Dissertations / Theses on the topic 'Satellite rainfall data'

Forecasting rainfall is of great importance for the farmers in West Africa. However, due too lack of reliable weather observations, rainfall forecats in West Africa are difficult and primarly based on satellite observations. This thesis will study a satellite dataset that could possible work as a substitute for weather balloon soundings and thus improving the rainfall forecasts. A satellite dataset with atmospheric temperature and humidity profiles, obtained from GPS-RO, was compared with radiosondes available from Abidjan, Bamako and Niamey, to study the potential of improving rainfall forecasts over West Africa. Two case studies with simulated weather forecasts with and without assimilated GPS-RO data was also compared. Data assimilation is used to produce an estimate of the atmospheric properties. Temperature profiles obtained from GPS-RO data showed insignificant bias compared to the radiosondes. Probable humidity sensor failure resulted in problem analysing the dew point temperature. From simulations, it was shown that GPS-RO assimilation may have a large impact on the forecasts and could potentially be a substitute for radiosondes in West Africa.
Regnprognoser är något som är viktigt för jordbrukare. I Västafrika saknas pålitliga väderobservationer och regnprognoser är istället baserade på satellit observationer. Denna uppsats riktar sig på att studera ett satellit dataset som har möjligheten att vara ett substitut för väderballongssonderingar och på så vis vara ett steg mot förbättrade regnprognoser. Ett dataset med atmosfäriska temperatur- och fuktighetsprofiler, erhållen från GPS-RO, jämfördes med radiosonderingar från Abidjan, Bamako och Niamey, för att studera dess potential för förbättrade regnprognoser över Västafrika. Två fallstudier med simulerade väderprognoser med och utan assimilerad GPS-RO data jämfördes också. Data assimilering används för att uppskatta de atmosfäriska egenskaperna. Temperaturprofilerna erhållna från GPS-RO data visade ingen signifikant skillnad jämfört med radiosonderingarna. Troligt sensorfel i fuktighetsgivarna från radiosonderingarna ledde till problem med analysen av daggpunktstemperaturen. Simuleringar visade att assimilation med GPS-RO kan ha stor påverkan på prognoserna och har potential att bli ett substitut för radiosonderingar i Västafrika.

