Variability of Indian summer monsoon rainfall in daily data from gauge and satellite

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114,, doi: /2008jd011694, 2009 Variability of Indian summer monsoon rainfall in daily data from gauge and satellite S. H. Rahman, 1 Debasis Sengupta, 2 and M. Ravichandran 1 Received 31 December 2008; revised 19 February 2009; accepted 17 June 2009; published 15 September [1] It has long been thought that tropical rainfall retrievals from satellites have large errors. Here we show, using a new daily 1 degree gridded rainfall data set based on about 1800 gauges from the India Meteorology Department (IMD), that modern satellite estimates are reasonably close to observed rainfall over the Indian monsoon region. Daily satellite rainfalls from the Global Precipitation Climatology Project (GPCP 1DD) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are available since The high summer monsoon (June September) rain over the Western Ghats and Himalayan foothills is captured in TMPA data. Away from hilly regions, the seasonal mean and intraseasonal variability of rainfall (averaged over regions of a few hundred kilometers linear dimension) from both satellite products are about 15% of observations. Satellite data generally underestimate both the mean and variability of rain, but the phase of intraseasonal variations is accurate. On synoptic timescales, TMPA gives reasonable depiction of the pattern and intensity of torrential rain from individual monsoon low-pressure systems and depressions. A pronounced biennial oscillation of seasonal total central India rain is seen in all three data sets, with GPCP 1DD being closest to IMD observations. The new satellite data are a promising resource for the study of tropical rainfall variability. Citation: Rahman, S. H., D. Sengupta, and M. Ravichandran (2009), Variability of Indian summer monsoon rainfall in daily data from gauge and satellite, J. Geophys. Res., 114,, doi: /2008jd Introduction [2] Rainfall data are crucial in applications such as water management for agriculture and power and drought and flood forecasting. Reliable observations of rainfall are important for climate science because precipitation is a major component of the Earth s water and energy cycles. These cycles are inherently complex, with interaction between land, ocean, atmosphere and cryosphere. The tropical atmosphere gets three fourths of its heat energy from the release of latent heat associated with precipitation. Elevated heating has strong influence on surface pressure, surface winds in the tropics, evaporation, and ocean circulation. Rainfall determines river runoff and modifies sea surface salinity, upper ocean stratification, and mixed layer depth. Thus, rainfall has very important direct and indirect influence on the distribution of tropical sea surface temperature, atmospheric water vapor, boundary layer moisture convergence, and convection. Finally, the distribution of water vapor and clouds modifies radiative fluxes in the 1 Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Hyderabad, India. 2 Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, India. Copyright 2009 by the American Geophysical Union /09/2008JD atmosphere, and turbulent exchange of heat and water vapor with land and ocean. [3] One of the most challenging problems in climate science is understanding spatial and temporal variations of tropical rainfall. Rainfall has variability on timescales ranging from hours (diurnal) through intraseasonal (weeks), to seasonal, interannual, and longer periods. Further, rain is associated with cloud systems that have complex structure, and are organized on many different space scales. [4] Traditionally it has been hard to obtain reliable precipitation information over oceans, where rain gauges are not available. Even over land, rainfall is a difficult atmospheric variable to measure because of its large spacetime variability. In this context the Tropical Rainfall Measuring Mission (TRMM) satellite has become an important resource, providing data over the entire tropics since November In combination with infrared observations from geostationary satellites, useful 3-hourly, daily and monthly rain products (the 3B42 data sets) have been made available by the TRMM Project [Adler et al., 2000; Kummerow et al., 2001; Huffman et al., 2007]. Another daily satellite-derived rainfall data set, the GPCP one degree daily product (GPCP 1DD), developed under the Global Precipitation Climatology Project (GPCP), is available from late 1996 to present. [5] These data have been validated against rainfall from gauges based on land [Nicholson et al., 2003a; Narayanan et al., 2005; Chokngamwong and Chiu, 2008] 1of14

