as inferred from rain gauge data only. The dark solid
line shows the maximum rainfall (in mm day 21 ) reported from any station on different days, roughly in-
dicating the increase or decrease of convective activity.
After 85 days (during September) the rainfall activity
is reduced. The light solid line shows the daily variance
of observed rainfall computed from rain gauge data dur-
FIG. 5. CMAP monthly obs rainfall during the 2001 monsoon season (contour interval is 10 cm; shading from 5
to 10 cm).
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FIG. 6. NCMRWF monthly observed rainfall during the 2001 monsoon season: (a) Jun, (b) Jul, (c) Aug, and (d) Sep. Contours are at 10,
20, . . . , 70, 100, 130 cm in (a) and (b), and every 10 cm in (c) and (d). Shading is between 5 and 10 cm in all panels.
ing the season. We notice that the two curves have a
good correlation. This shows that whenever the rainfall
activity increases, the spatial variability of rainfall also
increases. The dashed line shows the computed daily
root-mean-square error (rmse) of the objectively analyzed rain with respect to the rain gauge observations.
For convenience of plotting and visualization, the rmse
reported in millimeters is multiplied by a factor of 10.
We notice that in general the rmse of the final analysis
is around 6.5 mm. The day-to-day changes in the rmse
curve also agree very well with the other two curves.
The quality of the final analysis improves (deteriorates)
with a(n) decrease (increase) in rainfall activity in the
region during the monsoon. Figure 2b shows the amount
of spatial variability in the rainfall observations and in
the final analysis. The dashed line indicates the spatial
variance of the daily rainfall computed from the reported
rain gauge data only. In the rain gauge data, stations
with ‘‘no-rain’’ cases are also included. The light solid
line shows the daily spatial variance of the objectively
analyzed daily rain. The dark solid line shows the ratio
of the variance of the analyzed rain to the variance of
rainfall observations in terms of percentage. As expected, the monsoon rainfall has a high spatial variability as seen from the gauge data. The average of the
daily variance during the season from rain gauge data
is 195 mm 2 . However, the variance seen in the analyzed
field is much lower, around 56 mm 2 on average. In a
mean sense (average of 122 days) the variance of the
objectively analyzed rain is 36% of the original variance
of the gauge data.
By adding the daily analyzed values, monthly total
rainfall maps were prepared for four different months
(June, July, August, and September) for the summer
monsoon of 2001. These are now compared with the
GPCP and CMAP analyses for finding the usability of
this new dataset At the India Meteorological Department’s research section, monthly rainfall maps for the
Indian land region are prepared (and published regularly
since 1996) by subjectively analyzing the good quality
monthly rainfall data in the form of hand-drawn contours (India Meteorological Department 2001a). These
contour maps of monthly analyzed rainfall depict the
large-scale distribution of monsoon rainfall during each
month (Figs. 3a–d). Figures 4a–d and 5a–d show the
objectively analyzed rainfall from GPCP and CMAP,
respectively, for the corresponding monsoon months of
2001. Figure 6a–d shows the objectively analyzed
monthly total rainfall prepared by the present method
(NCMRWF) during the 2001 monsoon.
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MITRA ET AL.
777
FIG. 7. NCMRWF monthly observed rainfall (smoothed) during the 2001 monsoon season (contour interval is 10
cm; shading from 5 to 10 cm).
June is normally the month when the summer monsoon rainfall regime over India advances in a northnorthwestward direction to cover the whole country.
