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Advances in Space Research 48 (2011) 49–55

Improved rainfall estimation over the Indian region usingsatellite infrared technique

Anoop Kumar Mishra a,⇑, R.M. Gairola b, A.K. Varma b, Vijay K. Agarwal b

a Research Institute for Humanity and Nature, Kyoto, Japanb Oceanic Sciences Division, Meteorology and Oceanography Group, Space Applications Centre – ISRO, Ahmedabad 380 015, India

Received 17 June 2009; received in revised form 20 January 2011; accepted 23 February 2011Available online 1 March 2011

Abstract

The GOES Precipitation Index (GPI) technique (Arkin, 1979) for rainfall estimation has been in operation for the last three decades.However, its applications are limited to the larger temporal and spatial scales. The present study focuses on the augmentation on GPItechnique by incorporating a moisture factor for the environmental correction developed by Vicente et al. (1998). It consists of two steps;in the first step the GPI technique is applied to the Kalpana-IR data for rainfall estimation over the Indian land and oceanic region andin the second step an environmental moisture correction factor is applied to the GPI-based rainfall to estimate the final rainfall. Detailedvalidation with rain gauges and comparison with Tropical Rainfall Measuring Mission (TRMM) merged data product (3B42) are per-formed and it is found that the present technique is able to estimate the rainfall with better accuracy than the GPI technique over highertemporal and spatial domains for many operational applications in and around the Indian regions using Indian geostationary satellitedata. Further comparison with the Doppler Weather Radar shows that the present technique is able to retrieve the rainfall with reason-ably good accuracy.� 2011 COSPAR. Published by Elsevier Ltd. All rights reserved.

Keywords: Remote sensing; Microwave; Infrared; Precipitation; Rain gauge

1. Introduction and background

Precipitation is one of the most variable atmosphericparameters in space and time that has a direct impact onhuman life. While rain brings fresh water that supports lifeon earth, extreme rain events can cause destruction andmisery due to flash floods. The uneven distribution of raingauges and weather radars as well as the lack of sufficientrainfall measurements over the oceans has significantly lim-ited the use of such measurements for many process stud-ies. In developing countries, where precipitation iscritically important to everyday life, an operationallyworkable rainfall estimation technique is indispensable.

0273-1177/$36.00 � 2011 COSPAR. Published by Elsevier Ltd. All rights rese

doi:10.1016/j.asr.2011.02.016

⇑ Corresponding author. Address: Research Institute for Humanity andNature, 457-4 Kamigamo-Motoyama, Kita-ku, Kyoto 603-8047, Japan.Tel.: +81 75 707 2207; fax: +81 75 707 2506.

E-mail addresses: anoopmishra_1@yahoo.co.in, anoop@chikyu.ac.jp(A.K. Mishra).

Development of satellite remote sensing techniques pro-vides a unique opportunity for better observation of pre-cipitation for regions where ground measurements arelimited.

There are a number of techniques for rainfall estimatione.g. Climate Prediction Center MORPHing technique(CMORPH; Joyce et al., 2004), TRMM-merged products(Huffman et al., 2007), Precipitation Estimation fromRemotely Sensed Information using Artificial NeuralNetwork (PERSIANN) (Hsu et al., 1997) to name a few.Scofield (1987) developed the National EnvironmentalSatellite Data and Information Service (NESDIS) opera-tional convective precipitation technique. Kuligowski(2002) developed a rainfall algorithm technique known asSelf-Calibrate Multi-variate Precipitation Retrieval(SCaMPR) for shorter spatial and temporal scales basedon calibration of infrared brightness temperature (IR-TB’s)with Special Sensor Microwave Imager (SSM/I) microwaverainfall. For large scale rainfall estimates one of the best

rved.