2 Figure 1. (a) Region of study and selected boxes. Central India (CI), N, E; eastern India (EI), N, E; northern India (NI), N, E; western India (WI), N, E; and ISO box, N, E; point locations 1, 77.5 E, 21.5 N; and 2, 82.5 E, 17.5 N. (b) Location of rain gauges (taken from Rajeevan et al. [2005]). and ocean surface buoys [Bowman, 2005]. However, most comparisons of satellite and gauge rainfall examine timescales of a week or longer. For example, comparison on climatological [Quartly et al., 2007] or seasonal [Dinku et al., 2007] scale has been done using monthly data. On submonthly timescales a few results are available over Thailand, Africa, and China [Nicholson et al., 2003a, 2003b; Adeyewa and Nakamura, 2003; Chokngamwong and Chiu, 2008; J. Liang and P. Xie, Verifying highresolution satellite precipitation estimates on subdaily scales: Results for southern China, unpublished manuscript, 2007, available at pdf]. [6] Chokngamwong and Chiu [2008] have validated TRMM Multisatellite Precipitation Analysis (TMPA) (3B42 version 6) data using rain gauge data from more than 100 gauges over Thailand. Their results show that 5-year ( ) daily average rainfall for gauge and TMPA are 4.73 and 4.58 mm/d, respectively. The bias and daily root-mean-square deviation (RMSD) of TMPA are 0.12 and mm/d, respectively; the correlation coefficient of daily gauge data and TMPA data is The frequency distribution of daily TMPA rain rate is similar to gauge data. [7] Adeyewa and Nakamura [2003] have shown that TRMM precipitation radar (PR) data overestimate rain in the tropical rain forest region of Africa when compared with Global Precipitation Climatology Centre (GPCC) rain gauge data [Rudolf, 1993]. The 3B43 product, which is the TRMM merged analysis on monthly scale, has the closest agreement with rain gauge data. Nicholson et al. [2003a], using rain gauge data from 515 stations over North Africa shows 3 4% bias for GPCC or GPCP version 1 blended product, for seasonal rainfall fields ( ). Nicholson et al. [2003b] find excellent agreement of TRMM-adjusted GOES precipitation index (AGPI) and TRMM merged rainfall analysis (3B43) with high-density (920 stations) gauge data over west Africa on monthly to seasonal timescale. The RMSD of both satellite-derived products is 0.6 mm/d at seasonal scale and 1 mm/d at monthly resolution. The bias of AGPI is 0.2 mm/d, whereas the TRMM-merged product shows no bias over West Africa. The 1 1 latitude/longitude product also shows excellent agreement at the seasonal scale and good agreement at monthly scale. [8] The India Meteorological Department (IMD) has recently released a daily gridded, quality controlled rainfall data set for the period These daily data are based on 24 h accumulated rain from about 1800 rain gauges located all over India [Rajeevan et al., 2005]. In this paper we present the first results of a comparison of daily GPCP 1DD [Huffman et al., 2001] and TMPA (3B42 version 6: gauge adjusted; hereafter TMPA) [Huffman et al., 2007] rainfall with IMD daily rainfall over Indian land. We examine the patterns of seasonal mean Indian summer monsoon (1 June to 30 September) rainfall and its daily time variability, and look at simple measures of intraseasonal and interannual variability of monsoon rain over selected regions. Section 2 introduces the data sets, including some discussion of rainfall variability in the individual products. Results of the satellite-gauge rainfall comparison are presented in section 3, followed by a brief discussion in section Data [9] The characteristics of the satellite derived rainfall data products GPCP 1DD and TMPA are compared with IMD 2of14