Most of the working population in Ghana are farmers. It is of importance for them to know where and when precipitation will occur to prevent crop losses due to droughts and floodings. In order to have a sustainable agriculture, improved rainfall forecasts are needed. One way to do that is to enhance the initial conditions for the rainfall models. In the mid-latitudes, in-situ rainfall observations and radar data are used to monitor weather and measure rainfall. However, due to the lack of station data and the present absence of a radar network in West Africa, other rainfall estimates are needed as substitutes. The rainfall amount in convective systems, dominating in West Africa, is coupled to their vertical structure. Therefore, satellite measurements of cloud top temperatures and microwave scatter, as well as the number of lightning, can be used to estimate the amount of rainfall. In this report, derived rainfall estimates from satellites and the use of lightning data are analysed to see how well they estimate the actual rainfall amount. The satellite datasets used in this report are NOAA RFE2.0, NOAA ARC2, and the EUMETSAT MPE. The datasets were compared to in-situ measurements from GTS- and NGO collaborating observation stations in order to verify which satellite dataset that best estimates the rainfall or, alternatively, if a combination between two or all the datasets is a better approach. Lightning data from Vaisala GLD360 have been compared to GTS-station data and RFE2.0 to see if a relation between the number of lightning and rainfall amount could be found. It was also tested whether a combination between the satellite- and lightning data could be a better estimate than the two approaches separately. Rainfall estimates from RFE2.0 alone showed the best correlation to GTS- and the NGO collaborating station data. However, a difference in how well RFE2.0 estimated rainfall at GTS-stations compared to reference stations was seen. Comparing RFE2.0 to GTS-stations showed a better correlation, probably due to the use of these observations in the build up of RFE2.0. Even though RFE2.0 showed the best correlation compared to other datasets, satellite estimates showed in general poor skill in catching the actual rainfall amount, strongly underestimating heavy rainfall and somewhat overestimating lighter rainfall. This is probably due to the rather basic assumptions that the cloud top temperature is directly coupled to rain rate and also the poor temporal resolution of the polar orbiting satellites (carrying microwave sensors). Better instruments and algorithms need to be developed to be able to use satellite datasets as an alternative to rainfall measurements in West Africa. Furthermore, due to the lack of station data, only tentative results between GLD360 and GTS-stations could be made, showing a regime dependence. When further analysed to RFE2.0, a stronger temporal dependence, i.e. seasonal variation, rather than a spatial one was seen, especially during the build up of the monsoon. However, due to poor rainfall estimates from RFE2.0, no accurate rainfall-lightning relation could be made but trends regarding the relation were seen. The use of GLD360 showed to be an effective way to erase false precipitation from satellite estimates as well as locating the trajectory of convective cells. To be able to further analyse rainfall/lightning relation, more measurements of the true rainfall is needed from e.g. a radar.
Majoriteten av Ghanas befolkning arbetar inom jordbrukssektorn. Det är viktigt för jordbrukarna att veta när och var nederbörd kommer att falla för att deras skörd inte ska bli förstörd av till exempel torka eller översvämningar. Det behövs därför bättre nederbördsprognoser för ett hållbart jordbruk. Ett sätt att få mer noggranna prognoser är att förbättra initialvärden till nederbördsmodellerna. Vid de mellersta breddgraderna på norra halvklotet används nederbördsmätningar från in-situ stationer samt data från radarsystem som initialvärden, men på grund av få mätstationer och inget radarsystem i västra Afrika behövs alternativa nederbördsestimater. Nederbörden i västra Afrika domineras av konvektiva system, vars regnmängd är kopplad till dess vertikala struktur. Satellitmätningar av molntoppstemperaturen och mikrovågornas spridning och absorption, liksom antalet blixtar är också relaterat till molnets struktur och kan därför användas för att estimera nederbördsmängden. I den här rapporten analyserades nederbördsestimater från satellitdata samt användning av blixtdata för att undersöka hur bra metoderna är på att estimera den verkliga nederbördsmängden. Satellitdataseten som analyserades var NOAA RFE2.0, NOAA ARC2 och EUMETSAT MPE. Dataseten jämfördes med in-situ mätningar från GTS-stationer samt observationerfrån NGO-samarbetande jordbrukare för att verifiera vilket satellitdataset som ger det bästa nederbördsestimatet, alternativt att en kombination mellan två eller alla dataset ger det bästa estimatet. Vidare har blixtdata från Vaisala GLD360 jämförts med GTS-stationer och RFE2.0 för att se om antalet blixtar är relaterat till nederbördsmängden. Slutligen har det också undersökts om en kombination mellan satellit- och blixtdata är ett bättre än de två metoderna separat. Nederbördsestimater från RFE2.0 visade på bäst korrelation med både GTS- och NGO-stationer. En tydlig skillnad noterades dock i RFE2.0:s förmåga att estimera nederbörd vid jämförelse mellan de två stationsdataseten. En bättre korrelation mellan RFE2.0 och GTS-stationerna påvisades, troligen för att RFE2.0 använder dessa observationer i uppbyggnaden av datasetet. Även om RFE2.0 visade på bäst korrelation i jämförelse med ARC2 och MPE var samtliga satellitdataset dåliga på att estimera den verkliga nederbördsmängden. De underestimerar starkt stora mängder nederbörd samtidigt som de överestimerar små mängder. Anledningen är troligen det relativt enkla antagandet att molntoppstemperaturen är direkt kopplad till molnets regnmängd samt den dåliga tidsupplösningen på de polära satelliterna som är utrustade med mikrovågssensorer. För att satellitdataseten ska kunna användas som ett alternativt nederbördsestimat i Västafrika behövs bättre mätinstrument och algoritmer. Vid analysen mellan GLD360 och GTS-stationer kunde, på grund av för få stationsdata, endast övergripande resultat erhållas. Ett områdesberoende gick dock att urskilja som vid en ytterligare analys mellan GLD360 och RFE2.0 visade på ett större säsongsberoende, särskilt under uppbyggnaden av monsunperioden i april och maj. Eftersom RFE2.0 visade sig ha dåliga nederbördsestimat kunde ingen noggrann koppling hittas, utan resultatet visade på trender samt möjligheter att kunna använda blixtdata som ett alternativt nederbördsestimat. Till exempel visade det sig att GLD360 kunde användas som ett verktyg för att sålla bort falsk nederbörd från satellitestimat samt identifiera trajektorien för ett konvektivt system. För en djupare analys i att relatera blixtar och nederbörd i Västafrika krävs bättre tekniker för att estimera nederbörd eller fler in-situ observationer.

