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Estimation of Precipitation over India andAssociated Oceanic Regions by Combined Use of Gauge andMulti-satellite Sensor Observations at Fine Scale
Anoop MISHRA1*, Rakesh GAIROLA2 and Akiyo YATAGAI3
1

Divecha Center for Climate Change, Indian Institute of Science, Bangalore, India
2
Space Applications Centre, Indian Space Research Organization, India
3
Faculty of Life and Environmental Science, University of Tsukuba, Japan*e-mail:anoopmishra_1@yahoo.co.in

Abstract
The present study focuses on estimating rainfall (daily accumulated) using gauge and satellite observations over land and ocean regions of South Asia (30°S-50°N, 40°E-120°E) at a 0.25° × 0.25° spatial resolution
for the period 2007-2010 (four years). The study utilizes observations from rain gauges, the Special Sensor
Microwave/Imager (SSM/I) onboard the Defense Meteorological Satellite Program (DMSP), Precipitation
Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) and geo-stationary satellite Meteosat
from Eumetsat. The present study makes use of rainfall estimates by synergistic use of multi-satellite sensors
using Meteosat Infrared and Water Vapor absorption channels and PR observations (Mishra et al., 2009b,
2010) and SSM/I-derived microwave estimates using a regional scattering index developed by Mishra et al.,(2009a). The pixels over the land portion of the study area are filled with available rain gauge observations
over the southern part (around 14°N and 78°E) of the study area, which has a dense network of Automatic
Weather Station (AWS) rain gauges operated by the Indian Space Research Organization (ISRO). Pixels over
the other part of the study area are filled with available microwave observations first, but if microwave
observations are unavailable then these pixels are filled with microwave-calibrated infrared observations overthe land and oceanic regions of the study area. The precipitation estimates from the present approach are validated against rain gauge observations and other available standard rainfall products like TRMM-3B42V6 andGlobal Precipitation Climatology Project (GPCP) version 2.1. The validation results show that the present
approach of precipitation estimation is able to estimate rainfall with high accuracy.
Key words: precipitation, rain gauge, remote sensing, satellite, sensor

1. Introduction
Rainfall is a most unusual discontinuous atmosphericphenomena due to its high spatial and temporal variability. Accurate rainfall forecasts are essential for agricultural purposes. Furthermore, accurate assessment of
rainfall is required in order for the total water budget ofearth-atmosphere systems to be analyzed. The chiefsource of rainfall over South Asia is the monsoon, so arobust rainfall algorithm during pre-monsoon, monsoon
and post-monsoon periods is required.
Over South Asia and its adjoining oceans, information from reliable ground-based radars and a dense network of rain gauges is available for limited areas. For
overall synoptic applications, satellite-based estimates
are highly desired. Standard estimations based on Infra
Red (IR) measurements from satellites provide hightemporal sampling. These estimates have large errors
Global Environmental Research
15/2011: 131-138
printed in Japanbecause IR radiances from cloud tops have only anindirect and weak relationship with surface rainfall. The
most common Geostationary Operational Environment
Satellite (GOES) Precipitation Index (GPI) technique(Arkin & Meisner, 1987), based on a simple infraredbrightness temperature (IR-TB) threshold algorithm, hasbeen in operation to produce tropical and subtropicalprecipitation products on large temporal and spatial
scales for climatological studies.
Satellite microwave measurements, on the other hand,
have a strong physical relationship with rain and otherhydrometeors but are at a disadvantage on temporal andspatial scales because of coarser spatial resolution, andlower orbiting satellites provide less coverage.
The development of hybrid techniques using thestrengths of both microwave (MW) and IR, based on
proper inter-calibration over the common areas of overlap is, therefore, highly desirable. Adler et al. (1994)©2011 AIRIES132

A. MISHRA et al.

noted that opportunities exist to improve precipitation
estimates by combining two types of data so that thestrengths of each individual technique are maintained andtheir weaknesses are compensated for. Over the last few
years, a number of groups have embarked on development of so-called hybrid techniques through synergistic
use of multi-satellite sensor observations (Jobard &Desbois, 1994, Mishra et al., 2009a, 2010).
The present study focuses on estimating the rainfall
over South Asia by combining rain gauge and merged
satellite observations. The present technique’s rainfall
estimates were validated using Automatic Weather
Station (AWS) rain gauges and other available rainfall
products like Global Precipitation Climatology Project(GPCP) and TRMM-3B42.