Figure 3a indicates that most of the country had more
than 10 cm of rainfall. The west coast of India shows
rainfall amounts from 20 to 120 cm. Compared to the
IMD analysis, the GPCP analysis (Fig. 4a) underestimates the rainfall over the Indian landmass. The highrainfall regions along the west coast, east coast, and in
the central region are missing in the GPCP analysis. The
rainfall distribution in the CMAP analysis (Fig. 5a) for
June is closer to the IMD analysis than to the GPCP
analysis. In the CMAP analysis, the west coast rain (50
cm) is higher (closer to the IMD) than GPCP. In CMAP,
another area of heavy rainfall is seen near the head
region of the Bay of Bengal (Bangladesh coast), where
the highest contour reaches up to 110 cm. This highrainfall region has to be verified from other independent
datasets. Over oceanic regions of the Arabian Sea, Bay
of Bengal, and the equatorial Indian Ocean, the rainfall
distribution pattern seen in CMAP and GPCP generally
agrees well, except when CMAP indicates slightly higher values. The NCMRWF analysis for June (Fig. 6a),
which was performed at a higher resolution and with
more local data, naturally shows more details compared
to both GPCP and CMAP analyses. In the NCMRWF
analysis, areas with rainfall amounts less than 10 cm
match well with the IMD analysis and are close to
CMAP. As seen in the IMD analysis, we find in the
NCMRWF analysis two zones of maxima on the west
coast of India. The IMD data for June indicate a station
named Mangalore reporting 129 cm, while the maximum value of the contour in NCMRWF analysis is seen
to be between 100 and 130 cm for the same region. The
sharp gradient of rainfall between the west coast heavyrain region and the rain shadow region to the east is
brought out realistically in the NCMRWF analysis.
During the month of July, generally, the steady monsoon flow is well established, and the whole country
receives more rainfall compared to June. In the IMD
analysis (Fig. 3b), contours of 20 cm or more cover the
whole Indian region. On the west coast, we find rainfall
between 20 and 100 cm. We find another region with a
maxima of 40 cm of rainfall along the monsoon trough
(contours running in the southeast to northwest direction). The northeast part even shows a contour of 60
cm. The west coast rainfall amount in the GPCP (Fig.
4b) is only 30 cm compared to 100 cm in the IMD. In
the CMAP analysis (Fig. 5b) the west coast rainfall goes
up to 60 cm. The NCMRWF analysis produces realistically high values of rainfall along the west coast, as
also reported in the IMD analysis. Rainfall along the
monsoon trough in the NCMRWF is also seen realistically extending in the northwest to southeast direction.
In the NCMRWF analysis, we get another region of
maximum rainfall (up to 70 cm) along the foothills of
the Himalayas mountain range. Contours in the IMD
map indicate a region of high rainfall in that area, but
the values are lower compared to the NCMRWF. This
may be because the IMD does not include data from
neighboring Nepal in its analysis. In the NCMRWF
analysis, data from all neighboring countries received
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FIG. 8. 2001 monsoon seasonal total rainfall of (a) IMD, (b) NCMRWF, (c) GPCP, and (d) CMAP (Jun–Sep 2001 total rainfall).
through the global telecommunication system are included. This high-rainfall region near the Himalayan
foothills is also not seen in the GPCP and CMAP analyses.
Like other previous months, in August, the GPCP
(Fig. 4c) rain over the west coast is underestimated (20
cm) compared to the IMD analysis (Fig. 3c). CMAP
(Fig. 5c) is better than GPCP, as it reports a higher value
of 30 cm. In the NCMRWF analysis (Fig. 6c), the 20cm contour agrees well with the IMD analysis. On the
west coast in the NCMRWF analysis, we find two maxima of 50 and 80 cm, which agree well with the IMD
pattern. This realistic rainfall was not seen in the GPCP
and CMAP analyses. In the NCMRWF analysis the rainfall along the trough and the rain-shadow regions are
also reproduced realistically when compared to the IMD
analysis. Due to the higher resolution of the grid selection in the NCMRWF analysis, it has more features
compared to the GPCP and CMAP patterns. Similar to
July, August also has a very heavy rainfall region along
the foothills of the Himalayas with 100 cm of rainfall.
This high-rain region is noticed in the IMD and CMAP,
but their magnitudes are seen to be much lower.
