Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

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Advances in Space Research 44 (2009) 815–823

Rainfall retrieval over Indian land and oceanic regionsfrom SSM/I microwave data

Anoop Mishra *, R.M. Gairola, A.K. Varma, Abhijit Sarkar, Vijay K. Agarwal

Oceanic Sciences Division, Meteorology and Oceanography Group, Space Applications Centre – ISRO, Ahmedabad 380 015, India

Received 9 December 2008; received in revised form 17 February 2009; accepted 14 May 2009

Abstract

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager(SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHzchannels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scatteringindices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) todevelop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit betweenthe rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfallusing the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The develop-ment of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. Thevalidation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro andMarks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithmis able to retrieve the rainfall with reasonably good accuracy.� 2009 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: Precipitation; Remote sensing; Microwave; Scattering index

1. Introduction

Rainfall influences the dynamics of the earth’s atmo-sphere. Studies show that about three-fourths of the energythat drives atmospheric circulation is from the latent heatreleased by precipitation (Sellers, 1965; Hartman, 1994).Despite its importance, precipitation is one of the most dif-ficult atmospheric parameters to retrieve because of itslarge variability in space and time. Many investigators havefocused upon getting better rainfall intensity information.Generally speaking, ground based weather stations thatprovide rainfall information are still insufficient. Meteoro-logical satellites provide viable alternative that can be usedto estimate the rainfall intensity.

Earlier research on the rainfall intensity estimation usedsatellite infrared or visible data. Despite the variety of vis-

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doi:10.1016/j.asr.2009.05.011

* Corresponding author. Tel.: +91 07926916041; fax: +91 07926916075.E-mail address: [email protected] (A. Mishra).

ible and/or infrared technique developed to retrieve rainfallinformation, they are based on only cloud top informationdue to inability of VIS/IR radiances to penetrate theclouds. Barret and Martin (1981) provide a useful summaryof these techniques. However, Rodgers (1981) noted thatthere is sometimes little difference between the visible andinfrared signatures of precipitating and non-precipitatingclouds, leading to a misclassification of rain areas. Whilstthis may not be noticeable in climatological data sets; onan instantaneous scale such effects can be very prominent.At passive microwave frequency it is the precipitation par-ticles themselves that are the main source of attenuation ofthe upwelling radiation. This makes the passive microwavetechnique physically more suitable for rainfall retrievalthan those based on visible/infrared radiation.

Two theories, the emission and the scattering theory arefrequently adopted to estimate rainfall intensity withmicrowave data (Janowiak et al., 1995). The former usesthe observed emitted radiations from atmospheric liquid

rved.

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816 A. Mishra et al. / Advances in Space Research 44 (2009) 815–823

particles to estimate the rainfall intensity. The observedradiation is sensitive to the surface emissivity, so it is onlyapplied to oceanic regions owing to the sea surface emissiv-ity being low and homogeneous, and it is believed that thistheory is more appropriate for rainfall estimation in areasbeneath stratified clouds or shallow convective clouds.The latter method may be employed to estimate the rainfallintensity by measuring the extinction of the microwaveradiation caused by the particles of liquid water or ice.The high and variable emissivity of the land surface greatlycomplicates the signal from the liquid hydrometeors. Theliquid hydrometeors could increase or decrease theobserved radiation depending upon the surface emissivity,which is a function of the rain that has fallen over a timescales of days. So rainfall retrievals over the land are moredifficult than the ocean retrievals due to the large and var-iable emissivity of the land surface. Specifically the highemissivity effectively masks the emission signature that isrelated directly to the liquid water contents in the atmo-sphere. Instead only the brightness temperature depressiondue to the scattering from the upper portion of the cloudsis readily observed. The scattering index must then be con-verted to the rainfall rate using statistical relation betweenthe ice aloft and the base of cloud.

A further complication that arises over land is the lackof the consistent background against which to comparethe brightness temperature depression. To alleviate thisproblem caused by the varying emissivity associated withchange in surface characteristics (e.g., surface wetness,snow cover, vegetation, etc.) as well as temperature vari-ability, a rain/no rain temperature depression, a thresholdis required. Early work by Wilheit et al. (1977, 1991) pre-sented rainfall algorithm that is purely emission based,using 19.35 GHz frequency. Liu et al. (1998) developedsuch emission and scattering indexes for global scale, butfor the monthly time scale. Coupling of emission and scat-tering signal was also attempted by Petty (1994a,b) in orderto overcome problem such as saturation of emission signalat lower frequency and high rain rates, and the low sensi-tivity of the scattering signal to low rain rates. Mohr andZipser (1996a) used microwave radiometry to identifyingmesoscale convective systems (MCS) from the SSM/I datausing PCT approach.