50 A.K. Mishra et al. / Advances in Space Research 48 (2011) 49–55

known IR based rainfall technique is the GOES Precipita-tion Index (GPI) technique (Arkin, 1979). This algorithm isused for estimation of rainfall at large spatial and temporalscales for various applications including climatic studies.However, whenever the GPI is applied to smaller spatialand temporal domains, the technique is merely a goodqualitative indicator of rainfall occurrences. Previous stud-ies have shown that rainfall is overestimated in dry condi-tions and underestimated in moist conditions probablybecause falling raindrops evaporate to some extent beforearriving at the surface under dry conditions and tend togrow in size under moist conditions (Vicente et al., 1998).Thus the water vapor content below the clouds in theatmosphere influences the rain retrieval from IR measure-ments based on cloud top temperature. In order to aug-ment the utility of the GPI technique from larger climaticscales to shorter scales for daily weather monitoring, wehave attempted to minimize these errors in the rain estima-tion subject to the presence/absence of water vapor in theunderlying atmosphere. Thus our new algorithm focuseson improving precipitation estimation using the GPI tech-nique with the incorporation of an environmental moisturecorrection factor through the availability of water vapor.The improved algorithm was then demonstrated by apply-ing it to operational rainfall monitoring using relativelyhigher spatial and temporal resolution over a vast area ofthe Indian subcontinent and adjoining oceans. We nameit the Modified GPI (MGPI) for brevity. The differencebetween our approach and the approach adopted by Vicen-te et al. (1998) is that we have applied the environmentalcorrection factor to the GPI rainfall estimates directlywhereas Vicente et al. (1998) applied this moisture factorto the rainfall derived from regression between the bright-ness temperature and the RADAR rainfall after using cer-tain tests for the filtering of non-rainy pixels. Rainestimations from the MGPI are then validated againstground observations and with independent rainfall prod-ucts from other satellites to demonstrate its utility in real-time operational applications within the desired latencyof the data to end users.

2. Data used

2.1. Kalpana data

Kalpana is a dedicated meteorological geostationarysatellite launched by Polar Satellite Launch Vehicle (PSLV)and operating since 24th September 2002. This geostation-ary satellite carries onboard a Very High Resolution Radi-ometer (VHRR) along with other instruments. This sensoroperates in three wavelength bands namely Visible (VIS),Thermal (TIR), and Watervapor (WV). In WV and TIRbands, spatial resolution is 8 km where as in VIS band itis 2 km. The spectral ranges of these bands are:

(a) TIR band: 10.5–12.5 lm(b) VIS band: 0.55–0.75 lm

(c) WV band: 5.7–7.1 lm

For the present study we used the Kalpana-IR data duringtwo consecutive years of 2006 and 2007. A detail of theKalpana satellite is given by Kaila et al. (2002).

2.2. TRMM-3B42 data

TRMM is a joint US–Japan Satellite Mission to moni-tor tropical and subtropical precipitation and to estimateassociated latent heating of the atmosphere. It waslaunched in late November 1997 into a circular orbit atapproximately 350 km altitude (now raised to 420 km)and at 35� inclination from the equatorial plane.

The TRMM-3B42 is a merged rain product derivedusing Geostationary IR data and the observations fromthe international constellations of the microwave satellites.We compare rainfall derived by our new MGPI techniquewith TRMM-3B42 rain products that cover the same per-iod as that of the Kalpana observations. The TRMM-3B42 products also provide a good qualitative consistencycheck of rainfall patterns. In addition the MGPI was vali-dated against rain gauges.