2. Data Sources
The primary data used for this study were (1) infrared
and water vapor observations from geo-stationary satellites and (2) microwave observations from the TropicalRainfall Measuring Mission (TRMM) and the Defense
Meteorological Satellite Program (DMSP), which aresatellites in low earth orbits. The conventional data wereobtained from AWS for validation purposes. Intercomparison of the estimates has been performed using
available standard products like GPCP and TRMM3B42V6.
2.1 Meteosat and TRMM satellite data
Meteosat is a geostationary earth observation satellite
launched by the European Space Agency and now operated by the European Organization for the Exploration of
Meteorological Satellites. It provides thermal infrared(TIR) and water vapor (WV) images at half-hourlyintervals, with a spatial resolution of 4 km. In the present
study, we utilized TIR and WV data from Meteosat-7.
The TRMM, launched in late November 1997 into anear-circular orbit, with 35° inclination from the equatorial plane, provides data using passive instruments in the
optical and microwave spectra. It also carries a
Precipitation Radar (PR). We used the surface rainfall
data of Version-6 from Precipitation Radar (2A25)(Iguchi et al., 2000), along with Meteosat IR and WV for
development of the algorithm, and a standard merged IR
and MW data product called 3B42 (Huffman et al., 2007)
for the intercomparison.
2.2 Automatic Weather Station rain gauge data
AWS designed by the Indian Space Research
Organization (ISRO) is a very compact, modular, rugged,
powerful, low-cost system housed in a portable,
self-contained package. Among many other sensors, it
has a tipping bucket rain gauge with unlimited rainmeasuring capacity and an accuracy better than 1 mm.
The data are relayed via satellite and are available at thewebsite www.mosdac.gov.in. For the development andvalidation of the present algorithm, AWS rain gauge data
during 2007, 2008, 2009 and 2010 were used. The AWS
distribution over India is shown in Fig. 1.
2.3 GPCP Monthly data
The GPCP is an international project of the World
Climate Research Programme Global Energy and Water
Cycle Experiment, devoted to producing community
analyses of global precipitation. In the present study,
Version 2 GPCP monthly data (Adler et al., 2003) areused for a qualitative and quantitative comparison withthe present technique.

Fig. 1 ISRO AWS rain gauge distribution over the study area. Stations usedin the algorithm development were located within the black boundedbox.

Estimation of Precipitation over India and Associated Oceanic Regions by Combined Use of Gauge

133

2.4 SSM/I data
The Special Sensor Microwave/Imager (SSM/I)
instrument was developed as part of the DMSP and waslaunched aboard the Air Force Block 5D polar- orbiting
satellite on June 19, 1987. Built by Hughes Aircraft
Company, the sensor uses four frequencies: 19.35,
22.235, 37.0, and 85.5 GHz. All frequencies except thewater vapor absorption channel at 22.235 GHz are dual
polarized. The resolution depends on the frequency andranges from approximately 50 km for the 19 GHz
channel to 15 km for the 85.5 GHz channel. For thepresent study the SSM/I (F13) data were used for theyears 2007, 2008, 2009 and 2010.