3 Figure 2. Monsoon season (June September) mean rain rate (mm/d). The rain rate spatially averaged over India (mm/d) is shown at top right corner. gridded data over Indian land. This analysis has been done for the summer monsoon season (June September) of We briefly discuss these data sets in sections IMD [10] A new 1 1 gridded daily rainfall data set from IMD [Rajeevan et al., 2005] is used to validate GPCP 1DD and TMPA products over Indian land. The IMD product uses gauge data from 1803 stations to estimate accumulated rainfall in the 24 h ending 0830 LT (Indian Standard Time) (0300 UT). Standard quality control of the raw data is done, such as verification of station information, coding or typing error corrections [Rajeevan et al., 2006]. The distribution of gauges stations is shown in Figure 1b. A rain gauge station is included in the gridded product only if it has at least 80% data coverage (in time) during the period IMD uses the Shepard [1968] interpolation technique for gridding data from individual stations, while the GPCC uses the Willmott et al. [1985] method for interpolation. [11] Rajeevan et al. [2006] compared other global data sets with the 53-year ( ) IMD gridded rainfall from gauges. Comparison of monthly rainfall with the VASClimo data set [Beck et al., 2005] shows differences of the order of 50 mm over most of India. However, along the west coast of India IMD rainfall values are higher than VASClimo values. On interannual timescales, all major drought and excess rain years are captured by the VASClimo data set. The correlation coefficient for the period between IMD and VASClimo monthly data is TMPA [12] The input data in TMPA come from two different sets of sensors. The precipitation related passive microwave data are collected by a variety of low Earth orbit satellites, which includes the TRMM Microwave Imager (TMI), Special Sensor Microwave Imager (SSM/I) onboard Defense Meteorological Satellite Program satellites, Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on Aqua, and the Advanced Microwave Sounding Unit-B (AMSU-B) on the National Oceanic and Atmospheric Administration (NOAA) satellite series. In the current TMPA system, precipitation estimates are made from passive microwave fields of view (FOVs) from TMI, AMSR-E, and SSM/I with sensor-specific versions of the Goddard Profiling Algorithm [Kummerow et al., 1996; Olson et al., 1999]. Precipitation estimates from Passive microwave FOVs from AMSU-B are made with operational versions of the Zhao and Weng [2002] and Weng et al. [2003] algorithm. [13] The second major data source for the TMPA is infrared (IR) data from the international constellation of geosynchronous Earth orbit satellites. Finally, the research TMPA also makes use of three additional data sources: the TRMM Combined Instrument estimate, which employs data 3of14

4 Figure 3. The seasonal (June September) daily standard deviation (mm/d). The mean standard deviation over India is mentioned in top right corner. from both TMI and the TRMM precipitation radar (PR) as a source of calibration (TRMM product 2B31 [Haddad et al., 1997]); the GPCP monthly rain gauge analysis developed by the GPCC [Rudolf, 1993]; and the Climate Assessment and Monitoring System monthly rain gauge analysis developed by the Climate Prediction Center [Xie and Arkin, 1996]. The TMPA precipitation estimates are then produced in four stages: (1) the microwave precipitation estimates are calibrated and combined; (2) infrared precipitation estimates are created using the calibrated microwave precipitation; (3) the microwave and IR estimates are combined, and (4) rain gauge data are incorporated. Details of the combination procedure are given by Huffman et al. [2007] and Adler et al. [2000]. [14] The TMPA algorithm provides 3-hourly and daily precipitation and root-mean-square (RMS) error estimates at latitude/longitude grids in the TRMM domain over 50 S to 50 N [Huffman et al., 2007]. These data have been used for the comparison study. In order to keep all data sets on the same spatial grid, the TMPA data have been smoothed to 1 1 resolution GPCP 1DD [15] GPCP 1DD data are a companion to the monthly GPCP Version 2 satellite-gauge (SG) combination [Adler et al., 2003] product. GPCP 1DD provides precipitation estimates on each day on a globally complete 1 1 latitude/ longitude grid for the period October 1996 to present, with a delay of 2 3 months. The main components of the GPCP 1DD data sets are precipitation estimates that are generated from IR and passive microwave observations. The individual components of the GPCP 1DD precipitation estimates are (1) from SSM/I, by orbit, giving the fractional occurrence of precipitation, and (2) GPCP Version 2 SG combination ( monthly), providing monthly accumulation of precipitation, to algorithms applied to geosynchronous orbit IR brightness temperature histograms (1 1 in the band 40 S 40 N, 3-hourly), loworbit IR GOES Precipitation Index (same time/space grid as IR), TIROS Operational Vertical Sounder (TOVS; 1 1 on daily nodes [Susskind et al., 1997]), and Atmospheric Infrared Sounder (AIRS; 1 1 on daily nodes). [16] The GPCP 1DD product which we use here is derived only from precipitation estimates based on satellite data; no rain gauge information is included in this product directly [Huffman et al., 2001]. Gauge information is included in the final step. The monthly totals accumulated from the daily precipitation fields are scaled to fit the monthly totals of GPCP s version 2 SG product, which includes GPCC rain gauge analysis. The GPCC gauge analysis includes monthly precipitation data from about 6500 global telecommunication system (GTS) stations, i.e., synoptic weather stations and climate stations; there are about 80 stations from India (U. Schneider, personal communication, 2006). The TRMM merged product TMPA and GPCP 1DD analysis both use very similar procedures 4of14