During September, the typical monsoon flow system
generally starts to weaken and the associated reduced
rainfall pattern withdraws from the northwest part of
India toward the south and southeast (Fig. 3d). GPCP
(Fig. 4d), CMAP (Fig. 5d), and NCMRWF (Fig. 6d)
also show similar patterns of a withdrawn monsoon. The
coastal Kerala region in the NCMRWF analysis shows
40-cm rains matching with the IMD values. In the
NCMRWF analysis, another region of high rainfall in
northeast India is also reproduced. Heavy rainfall up to
50 cm along the foothills of the Himalayas in the
NCMRWF analysis is missing in the GPCP and CMAP
analyses. This rainfall along the foothills is also noticed
in the IMD analysis. Over the oceanic regions, the
NCMRWF analysis agrees with the CMAP and GPCP
patterns in capturing the oceanic monsoon rainfall regime during all four months. However, the maximum
values are realistically higher in the CMAP and GPCP,
as estimates from microwave data were used there.
When a nine-point smoother is applied to the NCMRWF
analyzed data (Figs. 7a–d), it loses many details, as seen
in Figs. 6a–d. For June, the smoothed version of the
NCMRWF analysis generally agrees very well with
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MITRA ET AL.
779
FIG. 9. Quantitative comparison of rainfall amounts from various analyses of different regions for (a) Jun, (b) Jul,
(c) Aug, and (d) Sep of the 2001 monsoon season.
CMAP over both land and ocean. The former, however,
shows higher values on the west coast, which is also
closer to the IMD. The July chart retains more regional
details as compared to CMAP and GPCP. The August
chart looks very similar to the GPCP and CMAP patterns, except along the west coast, where it shows higher
values (closer to IMD). The September smoothed field
is very similar to the CMAP analysis.
The observed seasonal total rainfall from the IMD,
NCMRWF, GPCP, and CMAP analyses are shown in
Figs. 8a–d. Very good agreement between the IMD and
NCMRWF analyses is seen over the west coast of India
(300-cm contour). Along the foothills of the Himalayas,
several pockets of 200-cm rainfall are seen in both analyses. In the GPCP and CMAP analyses, the maximum
west coast rainfall reaches only 80 and 160 cm, respectively, which is very low compared to the IMD
observations. Over the oceanic region, the areas of
heavy and light rains match well in all three (NCMRWF,
GPCP, and CMAP) analyses. The location of the equatorial maximum in rainfall (58S, 928E) is brought out
in all the three analyses. However, in the maxima there
is a 20-cm difference in the rainfall values among the
three analyses. Both GPCP and CMAP use microwavebased rain data. However, the NCMRWF analysis is
only from IR-based estimation, which leads to an underestimation. Figure 9 shows the quantitative comparison of rainfall amounts from different analyses at im-
portant (synoptic) locations within the Indian monsoon
region. The regions selected are the northern part of the
west coast (NWC) and the southern part of the west
coast (SWC), which are regions of heavy rainfall associated with active phases of the monsoon. The eastern
coast near the head of the northern Bay of Bengal
(ECHNB) is another region associated with the formation of monsoon lows and depressions. The fourth
region is the central part of India associated with the
monsoon trough (CIT). For the west coast region, we
find that the NCMRWF analysis agrees well with IMD
and is better than CMAP and GPCP. For the remaining
two regions, all four analyses agree well with each other.
4. Summary
Daily large-scale monsoon rainfall data have been
prepared by objective analysis using available real-time
rainfall estimates from satellites and rain gauges. The
characteristics of the output from this analysis system
have been examined by comparing the accumulated
monthly observed rainfall from other available independent popular datasets like the GPCP and CMAP
analyses. The monthly data prepared from daily analyses
are also compared to IMD monthly rainfall maps produced by subjective analysis. It is seen that with available real-time data from INSAT-1D and rain gauges, it
is possible to construct a usable large-scale rainfall map
780
JOURNAL OF HYDROMETEOROLOGY
on regular latitude–longitude grids. This high-resolution
NCMRWF analysis uses more local rain gauge data,
and it is able to more realistically produce details of the
typical features of the Indian summer monsoon rainfall
patterns. The magnitude and distribution of the orographic rainfall near the west coast of India is very
different (realistic) when compared to both the GPCP
and CMAP patterns. The regions of heavy and light rain
are demarcated clearly over the Indian landmass. Over
the oceanic regions of the Arabian Sea, Bay of Bengal,
and the equatorial Indian Ocean, the agreement of the
analysed rainfall at the monthly scale is quite good compared to the other two datasets. For NWP and other
model simulation verification of large-scale rainfall, this
dataset will be useful.