2.3. Automatic Weather Station (AWS) rain gauge data

AWS is as a very compact, modular, rugged and low-cost system housed in a portable, self-contained package.Among many other sensors it has a Tipping bucket raingauge with a magnet and reed switch with unlimited rainmeasuring capacity with accuracy better than 1 mm.AWS transmits its hourly meteorological data in burstsof 68 ms duration (at a data rate of 4.8 kbps). At presentthere are about 300 AWS operationally deployed all overIndia (Vashistha et al., TECO-2000). The INSAT DataCollection Platform is used for data acquisition. For thevalidation of the present algorithm the AWS rain gaugedata during the Southwest monsoon period of 2007 areused. The point measurements by AWS are averagedwithin 1� � 1� grids similar to MGPI observations. Dueto high spatial variability of precipitation (Varma andLiu, 2006; Mishra et al., 2010), with few observations ineach grids the averaged values may often have large uncer-tainty. This is the limitation of using the rain gauge datafor the validation over 1� � 1� grid box. Distribution ofAWS is shown in Fig. 1.

2.4. Doppler Weather Radar (DWR) data

Doppler Weather Radar (DWR) is an indigenouslydeveloped S-band radar over India at Sriharikota at 13�600N and 80� 230E location. It is capable of monitoringclouds, precipitation systems and winds over large areasof more than 400 km from the radar site. DWR has thecapability to continuously track and predict fast evolvingweather systems such as thunderstorms, cyclones andcloudbursts (http://www.imd.gov.in/section/dwr/dynamic/

Fig. 1. ISRO AWS rain gauge network and DWR location. The open dotsrepresent the AWS rain gauge locations while the blue ellipse shows thelocation of the DWR location. (For interpretation of the references tocolour in this figure legend, the reader is referred to the web version of thisarticle.)

A.K. Mishra et al. / Advances in Space Research 48 (2011) 49–55 51

radarfaq.pdf). Enough care has been taken for the heightproblem by applying the range corrections. The RADARreflectivity factor-z from the DWR is taken as the baseproduct for the present study. The DWR rain values havebeen derived using a Z–R relationship developed duringthe campaign using disdrometer data at the site. Rainfallmeasured using DWR depends upon an empirical exponen-tial relationship between the radar reflectivity factor, z, andthe rainrate, R, through the expression z = aRb, where con-stants ‘a’ and ‘b’ vary over different regions as well as rainregimes. In the present study we use a “tropical” Z–R,despite the “various regions and regimes”. The DWR val-ues are available in the scan radius of 614 km with a scanresolution of 300 m and a display range of 614 km. In thepresent study we only use data within 80 or 100 km. Dis-play grid size is 2 � 2 km and the pulse width is 2 ls. A sin-gle scan of DWR covers a full 360� in the azimuth directionat each elevation. For this study an elevation of 0.5� wasselected during the post monsoon months for the year2007 and 2008. These DWR data are then used to producethe surface rainfall for the comparisons with the MGPItechnique. Location of the radar is shown by blue ellipsein Fig. 1.

2.5. Model data

Model forecasts from the National Centre for Environ-mental Prediction-Global forecast System (NCEP-GFS),available in 1� � 1� grids, were used for environmental cor-rection. For the present study 6-hour forecast data consist-ing of atmospheric column relative humidity and

precipitable water are used. The 6-hour forecast forecasttime was chosen for two main reasons: (1) the forecastgradually deteriorates for longer forecast periods and (2)this is a sufficient turn around time to incorporate the fore-cast to real-time rainfall estimation for operational pur-poses in the area of study.

3. Methodology

The MGPI technique consists of two steps: (1) estimat-ing rainfall using the regular GPI technique, and then (2)incorporating the environmental moisture correction factorin the rainfall estimates derived in the first step.

For the present study Kalpana IR images at each synop-tic hour are utilized. A Kalpana IR pixel has dimensions of8 � 8 km at sub-satellite point meaning there are dozens ofpixels that fall within 1� � 1� boxes. The digital count rep-resenting the gray shade value (0–1023) of each pixel in agiven image is converted to the corresponding TB’s usinga look-up table provided with each IR image. Pixels lyingoutside the chosen area of analysis are ignored while thosewithin it are assigned to an appropriate 1� � 1� box.