5 Figure 4. Average rain rate (mm/d) of IMD, GPCP 1DD, and TMPA for August with different initial satellite input data, thus simplifying intercomparison. 3. Results [17] As discussed in section 2, the time intervals used for estimating accumulated daily rain by the IMD and TMPA data sets are different. In order to colocate the time, IMD data have been shifted in time by 1 day. We have used the IMD grid to mask out oceanic regions from the satellite products and regridded all the satellite data onto the IMD grid. The different regions used for comparison are shown in Figure 1. Four representative areas over Indian land, i.e., central India (CI), eastern India (EI), western India (WI) and northern India (NI) are chosen on the basis of the consideration that daily variability of rain is more or less spatially uniform within each of these regions [Goswami et al., 2006]. Apart from these four representative areas we have also chosen one box (the intraseasonal oscillation (ISO) box) to examine intraseasonal oscillations and two point locations for time series analysis (Figure 1a) Spatial Distribution of Rainfall [18] The seasonal (June September; JJAS) mean rainfall over India for IMD, GPCP 1DD, and TMPA is shown in Figure 2. The GPCP 1DD product does not show the observed high rainfall over the west coast of India, the northeast, or the Himalayan foothills. The high rainfall in these regions, particularly over the Western Ghats and the Himalayan foothills, is attributed to orography. These regions of high rain are seen in seasonal TRMM PR measurements and SSM/I data [Xie et al., 2006]. TMPA captures the pattern of orographic rain seen in the IMD data but consistently underestimates rainfall amounts. This product also underestimates rain over the Gangetic plains (see Figure 2). The 6-year seasonal mean of TMPA over all grid points are shown in the right corner. TMPA mean is 6.6 mm/d, compared to the IMD value of 8.2 mm/d. Note that the seasonal mean rainfall for the IMD data is 7.7 mm/d [Rajeevan et al., 2005]. An interesting feature is the region of suppressed rain east of the eastern Ghats in peninsular India. This rain shadow region is present in TMPA data but not in GPCP 1DD. [19] The spatial distribution of daily standard deviation (SD) of IMD, GPCP 1DD and TMPA rain during JJAS is shown in Figure 3. The SD values averaged over all grid points are shown at top right of each plot in Figure 3. Although the large-scale spatial distributions of GPCP 1DD and TMPA SD might be considered reasonable, the large SD in the IMD data along the west coast of India is captured only by TMPA. Averaged over all of India, the daily SD of GPCP 1DD and TMPA rain is within 24% of 5of14