3. Methodology
In the present study, rainfall over the study area wasestimated by combining the rain gauge and satellite
observations. The southern part (around 14°N and 78°E)
of the study area has a dense network of ISRO AWS rain
gauges (shown by the bounded box in Fig. 1). The density of the gauges is such that at least 2-6 gauges fallwithin each 0.25° × 0.25° box over most of the region.
From this, spatially averaged rainfall estimates wereconstructed using a simple spatial averaging technique. If
the number of gauges was less than 2 in any 0.25° × 0.25°
box, then the pixels within the box were calibrated using
weighted averaging by making use of the meteosat
infrared brightness temperature, based on match-ups
between the rain from the rain gauge and the meteosatbrightness temperature. Thus, the rainfall over the southern part of the land portion of the study area was estimated using rain gauge observations.
Rainfall over the remaining part of the study area wasestimated with SSM/I observations, using the regional
scattering index technique (Mishra et al., 2009a) developed separately for the land and oceanic part of the study
area. The technique is described below:
The procedure consists of two steps. In the first step, a
region specific ‘scattering index’ (SI) is developed using
a combination of the 19, 22 and 85 GHz channels separately for both land and oceanic regions. The second stepestablishes a new relationship between the scatteringindex and the rain rate using the TRMM PR data, mainly
following Ferraro and Marks (1995).
For the development of the scattering index, thefollowing relationship among 19, 22 and 85 GHz was
established under non rainy conditions:
F=A+B×Tv(19)+C×Tv(22)+D×(Tv(22))2

Fig. 2(a) Relationship between the scattering index from the
SSM/I and rainfall from PR for the land portion of thestudy area. Line drawn over the scatters indicates the lineof best fit.

Fig. 2(b) Same as Fig. 2a, but for the oceanic region.
Line drawn over the scatters indicates the line of best fit.

(1)

where F is the brightness temperature of the 85 GHz
channel and Tv(f) is the vertically polarized brightness
temperature at frequency f. For land regions, the coefficients are A = 448.6809, B = –1.5456, C = –0.6020, and
D = 0.0055. For oceanic regions, the coefficients are
A = –362.4467, B = 1.1379, C = 3.5247, and D = –0.0078.
After A, B, C and D are calculated, F is obtained, and thescattering index for the 85 GHz channel is defined as

Fig. 2(c) Relationship between Meteosat brightness temperatureand PR-rain rates.

134

SI (85) =F–Tv(85)A. MISHRA et al.

(2)

for the entire database (both raining and non-raining).
From this step onwards, the index, SI, can be used toseparate the scattering and non-scattering signals for agiven set of independent data.
Now the SI has been calibrated with Precipitation
Radar measurements. Specifically the following two
relationships (Figs. 2a and 2b) were found to work bestfor the land and oceanic regions, respectively (Mishra
et al., 2009a).
For land :(3)
RR (mm/h) = .0268×(SI)1.5978
For ocean:
RR (mm/h) = .0118×(SI)1.4985(4)

where RR is the rain rate in millimeters/hour.
The above two equations over land and ocean are
applied to get the rainfall using the scattering index from
SSM/I observations. Rainfall over coastal region is estimated by using Equation 2.
If the rain gauge and microwave observations are
missing then the gap (both temporal and spatial) over the
study area is filled in with microwave calibrated infraredobservations obtained through synergistic use of multisatellite sensor observations (Mishra et al., 2010). The
scheme is as follows:
The procedure begins with a cloud classification
scheme following Roca et al. (2002) from the Meteosat
IR and WV channels to identify thin cirrus, deep convective and very deep convective clouds over each
0.25° × 0.25° grid box. Subsequently, calibration of IR
brightness temperatures (IR-TBs) is performed (Fig. 2c)using PR rainfall within a 15 minute difference, in which
the autocovariance function of rainfall decreases toapproximately 0.9. Since TIR and PR measurements
have nearly identical spatial resolution (4~5 km), nonuniformity is not considered on a finer scale. Finally therainfall rates are computed based on the nonlinear powerlaw relation between the collocated and near simultaneous IR-TBs and PR rainfall rates in each 0.25° × 0.25°
grid box. After we applied both linear and non-linear
regression between the TB and rainfall rate, we foundthat the following power-law regression equation best
explains the relationship between the two variables(Mishra et al., 2010):
R=16.6614×exp(-(TB-204.57)/16.52688)

red observations. By utilizing the above technique, wecreated rainfall data sets for the period 2007 to 2010.
The usefulness of the technique can be examined byapplying it to a recent cyclonic event. Only one casestudy is presented below for brevity.
For comparing the daily accumulated rainfall over a
0.25° × 0.25° box grid of the study area according to thepresent scheme with that from the TRMM-3B42V6, wehave selected the case of a recent tropical cyclone named
Laila, which developed on May 17, 2010 over the Bay of
Bengal.
Figures 3a and 3b show the rainfall associated withthe landfall of Tropical Cyclone Laila over the Andhra
Pradesh coast on May 20, 2010, as observed by the pre-Fig. 3(a) 24-hour accumulated rainfall over a 0.25° × 0.25°
grid using the present technique on May 20, 2010.