The fractional cloud coverage within a grid box is theratio of the pixels colder than a specified threshold temper-ature (235 �K) to the total number of pixels. Finally, toproduce the estimates of precipitation the following regres-sion equation is used:

R ¼ 3F Dt ð1Þ

where R is rainfall from GPI estimates and 3 in mm/h; F isthe fractional coverage of cloud-top temperature <235 �Kfor the desired 1.00� latitude/longitude region; and Dt isthe integration time. Arkin (1979) used 3-hourly imagesto produce 3-hourly precipitation for each image. Aspointed out by Scofield (1987), the GPI technique overesti-mates rain in a dry atmosphere and underestimates in a wetatmosphere. Scofield (1987) proposed the use of an envi-ronmental moisture correction factor defined as the prod-uct of precipitable water (PW) integrated from thesurface to 500-mb with relative humidity (RH) (mean val-ues between the surface to 500-mb level) PWRH, PWRH isdefined as

PWRH ¼ PW�RH ð2Þ

The PW and RH are obtained from models or from thereanalysis. We adopted the same approach using data fromNCEP-GFS. Precipitable water is expressed in incheswhereas the relative humidity is in percentage. The valuesof the PWRH factor are between 0 and 2 and it hardlygo beyond this limit and the environment is considereddry if PWRH is significantly below 1.0 and quite moist ifPWRH is greater than 1.0 (Vicente et al., 1998). After themultiplication the value obtained (PWRH) is in general be-tween 0 and 2 (only very few cases are with PWRH > 2).Beyond some points (i.e. PWRH >= 2) the environmentis saturated enough to make any further change. The finalrainfall is estimated by multiplying the rainfall derived

Fig. 2. Rainfall (daily accumulated) from (a) GPI, (b) TRMM-3B42, and(c) MGPI on 9th June 2007 in mm/day.

52 A.K. Mishra et al. / Advances in Space Research 48 (2011) 49–55

from the GPI technique (Eq. (1)) with the PWRH factor(Eq. (2)) i.e.

Final rainfall ¼ PWRH�R ð3Þ

This equation is valid for all the seasons as well as surfaceterrain types. Since the GPI technique is valid for convec-tive rainfall only (Arkin, 1979), Eq. (3) is valid for the con-vective rainfall only.

4. Results and discussions

The present technique was tested for the Indian land andoceanic regions during Southwest monsoon, pre-monsoonand post monsoon seasons of 2006 and 2007. The perfor-mance of the algorithm is examined by comparison withTRMM-3B42 and validation with rain-gauges. Variouscase studies were undertaken; however, only one of theresults will be discussed in the present paper for brevity.

The case study presented here was carried out for 9thJune 2007 when there were three intense convective systemspresent at around 25�N 90�E, 25�N 115�E and 5�S 95�E.Fig. 2a shows the daily rainfall from GPI for the studyregion which clearly shows the rain associated with thesesystems. From the figure it is evident that the rain amountsassociated with these systems from the GPI are in the range50–70 mm, 60–80 mm and 40–70 mm respectively whichappear to be underestimations when compared with corre-sponding TRMM-3B42 observations (Fig. 2b) which indi-cate that the maximum rain amounts associated with thesesystems are in the range 100–120 mm, 90–110 mm and 80–100 respectively.

As is evident from Fig. 2a and b, GPI underestimatesthe rain amount, but overestimates the spatial coverage.Fig. 2c shows the resulting rainfall after applying thePWRH factor mentioned above. It is evident that the rain-fall associated with all the mentioned systems has now beenincreased to 100–120 mm, 80–110 mm and 80–100 mmrespectively, which are very close to TRMM-3B42 observa-tions. A careful look at Fig. 2b and c indicates that appli-cation of the PWRH has not only improved the rainamounts but also removed the false rain which was notsupported by the amount of environmental moisture pres-ent in the atmosphere, for example in the Chinese zone of30–40�N/90–100�E. Further, from Fig. 2a one observesthat the GPI technique estimated the rainfall amount upto 20–40 mm over the Himalayan regions (30–35�N/90–95�E), suggesting a cold bias. The PWRH correction termin MGPI clearly removed this regional bias and is well cor-roborated with TRMM-3B42 product. Apart from theseintense systems there is one more system around 15�N80�E with observed rainfall amount in the range 10–30 mm from the GPI technique (Fig. 2a). TRMM-3B42observation of the same system gives the rainfall amountin the range 40–120 mm, with the maximum being120 mm (Fig. 2b). The MGPI enhances rain amounts byup to 50 mm for this system (Fig. 2c). It is clear from theabove analysis that the application of the moisture factor