5. Future work
The satellite rainfall estimates used here are from the
INSAT-1D geostationary satellite, the last one in the
INSAT-1 series. It was operational in July 1990 and it
continued to provide IR data until April 2002. The Indian satellite program has a plan for continued launching
of geostationary satellites with improved meteorological
satellite data capabilities. The recently launched METSAT has an IR channel at 8-km spatial resolution. During
2003–04, two other Indian satellites, INSAT-3A and INSAT-3D, will also be launched and will have IR resolutions of 8 and 4 km, respectively. The geostationary
satellite METEOSAT-5 located at 638E is expected to
continue operating over the Indian Ocean sector until
the end of year 2003. There is a plan for moving METEOSAT-6 or -7 to replace METEOSAT-5 over the Indian Ocean region. Rainfall estimates from these current
and future satellites will be useful for producing merged
daily rainfall data sets on regular latitude–longitude
grids. Some recent rainfall algorithms from IR data (Falkovich et al. 2000) remove the cirrus cloud influence
on estimated rain. These types of techniques have to be
tested for the Indian Ocean region. The IR-based rains
from geostationary satellites should be calibrated by obtaining relationships between microwave rain rates and
IR properties of cloud systems (Jobard and Desbois
1994; Adler et al. 1994). New knowledge of satellite
rain estimation from the European project EURAINSAT
will help in rainfall estimation on the geostationary scale
with METEOSAT Second Generation (MSG) Spinning
Enhanced Visible and Infrared Image (SEVIRI).
The India Meteorological Department has 556 surface
observatories (one in each district) where rainfall is
measured regularly. Of these, only around 35% of the
station data is received in real time digitally via computer. Apart from this, there are 3540 rain gauge stations
maintained by state governments, and another 206 agrometeorological stations take rainfall observations. They
are, however, kept in manuscript form and later transferred to computer. As the communication capabilities
have improved in last few years, the requirement to get
VOLUME 4
the rest of the data in real time in digital form should
become possible soon. When this huge amount of data
is used, the quality of the analyzed rain will improve
substantially. In order to obtain more real-time rain
gauge data, the reporting of daily (24-h collection) rain
gauge data (especially zero precipitation amounts) needs
to be standardized around the globe. The organized,
constructed 24-h rainfall database from subdaily reports
of precipitation (3-hourly, 6-hourly, etc.) coming regularly from good stations can contribute significantly as
an additional data source. This will require coordination
efforts at the national and international levels. Techniques developed to delineate areas of no-rain will be
useful where less rain gauge data are available (Ebert
and Weymouth 1999). Useful data are likely to come
from the international Global Precipitation Measurement (GPM) mission and the Indian–French satellite
Megha-Tropiques, where the experience and knowledge
gained from the Tropical Rainfall Measuring Mission
(TRMM) and SSM/I Defense Meteorological Satellite
Program (DMSP) will be very useful.
Acknowledgments. We are grateful to all the authors
and creators of the two datasets from the GPCP and
CMAP projects used in this study for providing the
monthly total values through ftp. The Satellite Meteorology division of the India Meteorological Department
provided satellite rainfall estimates from INSAT in real
time. We are grateful to Dr. Rajeswar Rao of the SATMET division, IMD for providing satellite rainfall data
from the archives for the few missing days. We are
thankful to Mr. Brian Mackey for many useful suggestions. The work of Prof. T. N. Krishnamurti was partially
supported by NASA TRMM Grant NAG5 9662.
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