(5)

In this way, the rainfall over vacant areas (where rain
gauge and microwave observations are absent) is estimated by applying the above equation using Meteosat
data.

4. Results and Discussion
The present technique has been developed by merging
rain gauge, microwave and microwave-calibrated infra-

Fig. 3(b) 24-hour accumulated rainfall over a 0.25° × 0.25°
grid using TRMM-3B42V6 on May 20, 2010.

Estimation of Precipitation over India and Associated Oceanic Regions by Combined Use of Gauge

sent technique and TRMM-3B42, respectively. From theabove figures, one may confirm that the present scheme
gives a reasonable depiction of the pattern and the intensity of the rain and it consistently matches those from
TRMM-3B42V6 observations.
Further, for a quantitative assessment of the present
scheme, we considered a total of 61,064 temporally andspatially collocated data points of daily accumulated
rainfall during (1-5 July, 11-19 August and 12-14 December 2008; 12-17 September and 24-26 November 2009;
and 17-21 May and 25-28 June 2010) from present
technique and TRMM- 3B42V6 over the study area, witha grid of 0.25° × 0.25°.
Figure 4 shows the scatter plot and Table 1 shows theassociated statistics between the two rainfall estimates.
Rainfall obtained from the present technique and thatfrom TRMM-3B42 V6 have a correlation coefficient of
0.86, with a root mean square error of 15.28, bias of 1.12,
probability of detection (POD) of 0.72, false alarm ratio(FAR) of 0.25, and a skill score of 0.23. A description of
FAR, POD and the skill score is given by Ebert &Manton (1998). From Fig. 4, we note a reasonable
number of high rainfall points under the present scheme
which are underestimated by the TRMM-3B42V6
observations. This may be due to the fact that TRMM-

135

3B42V6 tends to underestimate orographic rain (Rahman
et al., 2009), which may be depicted well by the present
technique.
Further, in order to evaluate the performance of thepresent scheme we have compared it with GPCP observations. Figures 5a and 5b show a time series of the daily
averaged monthly rainfall over central India (20-25°N
and 76-82°E) with a 2.5° × 2.5° grid for the years 2007
and 2008, respectively, from the present scheme and
GPCP observations. Both schemes are able to depict
seasonal rainfall variation. They are able to detect theactive and break cycle of the monsoon over central India
during the southwest monsoon season. Both estimatesconsistently match each other, with the exception of afew high rainfall values during the monsoon season of2007 from the present technique. This may again be dueto the inability of the GPCP to capture high rainfall
values associated with orography during the monsoon
period (Rahman et al., 2009). Figure 6 shows the scatterplot of the rainfall from the present technique and thosefrom the GPCP observations over the study area, with a
2.5° × 2.5° grid and Table 2 shows the associated statistics. For this purpose we considered 5,024 data points
during January, June and September 2007; July andOctober 2008; and November and December 2009. It is

Table 1 Statistics comparing rainfall from present
technique and that from TRMM-3B42V6.
No. of data points
Correlation coefficients
Root mean square error (mm)Bias (mm)
TRMM-3B42 mean (mm)Present scheme mean (mm)Probability of detection (POD)
False alarm ratio (FAR)
Heidke skill score

61,064
0.86
15.28
1.12
10.68
11.80
0.72
0.25
0.23

Fig. 4 Scatter plot between the TRMM-3B42V6 and present
scheme daily rainfall over a 0.25° × 0.25° grid of thestudy area, i.e., 30°S-50°N, 40°E-120°E.