A.K. Mishra et al. / Advances in Space Research 48 (2011) 49–55 53

leads to a significant improvement to the GPI technique.We, in fact, applied this modified method to many morecases and found it suitable enough to propose it as a sub-stitute to the GPI technique used earlier by Gairola et al.(2007) for operational implementation.

Performances of the GPI and the present technique arealso quantitatively analyzed vis-a-vis TRMM-3B42 rainfallvalues. Many cases were considered for the comparisonduring SW–NE monsoon period of 2006 and 2007, whichon averaging in 1� � 1� (3-hourly) boxes resulted in65,453 (during 1–22 July 2006, 7–28 August 2006, 3–18September 2006, 1–17 December 2006, 2–26 March 2007,3–30 June 2007, 16–26 July 2007) grid points over the areaof study, i.e. 20�S–40�N/50�E–120�E. These points areplotted in Fig. 3a and b showing comparisons of rainfallbetween both the MGPI and regular GPI techniques andthe TRMM-3B42 data, respectively.

The correlation coefficient between the GPI andTRMM-3B42 is 0.62 and the root mean square error is2.35 mm while bias is �2.352 mm. The correlation coeffi-cient between the present technique and TRMM-3B42 is0.73 and the root mean square error 1.38 mm while the biasis �0.1684 mm. Based on these statistics we believe thatMGPI exhibits superior performance compared to theGPI. It is clear from the Fig. 3b that high rainfall values(in the range of 8–18 mm/3 h) from MGPI were in goodagreement with TRMM-3B42 but did not match well forlow rainfall values (<8 mm). This may be due to the factthat TRMM-3B42 data are less accurate in depicting lowrainfall values (Huffman et al., 2007). From the above dis-cussions it is clear that the application of environmentalmoisture correction factor improves the GPI rain estimatesand that makes MGPI using Kalpana geostationary IRobservations a very valuable operational tool for now-cast-ing in an important area of the globe where the agricultureand economy are still largely dependent on monsoon rain.

Fig. 3. Scatter plot between the rainfall (3-hourly) from (a) GPI

Further, we carried out a validation of our results usinga data base of 1245 data points during 15–21 May 2007, 1–7 July 2007, 13–16 September 2007, and 21–17 November2007 of AWS rain-gauges after averaging rain valueswithin 1� � 1� grid box. Fig. 4a and b shows the statisticalcomparison between GPI and MGPI derived rainfall val-ues with rain gauge observations respectively. Fig. 1 showsthe statistical comparison of the three techniques. The cor-relation coefficient between the GPI technique and raingauge is 0.4620 and the root mean square error is25.76 mm while the bias is �5.332 mm. The correlationcoefficient between the MGPI technique and rain gauge is0.6022 and the root mean square error is 23.84 mm whilethe bias is �0.13904 mm. It is clear that MGPI techniqueis able to retrieve the rainfall much more accurately thanthe GPI technique by removing unwanted biases and aug-menting the quantitative precipitation to more realistic lev-els. The results show significant improvements in the errorstatistics and their qualitative and quantitative representa-tions in the above mentioned case studies.

In order to confirm the utility of Kalpana based MGPImethod we compared TRMM-3B42 data with a similar setof AWS rain gauge data (Fig. 5). The error statistics (Table1) shows that the correlation coefficient is almost similar tothat observed in MGPI versus AWS (Fig. 3b) but the biasin 3B42 is larger compared to MGPI.