In the present study the basis for the rainfall retrievalover Indian land and oceanic regions comes from the workof Grody (1991), who developed a global scattering index(SI) at 85 GHz using SSM/I data. Further refinement oftechnique was described by Ferraro and Marks (1995).However, it is found that this scattering index is highly var-iable with regions and seasons as well as the platform. Inthe present study a regional scattering index is developedspecifically for the Indian land and oceanic regions(50�E–120�E and 20�S–40�N, Fig. 17) using SSM/I F-13data and then a relationship is established between rainfalland scattering index for the land and ocean separatelyusing TRMM (Tropical Rainfall Measuring Mission) PR(Precipitation Radar) observed rainfall. We also used the

global relationship between the rainfall and the scatteringindex developed by Ferraro and Marks (1995) to estimatethe rainfall. Validation of both regional as well as the glo-bal scheme is carried out with the ground observation totest the strength of this regional scheme. Intercomparisonwith 2A12 data of TRMM followed from Goddard Profil-ing (GPROF) algorithm (Kummerow et al. 1996) has beenperformed to check the applicability of the algorithm inview of the algorithm development for Indo-FrenchMegha-Tropiques mission to be launched from Indiasometime in 2009.

2. Data used

2.1. SSM/I data

The SSM/I instrument was developed as part of theDefense Meteorological Satellite Program (DMSP) andwas flown aboard the Air Force Block 5D polar-orbitingsatellite on June 19, 1987. Built by Hughes Aircraft Com-pany, the sensor consists of four frequencies at 19.35,22.235, 37.0 and 85.5 GHz. All frequencies except the watervapor absorption channel at 22.235 GHz are dual polar-ized. The resolution depending on frequency range fromapproximately 50 km at 19 GHz to 15 km at 85.5 GHzchannel. Spatial resolution (field of view) of the differentSSM/I channels as well as other instrumental parametersis given by Hollinger and Lo (1983).

For comparing the measurements at different frequen-cies, effect due to the different spatial resolutions are mini-mized by convolving all measurements to the 50 kmresolution, which is the resolution of the lowest frequencychannel. This enables one to investigate the spectral varia-tions without having to consider the effects of spatial inho-mogeneity on the different channel measurements. For thepresent study the SSM/I (F-13) data was downloaded fromftp site (ftp://ghrc.nsstc.nasa.gov/pub) for the years 2005,2006 and 2007.

2.2. Precipitation Radar (PR) data

The TRMM is a joint US–Japan Satellite Mission tomonitor the tropical and subtropical precipitation and toestimate associated released latent heat. TRMM satellitewas launched in late November 1997 into circular orbitapproximately 350 km altitude (now 420 km) and 35� incli-nation from the equatorial plane. The data from onboardTRMM has been utilized. The Precipitation Radar (PR),the first of its kind in space, is a 2-m active phased arrayRadar system operating at 13.8 GHz PR (Iguchi et al.,1998). The PR scans across a 215 km swath width with80 vertical range bins extending to 20 km above the earthellipsoid. The vertical and horizontal resolutions are250 m and 5 km � 5 km, respectively. The current study uti-lizes the latest version of data from the TRMM ScienceData and Information system (TSDIS) 2A25 algorithm.

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Cal

cula

ted

85G

Hz

Bri

ghtn

ess

tem

pera

ture

(F)

Observed 85GHz Brightness temperature

Fig. 1. Comparison between the derived F and observed 85-GHzvertically polarized measurements for the land portion.

A. Mishra et al. / Advances in Space Research 44 (2009) 815–823 817

The data for the years of 2005, 2006 and 2007 were used forthe present study.