6 Figure 5. Average rain rate (mm/d) of IMD, GPCP 1DD, and TMPA for August the IMD daily SD (14.4 mm/d) (Figure 3). The relative superiority of TMPA data can also be seen in the results of Dinku et al. [2007]. They have compared several existing satellite rainfall products with the Somali highland rain gauge data of different spatial resolutions but with temporal resolution of 10 days. TMPA performs somewhat better than GPCP 1DD, with an efficiency value three times higher than 1DD, and 54% and 15% reduction in bias and RMS difference, respectively. [20] Each summer monsoon season, several depressions and low-pressure systems form over the north Bay of Bengal. These systems move to the west and northwest, and bring torrential rain to central India. We show examples of satellite and gauge rainfall due to a low pressure system and a monsoon depression. A low-pressure system developed over central India during August 2002 [Thapliyal et al., 2003]. Figure 4 shows the August average rainfall from IMD, GPCP 1DD and TMPA. The maximum rain in the IMD data reaches 93 mm/d; GPCP 1DD grossly underestimates the peak rain rate, whereas the maximum rain rate in TMPA is about 76 mm/d. A monsoon depression was formed over north Bay of Bengal during the last week of August 2003 [Jayanthi et al., 2004]. It crossed the state of Orissa on 27 August, moved inland over Chhattisgarh on 29 August and weakened thereafter. Orissa and Chhattisgarh received torrential rainfall during August 2003; Figure 5 shows the average rain rate from IMD, GPCP 1DD and TMPA during this period. TMPA captures both the spatial distribution and intensity of rainfall seen in IMD data, whereas GPCP 1DD does not capture the rain over western India, while overestimating the peak rainfall Rainfall Time Series [21] Figure 6 shows the rain rate of IMD, GPCP 1DD and TMPA averaged over central India (CI; E, N) and eastern India (EI; N, E) for the summer monsoon season (JJAS) of ; the 6-year mean and SD are mentioned in the upper corner. The major active and break spells are captured by GPCP 1DD and TMPA data in all years. The variability of TMPA rain is close to that of IMD rain over the CI box (>10 6 km 2, 133 grid boxes) and the smaller EI box (25 grid boxes, see Figure 1a). Figure 7 shows scatterplots of GPCP 1DD and TMPA rain rate versus IMD rain rate over CI and EI during JJAS of The correlation coefficient (CC) is mentioned in Figure 7. GPCP 1DD shows more scatter compared to TMPA, also reflected in the CC values of 0.74 for GPCP 1DD and 0.82 for TMPA. The CC values for EI are 0.64 and 0.73 for GPCP 1DD and TMPA, respectively. [22] Figure 8 shows the daily rain rate of IMD, GPCP 1DD and TMPA averaged over northern India (NI; N, 6of14

7 Figure 6. Daily rain rate (mm/d) averaged over (a) central India (CI) and (b) eastern India (EI). Note different scales in Figures 6a and 6b. The mean and standard deviation of the time series are given in mm/d E) and western India (WI; N, E). NI and WI are both composed of 25 equal grid points. The variations of IMD rainfall are captured by both the satellite data; TMPA performs much better than GPCP 1DD in terms of mean and SD. Figure 9 shows scatterplots of GPCP 1DD and TMPA versus IMD rain in NI and WI, confirming the better performance of TMPA rain as compared to GPCP 1DD rain. The root-mean-square error (RMSE) of the satellite data over CI, EI, NI and WI with respect to IMD is given in Table 1. RMSE values shows minimum for TMPA in all regions. Figures 6 and 8, and Table 1 suggest that the satellite products specially TMPA, are realistic enough to be used for the study of monsoon rainfall variability on synoptic and longer timescales over Indian land. [23] In order to examine how the satellite based products capture time variability at a single location, we consider two locations (Figure 1a), one at 17.5 N, 82.5 E near the east coast of India marked 2 and another at 21.5 N, 77.5 E in central India marked 1. At location 2, which is in a localized rain shadow (Figure 2), the 6-year JJAS mean of IMD, GPCP 1DD and TMPA rain rates are 4.8, 6.6, and 5.6 mm/d, respectively, and the SD are 9.9, 9.9, and 9.6 mm/d; the satellite products overestimate the mean. The CC values are 0.41 for GPCP 1DD and 0.46 for TMPA. The 6-year JJAS mean (SD) values over the central India grid point are 6.8 (16.2), 6.1 (11.9) and 6.0 (10.8) mm/d for IMD, GPCP 1DD and TMPA. The CC value 0.64 for TMPA is large compared to GPCP 1DD CC value of As seen before, the satellite products generally underestimates the mean and variability of rainfall. Heavy rain events are in general underestimated in satellite data including TMPA (Figures 10a and 10b), but there are occasions of spurious heavy rain in the satellite data Intraseasonal and Interannual Variability [24] The Indian summer monsoon exhibits pronounced subseasonal variability on timescales ranging from a few days to more than a month. The intraseasonal oscillation (ISO) of the monsoon consists of two main modes, the quasi-biweekly mode with a period of days and a longer period intraseasonal mode of day period [Krishnamurti and Bhalme, 1976; Krishnamurti and Ardanuy, 1980; Yasunari, 1980]. The day mode has clear westward propagation and weak northward propagation in the northern hemisphere while the day mode has northward propagation. These characteristics of monsoon ISO are seen in various parameters, such as surface pressure [Krishnamurti and Subrahmanium, 1982], the low-level jet [Joseph and Sijikumar, 2004] surface wind speed [Goswami et al., 1998], cloudiness [Yasunari, 1980; Sikka and Gadgil, 1980], rainfall [Kripalani et al., 2004] and water vapor [Cadet and Greco, 1987; Sajith et al., 2003]. A recent review is given by Goswami [2005]. [25] In order to see how well the intraseasonal oscillations are captured by satellite rainfall data sets, we chose an 7of14