Finally, we compared the rainfall from the MGPI tech-nique with the rainfall from DWR. Fig. 6 shows the statis-tical comparison between rainfall from RADARobservation and rainfall from the MGPI technique. A totalof 542 data points during 13–17 September 2007, 19–25November 2007, 1–7 March 2008, and 14–18 May 2008were considered after averaging them in 1� � 1� grid boxes.Table 2 shows the statistical comparison of these twoobservations. The correlation coefficient between rainfallfrom RADAR observation and the rainfall from MGPI

vs TRMM-3B42, and (b) MGPI vs TRMM-3B42 in mm/3 h.

Fig. 4. Scatter plot between the rainfall (daily accumulated) from (a) GPI vs AWS rain-gauge, and (b) MGPI vs AWS rain gauge in mm/day.

Fig. 5. Scatter plot between the rainfall (daily accumulated) fromTRMM-3B42 and the rainfall from the AWS rain-gauge in mm/day.

Table 1Statistical comparison of rainfall (daily accumulated) from GPI, TRMM-3B42 and MGPI with that from AWS rain-gauge.

Technique Correlationcoefficient

Root mean square error(mm)

Bias(mm)

GPI 0.4620 25.76 �5.3320MGPI 0.6022 23.84 �0.1390TRMM-

3B420.6743 23.76 4.04

Fig. 6. Scatter plot between the rainfall (3-hourly) from present techniqueand the rainfall from the DWR (rainfall unit is in mm/3 h).

Table 2Statistical comparison of rainfall (3-hourly) from MGPI with rainfall fromDWR.

No. of data points 542Correlation coefficients 0.69Root mean square error (mm) 3.15Bias (mm) 1.41

54 A.K. Mishra et al. / Advances in Space Research 48 (2011) 49–55

technique is 0.69, root mean square error is 3.15 mm/3 hwhile the bias is 1.41 mm/3 h. The above results show thatMGPI technique is able to retrieve rainfall with very goodaccuracy.

The turn around time of MGPI using Kalpana-IR data issuitable enough for its application to real time operations.

5. Concluding remarks

This paper describes a modification to the traditionalGPI rainfall technique and its wider application in the

A.K. Mishra et al. / Advances in Space Research 48 (2011) 49–55 55

Indian land and oceanic regions using Indian geostationaryKalpana data. This modified technique implements anenvironmental moisture correction factor to the GPI tech-nique. Comparisons with the TRMM-3B42 rain productsand validation against rain gauge data are also presented.The comparison of the MGPI and the GPI technique withthe TRMM-3B42 shows that before the application of theenvironmental correction factor high rainfall values werenot well picked up (GPI technique). However, after theapplication of this factor high rainfall values are moreaccurately resolved at a smaller temporal scale (3-hourly)which is highly desirable in now-casting applications Vali-dation of these results with the AWS rain-gauges show thatthe MGPI technique shows a significant improvement tothe GPI technique for retrieving high as well as low rainfallevents. However, there were also certain instances wherethe MGP technique overestimated rainfall. Validationusing rain gauges indicates that the GPI technique showsa larger bias and root mean square error as well as poorercorrelation, while the MGPI technique shows significantimprovements both quantitatively and qualitatively. Thus,we conclude that the just presented MGPI technique pro-vides better suitability for operational applications, suchas now-casting and for validation of NWP model resultsusing Indian INSAT/Kalpana data on real-time basis asintended in the objectives of this investigation.

Acknowledgements

We thank Director Space Applications Centre, forencouragements. TRMM data obtained from NASA –Goddard Earth Sciences Data and Information ServicesCentre Distributed Active Archive Centre (GES DISCDAAC) are gratefully acknowledged. We also thank theanonymous reviewers for their useful and critical com-ments, which improved our manuscript substantially.

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