2.3. TMI-2A12 data (TRMM microwave imager-2A12)

The 2A12 provides rainfall rates and the vertical struc-ture of hydrometeors and latent heating based upon thenine channels of the TRMM microwave imager (TMI).The algorithm is based upon a Bayesian approach thatbegins by establishing a large database of the potentialhydrometeor profiles and their computed brightness tem-peratures (Tb). This database is computed from the cloudresolving Models such as the Goddard Cumulus Ensemblemodel. Once the database is established, the retrievalsearches the database and in Bayes’s formulation, the prob-ability of a particular profile R, for a given Tb can beobtained (Kummerow et al., 1996). The selective data forthe years of 2005, 2006 and 2007 were downloaded forthe intercomparison of the present algorithm.

2.4. Automatic Weather Station (AWS) rain gauge data

The Indian Space Research Organization (ISRO)designed Automatic Weather Station (AWS) is as a verycompact, modular, rugged, powerful and low-cost systemand housed in a portable, self contained package. It hasa Tipping bucket rain gauge with a magnet and reed switchwith unlimited rain measuring capacity with accuracy ofless than 1 mm. AWS transmits its hourly meteorologicaldata in burst of 68 ms duration (at a data rate of 4.8 kbps).At present there are about 300 AWS operationally workingall over India. INSAT Data Collection Platform is used fordata acquisition. The AWS map is shown in Fig. 18. Forthe validation of the present algorithm the AWS rain gaugedata during July and September 2007 is used.

3. Methodology

Present study consists of two steps, in the first stepregion specific ‘scattering index’ is developed using combi-nation of the 19, 22 and 85 GHz channel, both over Indianland and oceanic regions separately. The second step estab-lishes a new relationship between the scattering index andthe rain rate using the TRMM PR data mainly followingFerraro and Marks (1995). Here it should be noted thatthe additional use of 37 GHz channel may result in a smal-ler error than the two channel algorithm (i.e., 19 and22 GHz). However, 37 GHz channel can exhibit the scat-tering effect and therefore was not used to estimate thenon-scattering contribution at 85 GHz. Since the 19 and22-GHz channels are relatively unaffected by scattering,these observations from SSM/I are used for estimation of85 GHz brightness temperatures during non-scatteringconditions.

For the development of the scattering index, the follow-ing form of relationship between 19, 22 and 85 GHz isestablished under non-rainy conditions.

F ¼ Aþ B � T vð19Þ þ C � T vð22Þ þ D � ðT vð22Þ � �2Þ ð1Þ

where F = 85 GHz channel brightness temperature. Tv(f) isvertically polarized brightness temperature in kelvin at fre-quency ‘f’.

The value of A, B, C and D were derived by assemblinga data set (4671 data points over the land and 3974 datapoints over the ocean) of SSM/I F-13 over Indian landand oceanic regions under non-rainy condition separately.For the land region the value of the coefficients were foundas follows:

A ¼ 448:6809;B ¼ �1:5456;C ¼ �0:6020;D ¼ 0:0055

with standard error of estimates = 1.3513. Similarly for theoceanic regions the values of coefficients were found asfollows:

A ¼ �362:4467;B ¼ 1:1379;C ¼ 3:5247;D ¼ �0:0078

with standard error of estimate = 1.8261. Once the value ofA, B, C and D are calculated, value of F is obtained andthen scattering index at 85 GHz channel is defined as

SIð85Þ ¼ F � T vð85Þ ð2Þ

for all the database (both raining and non-raining). Fornon-raining cases the value of the SI is suppose to be ide-ally zero. From this step onwards, the index SI could beused to separate the scattering and non-scattering signalsfor a given set of independent data. Fig. 1 shows the com-parisons between the derived F and observed 85-GHz ver-tically polarized measurements for the land portions.

Fig. 2 shows the comparisons between F (the estimated85 GHz brightness temperature) and observed at verticallypolarized measurements for the oceanic regions.

Now the SI has been calibrated with PrecipitationRadar measurements from Indian land and oceanicregions. For this calibration purpose 681 collocated datapoints over land and 792 over the oceans were used. Figs.3 and 4 show the scatter plot between the SSM/I scattering

Fig. 2. Same as Fig. 1 but for the oceanic portion. Fig. 4. Same as Fig. 3 but for the oceanic portion.


index and the PR rainfall for the land and oceanic regions,respectively. An attempt has been made to estimate rainfallfrom SI (Ferraro and Li, 2002). Specifically following tworelations were found to work best for the land and oceanicregions separately over Indian land and adjoining oceans,respectively.