8 Figure 7. Scatterplots between (left) IMD and GPCP 1DD; (right) IMD and TMPA over (a) central India (CI) and (b) eastern India (EI). Note different scales in Figures 7a and 7b. Correlation coefficients are given. 8of14

9 Figure 8. Same as Figure 5 but for (a) northern India (NI) and (b) western India (WI). 9of14

10 Figure 9. Same as Figure 6 but for (a) northern India (NI) and (b) western India (WI). area ( E, N) in the center of the monsoon zone which receives heavy rain during the summer monsoon season [Gadgil, 2003]. We choose two contrasting monsoon years, 2002 (a drought year, the Indian monsoon rain was 20% lower than the long-term average) and 2003 (a normal year). Figure 11 shows the 7-day running mean rainfall time series over this region for IMD, GPCP 1DD, and TMPA. The running mean filters out high-frequency variability, and helps to show the intraseasonal variability on timescales of about a week and longer. All data sets show that the rain has pronounced ISO with periods of 10 days and longer in 2002, but ISO are not very prominent in However, TMPA is closer to IMD observation than GPCP 1DD. This suggests that for ISO studies TMPA is more realistic than GPCP 1DD. Our results are consistent with Hartmann and Michelsen [1989], who report little evidence of intraseasonal activity in Indian daily rainfall north of 22 N during normal monsoon years. [26] Figure 12 shows the power spectra of daily rain rate, averaged over the ISO box during June September of 2002 and In 2002, both satellite products over estimate the power at about day periods. The power at days in 2003 is closer to the IMD values in TMPA spectra, as compared to GPCP 1DD spectra. TMPA performs distinctly better on the day periods in both 2002 and [27] Figure 13 shows the interannual variation of seasonal mean monsoon rainfall over central India ( E, N) in (IMD gridded data are not available after 2003). We find a pronounced biennial oscillation in gauge as well as satellite rainfall. The amplitude of this oscillation is 10 cm in the IMD gridded data, whereas it is 5 cm in TMPA and GPCP 1DD data. In all years, the seasonal total GPCP 1DD rainfall is closer to the IMD total than TMPA. The biennial oscillation is not present in the all India seasonal total rain (not shown). The all India seasonal total rainfall for individual years during and its 6-year mean, as well as the 6-year seasonal standard deviation (SD) are shown in Table 2. The coefficient of variation (i.e SD divided by mean) of IMD seasonal rainfall over this 6-year period is 10.8%, close to the value of 11.8% for the IMD data [Rajeevan et al., 2005]. The GPCP 1DD and TMPA Table 1. RMS Difference Between Daily Satellite and IMD Rain Rate Averaged Over Regions CI, EI, NI, and WI for June September a CI EI NI WI GPCP 1DD TMPA a RMS difference in units of mm/d. Regions are CI, central India; EI, eastern India; WI, western India; and NI, northern India. 10 of 14

11 Figure 10. Daily rain rate (mm/d) at a single grid point near the east coast of India and in central India for (a) June September 2002 and (b) June September of 14

12 Figure 11. Seven-day running mean rainfall (mm/d) over the ISO box ( E, N) showing subseasonal variability in (top) 2002 and (bottom) Figure 12. JJAS power spectra of IMD, GPCP 1DD, and TMPA rainfall over ISO box ( E, N). 12 of 14