Fig. 5(a) Time series plot of daily average monthly rainfall over theCentral Indian region (20-25° N and 76-82°E) over a 2.5° ×
2.5° grid from the present scheme and GPCP data for theyear 2007 over the study area, i.e., 30°S-50°N,
40°E-120°E.

Fig. 5(b) Time series plot of daily average monthly rainfall over theCentral Indian region (20-25° N and 76-82°E) over a 2.5° ×
2.5° grid from the present scheme and GPCP data for theyear 2008 over the study area, i.e., 30°S-50°N,
40°E-120°E.

136

A. MISHRA et al.

clear from the observations that rainfall from the present
technique and that from the GPCP have a correlation
coefficient of 0.89, root mean square error of 73.67, biasof 22.82, probability of detection (POD) of 0.78, false
alarm ratio (FAR) of 0.19, and a skill score of 0.17. Againa few points with underestimated values of orographic
rainfall from the GPCP are observed here. The GPCP
product fails to show the observed high rainfall over thewest coast of India, the northeast and Himalaya foothills.
High rainfall over these regions, particularly over theWestern Ghats and the Himalayan foothills is attributed
to orography. These regions of high rainfall values areseen in the PR and SSM/I observations (Xie et al., 2006)
and hence are observed under the present scheme also.
From the statistics, it is clear that the present technique isable to retrieve rainfall with a very good accuracy.
Finally, the daily accumulated rainfall from the present scheme was validated against rain gauge observations over the India land region with a 0.25° × 0.25° grid.
For this purpose we considered 1,371 spatially and temporally collocated data points from the present scheme
and rain gauge observations during 17-25 June and 12-17
September 2007; 16-21 July, 7-11 August and 19-21

Fig. 6 Scatter plot between the monthly rainfall of GPCP andpresent scheme over a 2.5° × 2.5° grid of the study.
Line drawn over scatters represents the line of best fit.

Table 2 Statistics comparing rainfall from the present
technique and that from GPCP.
No. of data points
Correlation coefficient
Root mean square error (mm)Bias (mm)
GPCP mean (mm)Present scheme mean (mm)Probability of detection (POD)
False alarm ratio (FAR)
Heidke skill score

5,024
0.89
73.67
22.82
151.43174.25
0.78
0.19
0.17

November 2008; and 1-5 March and 18-24 May 2009.
Figure 7 shows the scatter plot and Table 3 presents theassociated statistics. The two estimates in Fig. 7 havea correlation coefficient (R) of 0.77, rms error of
27.14 mm/day, bias of 2.71 mm/day, POD of 0.83, FAR
of 0.34, and skill score of 0.29.

5. Summary and conclusions
The present study describes the development of arainfall product for the period 2007-2010 over South
Asia (30°S-50°N, 40°E-120°E) at a 0.25° × 0.25° spatial
resolution (daily accumulated). The rain rates have been
derived by merging rain gauge and multi-sensor satellite
observations. Validation and statistical analyses havebeen performed using ground observations and othersatellite rainfall products. Validation with rain gauges
and comparison with other satellite products show thatthe present approach of rainfall estimation is able to estimate the rainfall over South Asia with high accuracy.
There are several rainfall products available for research
purposes, e.g., water resources over Asia from therain-gauge-based Asian Precipitation-Highly Resolved

Fig. 7 Scatter plot between daily rainfall from rain gauge andpresent scheme over a 0.25° × 0.25° grid of the study.
Line drawn over scatters represents the line of best fit.