For the land application:

RR ðmm=hÞ ¼ 0:0268 � ðSIÞ � �1:5978 ð3Þ

with standard error of estimate 1.1375For the ocean application:

RR ðmm=hÞ ¼ 0:0118 � ðSIÞ � �1:4985 ð4Þ

with standard error of estimate 0.9097; where RR is rainrate in mm/h.

The above two equations over land and ocean areapplied to get the rainfall using scattering index. Further

Fig. 3. Relationship between the scattering index and the Radar rain ratefor the land portion.

we have also used the following global relationship devel-oped by Ferraro and Marks (1995) to estimate the rainfall.

RR ðmm=hÞ ¼ 0:00513 � ðSIÞ � �1:9468 ð5Þ

4. Results and discussions

As is mentioned earlier, the present technique was testedfor the Indian land and oceanic region during monsoon,pre monsoon and post monsoon season of 2005, 2006and 2007 and the performance of the algorithm is validatedwith the rain-gauge measurements and intercompared with2A12 rainfall. Various case studies were carried out and

Fig. 5. Rainfall plot from present technique on 9th September 2005 1240UTC over oceanic region. Rainfall rates are in mm/h.

Fig. 6. Rainfall plot from GPROF technique on 9th September 2005 1250UTC over oceanic region. Rainfall rates are in mm/h.

Fig. 8. Rainfall plot from GPROF technique on 14th August 2006 0235UTC over land. Rainfall rates are in mm/h.


only some of the results will be discussed in the presentpaper for brevity.

The first case study is taken up on 9th September 2005when there were two localized cloud systems over oceanat the locations 13�N 84�E and 16�N 84�E. Figs. 5 and 6show the rainfall plots of these systems both by presenttechnique and the 2A12 (GPROF) rain product, respec-tively. From the figure it is evident that the rain rate fromthe present technique is approximately matching with thosefrom the GPROF technique, i.e., ranging from the 8 to10 mm. Here it may be noted that there is a time difference

Fig. 7. Rainfall plot from present technique on 14th August 2006 0235UTC over land. Rainfall rates are in mm/h.

of about 10 min between the SSMI and the TRMM obser-vation in this case, which may probably be the reason forthe slight noticeable difference and minute shifting of therain patterns.

Our second case study comprises of two intense convec-tive systems over land one at 23�N 77�E and another at29�N 80�E on 14th August 2006, for which, Figs. 7 and 8show the rainfall plots both from the present and GPROFtechnique, respectively. In both the figures rainfall amountas well as the location of the system match quite well. Rain

Fig. 9. Rainfall plot from present technique on 4th June 2006 0240 UTCover ocean. Rainfall rates are in mm/h.

Fig. 10. Rainfall plot from GPROF technique on 4th June 2006 0250UTC over ocean. Rainfall rates are in mm/h.

Fig. 12. Histogram of rainfall rates from GPROF algorithm and presenttechnique both over land. Rainfall ranges are in mm/h.


rate at the centre of the system at 23�N 77�E is about22 mm/h from the 2A12 whereas present method showsthis amount to be about 20 mm/h. While outside the centreof the system rain rate is varying from 4 to 12 mm/h withboth the technique. The rain rate of the system located at29�N 80�E is in the range 6–14 mm/h both from the presenttechnique as well as the 2A12 data. In this case also, thetime difference between the TRMM pass and the SSM/Ipass over the system is about 10 min.

We made a third case study of an intense cyclone Gonuover the Arabian Sea on 4th June 2007. From Figs. 9 and10 one can see the intense cyclonic storm at 64�E 15�N.Present technique estimates rainfall rate as 12 mm at thecentre of the system while this amount is varying from 3to 10 mm outside the centre at 0240 UTC. Furthermore,the rainfall structure from the 2A12 is slightly differentfrom that of present technique. It got split in to two sepa-

Fig. 11. Histogram of rainfall rates from GPROF algorithm and presenttechnique both over land. Rainfall ranges are in mm/h.

rate subsystems each having the rainfall rate at the centreas 12 mm/h while outside the centre the rainfall rate variesfrom 3 to 10 mm at 0240 UTC. This may be due to the12 min time difference between both the observations. Itwas further confirmed from the TRMM based 3B42 prod-ucts (Adler et al., 2000) of the system that it got split some-times between the 1200 UTC and 1500 UTC.