13 Figure 13. Interannual variation of total summer monsoon (JJAS) rain (cm) over central India. variability are lower than the IMD values, but TMPA has somewhat higher year-to-year variability than GPCP 1DD. 4. Discussion [28] We have validated Daily satellite-based rainfall from GPCP 1DD and TMPA (TRMM 3B42 version 6) against daily gridded data from rain gauges released recently by the India Meteorological Department, for the summer monsoon seasons of The GPCP 1DD product reproduces the broad features of mean monsoon rainfall over India. If one looks at the shape and size of high- and low-rainfall regions in any detail, GPCP 1DD may be considered an inadequate representation. However, the patterns of TMPA mean monsoon rainfall, including those related to orography [Xie et al., 2006], are reasonably close to the observed patterns from IMD data (Figure 2). [29] Adler et al. [2000] have shown that the TRMM merged monthly product has considerable regional differences over the west Pacific, east Pacific, eastern Indian Ocean and the south pacific convergence zone, when compared with GPCP version 1 data [Huffman et al., 1997]. The regional differences have been attributed to the fact that TRMM is better at taking into account the vertical structure of rain, for instance the difference in structure between the western Pacific (deep convection) and the eastern Pacific (shallow convection). The differences between GPCP 1DD and TMPA documented here may also arise from the way the gauge data enter the satellite retrieval algorithms during their initial formulation. [30] Both satellite products underestimate seasonal mean all-india rain rate (Figure 2). The 6-year all-india mean rain rate of GPCP 1DD (TMPA) is 24% (19%) lower than the IMD mean rain rate; in individual years, GPCP 1DD (TMPA) underestimates seasonal mean rainfall by 17% to 27% (12% to 24%). The main reason for this is that GPCP 1DD underestimates the heavy rainfall in the Western Ghats, the Himalayan foothills and the hilly regions of northeast India. We note, however, that the spatial coverage of IMD gauges in these regions might be considered inadequate (Figure 1b). The 6-year daily standard deviation of GPCP 1DD (TMPA) all-india rain is 24% (22%) lower than the IMD value (Figure 3); the spatial distribution of daily variability in the TMPA product is reasonably close to IMD data, except for the Gangetic plains, the Himalayan foothills and parts of east central India (Figure 2). This is likely to be due to the inclusion of more data from microwave sensors in TMPA, as well as data from the TRMM precipitation radar. [31] Both the mean and daily standard deviation of TMPA rain are close to the IMD values over northern India and western India (Figure 8). The 6-year mean rain rate and SD of TMPA (GPCP 1DD) over NI and WI are within 15% (28%) and 3% (23%) of the values based on IMD gauge data. However, for central India and eastern India (Figure 6), GPCP 1DD performs slightly better than TMPA. The 6-year mean rain rate and SD of TMPA (GPCP 1DD) over nonhilly regions, i.e., CI and EI, are within 17% (8%) and 9% (5%) of those based on IMD gauge data. At individual grid points, the RMS difference between daily satellite and gauge-based rain can be high, but over larger regions the agreement is distinctly better (Table 1). [32] Interannual variability in satellite rain has the right phase, but the amplitude is generally underestimated specially over the west coast of India (Figures 2 and 3 and Table 2). However, TMPA values are close to IMD observations, likely because it is calibrated by the TRMM Combined Instrument product. The daily satellite data, particularly the TMPA product, have great potential for the study of rainfall variability on synoptic to interannual timescales over the Indian monsoon region. [33] Acknowledgments. We thank Shailesh Nayak, Secretary, MoES, for his useful suggestions and Jaison Kurian for helping in computation. We thank V. Venugopal and Sudhir Joseph for their suggestion to improve the manuscript. We wish to acknowledge M. Rajeevan for providing the IMD data, and NASA GSFC, NOAA-CDC for providing the TMPA and GPCP 1DD data on their ftp site. Ferret and GrADS have provided data analysis and graphics. D.S. thanks Ministry of Earth Sciences for the support. We Table 2. All India Seasonal Total Rainfall, a IMD GPCP 1DD TMPA Mean SD a Total June September rainfall in units of cm. IMD, India Meteorology Department; GPCP 1DD, Global Precipitation Climatology Project; and TMPA, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis. 13 of 14

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