Table 3 Statistics comparing rainfall from present technique and that from rain gauge observations.
No. of data points
Correlation coefficient
Root mean square error (mm)Bias (mm)Rain gauge mean (mm)Present scheme mean (mm)Probability of detection (POD)
False Alarm Ratio (FAR)
Heidke Skill Score

1371
0.77
27.14
2.71
24.06
26.77
0.83
0.34
0.29

Estimation of Precipitation over India and Associated Oceanic Regions by Combined Use of Gauge

Observational Data Integration Towards Evaluation(APHRODITE) (Yatagai et al., 2009) and global rainfall
products like TRMM-3B42 V6, GPCPV2, Climate
Prediction Centre Morphing technique (CMORPH)(Joyce et al., 2004), and Global Satellite Mapping ofPrecipitation (GSMaP) (Kubota et al., 2007). The raingauge-based product does not provide information over
the oceanic regions, while the above-mentioned otherglobal rainfall products rely on rainfall estimation basedon proxy variables like Polarization Corrected Temperatures (PCT) (Spencer et al., 1989), Scattering Indices(SI) (Ferraro & Marks, 1995), etc., which vary with theregions and seasons and do not perform well over South
Asia (Mishra et al., 2009a); hence these rainfall products
may not perform well over South Asian regions. The
present rainfall product, based on rain-gauge and microwave observations using regional scattering indices andregion-specific microwave-calibrated infrared observations developed for the South Asia region, is able to givevery good estimates of rainfall over the land and oceanic
portion of South Asia for research purposes.

Acknowledgement
This research was supported by the Environment
Research and Technology Development Fund (A-0601)
of the Ministry of the Environment, Japan. We thank the
Ministry of the Environment of Japan for their funding
through the Eco-Frontier Fellowship program (A06018).
The TRMM-3B42V6, GPCP, SSM/I, AWS, and Meteosat
data used in the present study are also thankfully
acknowledged.
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Anoop Kumar MISHRA

Dr. Anoop Kumar MISHRA was born to Mrs Rani
Mishra and Mr Mahesh Prasad Mishra on
November 09, 1981 at Allahabad, Uttar Pradesh,
India. After completing his M.Sc. from
University of Allahabad in 2004, he joined Space
Applications Centre, Indian Space Research
Organization in 2006. He was awarded the PhD degree in Physics from
Gujrat University, India in 2011. He worked as postdoctoral fellow at
Research Institute for Humanity and Nature, Japan during June 2010 March 2011. At present, he is working on precipitation retrieval using
merged rain gauge and satellite observations at Centre for Atmospheric andOceanic Science, Indian Institute of Science, India. He has developed
rainfall retrieval techniques for the Indian land and oceanic regions using
infrared, and microwave observations for the coming missions like
INSAT-3D and Megha-Tropiques. He has worked on merged rainfall
estimation technique using synergy from infrared and microwave
observations.

Rakesh Moham GAIROLA
Dr. Rakesh Moham GAIROLA, has been working
with Space Applications Centre, Indian Space
Research Organisation (ISRO) for the Radiative
Transfer Modeling and Geophysical Parameter
Retrievals and applications. He is currently
involved in retrieval of rainfall from satellite
microwave and optical sensors from various space based platforms, like
Indo-French- Megha-Tropiques, TRMM and INSAT-3D etc. He hasdeveloped various operational algorithms for estimating rainfall at various
spatial and temporal scales for different application purposes. Currently heis also working as the Principal Investigator of an AO-project under NASAGlobal Precipitation Mission (GPM). He is also working as Associate
Project Director and ISRO-Principal Investigator for an INDO-French(SARAL-Project). His other research interests are, Remote Sensing and its
applications towards the global hydrologic cycle and Climate Change.

Akiyo YATAGAI
Dr. Akiyo YATAGAI is a climatologist and a
researcher at the Faculty of Life and Environmental Sciences, University of Tsukuba. She
received Doctor of Philosophy in 1996 from the
Graduate School of Geoscience, University ofTsukuba. She was a Researcher at the Earth
Observation Research Center, National Space Development Agency from1995 to 2001. She was an Assistant Professor at the Research Institute for
Humanity and Nature (RIHN), Kyoto from 2002 to 2011. She was a
Principal Investigator of the Asian Precipitation – Highly Resolved
Observational Data Integration Towards Evaluation of water resources(APHRODITE) project for 2006–2011, funded by the Global Environment
Research Fund, Ministry of the Environment, Japan.

(Received 27 May 2011, Accepted 7 October 2011)