Figs. 11 and 12 show the histogram of the rainfall fromthe 2A12 and the present technique for the 24th September2007 and 19th July 2007 over land and ocean, respectively.Black bar represents rainfall from the 2A12 techniquewhile grey bar represents the rainfall from the presentone. It is clear that range wise estimates from both tech-nique are quite close to each other, except the low rainfall

Fig. 13. Scatter plot between the GPROF rain rate and rain rate from thepresent technique over ocean. Rain rates are in mm/h.

Fig. 14. Same as Fig. 13 but for the land.

Fig. 16. Scatter plot between the rain-gauge rainfall and the rainfall fromthe Ferraro global scheme.

Table 1Statistical comparison of rainfall from the present and the Ferraro schemewith the rain-gauge rainfall.

Technique Present technique Ferraro technique

No. of data points 192 192Correlation coefficients (%) 0.68 0.65Root mean square error (mm) 9.35 11.14Bias (mm) �3.64 �6.85


range below 4 mm/h for the land case where the frequencyof occurrence is around 180 from 2A12 and 120 from thepresent technique.

Figs. 13 and 14 show the statistical comparison betweenthe rainfall from 2A12 and the present technique for oceanand land region, respectively. The correlation coefficientand the root mean square error between the rainfall ratesfrom present and the GPROF technique are 0.8344 and1.06498 mm/h, respectively, for the land region while forthe oceanic region the correlation coefficient and root meansquare error are 0.80159 and 1.11887 mm/h, respectively.The better error statistics over land as compared to the

Fig. 15. Scatter plot between the rain-gauge rainfall and the rainfall fromthe present technique.

ocean leads us to conclude that the present algorithm is rel-atively better for the land compared to the oceanic regions.

Fig. 17. Spatial domain of the present study.


This is highly desired, as the various types of complex ter-rains exist in Indian subcontinent. In addition, the south-west and north-east monsoon are the main sources of waterthrough rainfall for various real life operational applicationslike agriculture, hydrology and heavy rain/flood conditions.

Fig. 15 shows the statistical comparison between therain-gauge rainfall and the rainfall from the present tech-nique. Fig. 16 is similar to Fig. 15 but here rainfall is fromthe Ferraro scheme. And Table 1 shows the statistical com-parison between the rain-gauge rainfall and the rainfallfrom the present technique and the rainfall from the Ferr-aro scheme. From the comparison it is evident that presentscheme is able to retrieve the rainfall over the Indian land

Fig. 18. AWS rain gauge dis

and oceanic regions with better accuracy than that by usingthe Ferraro scheme.

5. Conclusions

The present paper describes the rainfall estimation usinga newly established scattering index (SI) formulation fromthe SSM/I F-13 microwave data first time for the largeIndian land and oceanic regions. The rain rate is derivedfrom the power law relationship between SSMI derivedscattering index and the PR rain rate. The validation withthe ground observation (AWS rain-gauges) shows that thepresent technique is able to retrieve the rainfall with better

tribution over the India.


accuracy than that from Ferraro scheme which is based ona single set of coefficients for its applicability for globalrainfall retrieval. Intercomparison with the GPROF(2A12 rain product) algorithm shows that present algo-rithm is able to retrieve the rainfall with good accuracyboth qualitatively as well as quantitatively which is highlydesired particularly over the south-west and north-eastmonsoon dominated regions of Indian subcontinent. Ingeneral the oceanic rainfall algorithms (due to the homoge-neous background of ocean) perform better than algo-rithms over land (due to highly varying emissivity of theland). However, in the present case the region specificempirical algorithm performs better over the land thanthe oceanic regions which was confirmed by the compari-son of the present scheme with the GPROF algorithm.Despite the different system configurations and the geome-try of PR and SSM/I observations, the rainfall from pres-ent technique is quite close to the standard operationalproducts from GPROF algorithm which is not as simplein operation as the present one. The present study alsohelps in concluding that such empirical rainfall retrievalshould be developed regionally based on the different cli-matic zones of the globe separately. More comprehensiveglobal rainfall retrieval along with validation is needed tothe efficacy of the methodology; that is planned for the nextupdate of the present method.

Acknowledgements

We thank Director Space Applications Centre, forencouragements. NASA – Goddard Earth Sciences Dataand Information Services Centre Distributed Active Ar-chive Centre (GES DISC DAAC) is also acknowledgedfor providing SSM/I and TRMM datasets.

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Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

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