Satellite information, in combination with conventional point source measurements, can be a valuable source of information. This thesis is devoted to the spatial estimation of areal rainfall over a region using both the measurements from a dense and sparse network of rain-gauges and images from the meteorological satellites. A primary concern is to study the effects of such satellite assisted rainfall estimates on the performance of rainfall-runoff models. Low-cost image processing systems and peripherals are used to process and manipulate the data. Both secondary as well as primary satellite images were used for analysis. The secondary data was obtained from the in-house satellite receiver and the primary data was obtained from an outside source. Ground truth data was obtained from the local Water Authority. A number of algorithms are presented that combine the satellite and conventional data sources to produce areal rainfall estimates and the results are compared with some of the more traditional methodologies. The results indicate that the satellite cloud information is valuable in the assessment of the spatial distribution of areal rainfall, for both half-hourly as well as daily estimates of rainfall. It is also demonstrated how the performance of the simple multiple regression rainfall-runoff model is improved when satellite cloud information is used as a separate input in addition to rainfall estimates from conventional means. The use of low-cost equipment, from image processing systems to satellite imagery, makes it possible for developing countries to introduce such systems in areas where the benefits are greatest.

Water is essential for life. In the Arabian Peninsula (AP), rainfall is irregular, infrequent and low. Climate studies show that the AP receives between less than SOmm per year and more than 2S0mm per year depending upon location. Therefore rainfall monitoring and modelling are very important in optimising the use of this scarce resource. Monitoring rainfall using satellite observations is an alternative method which can solve the problem of the inadequate rainfall monitoring by ground-based methods (raingauge and radar). In this project, rainfall variability over the study area was shown using raingauges observations. The Tropical Applications of Meteorology using SATellite (TAMSAT) approach has been tried to estimate rainfall in the Arabian Peninsula. It depends on the use of cold cloud duration based only on thermal infra- red imagery. Then, the rainfall estimates were utilized to evaluate the rainfall ERA- Interim reanalysis dataset over the study area, and the tele-connection between the rainfall variability over the southwest mountainous region of the AP and the southwesterly monsoon was derived using the ERA-Interim data. The results show that the AP has extreme temporal/spatial variation of rainfall. Most of the' study area receives rainfall between October and May while rainfall occurs in the southwest region (SWAP) throughout the year by getting two rainy seasons (winter and summer). It was found that the TAMSAT approach performs well over the SWAP during summer which led to conducting an evaluation of rainfall ERA-Interim date only over this region. The evaluation has shown that the rainfall reanalysis data captures the summer intra/inter-annual rainfall pattern while its rainfall values are overestimated specifically for September. The analysis of the connection between rainfall variability over the SWAP and the southwesterly monsoon gave indication of the monsoon influences. The main finding is the relationship between weak (break)/strong (active) monsoon and the increase/decrease of rainfall over the study area in terms of providing perceptible water which enhances the creation of rainy clouds.

This thesis describes the use of a distrometer and two IBM-PC's to collect data in a large propagation experiment. The uses and methods of collecting drop size distribution are discussed as are the uses of IBM-PC's for both data collection and control. Methods of requiring the PC's to operate in real time are also included.
M.S.

Networks of ground-based hydro-meteorological observations are frequently sparse in developing countries and the situation is not improving. Part of the reason is the lack of resources available in countries which have more pressing economic and social issues. However, these are also the very countries where improved estimates of water resource availability are required. While hydrological models have the potential to provide the necessary information, without adequately accurate climate (rainfall, evaporation, etc.) input information, it is extremely difficult to establish models and generate representative water resource availability information. This paper reports on a preliminary analysis of the potential for using satellite derived rainfall data through a comparison with available gauge data for four basins in the southern Africa region. It is clear that the satellite data cannot be used directly in conjunction with historical gauge data. Specifically, the satellite data do not reflect the strong influences on precipitation of topography in some of the basins. However, the prospects of applying relatively straightforward adjustments are promising and further assessments appear to be justified.

碩士
淡江大學
水資源及環境工程學系碩士班
98
The main purpose of this study is to explore the influence of satellite imagery and meteorological data on typhoon rainfall forecast using artificial neural networks. The self-organizing map (SOM) is adept at recognizing infrared and visible images and can extract some useful information. In this study, six watershed rainfall estimation models are constructed to forecast the amount of rainfall for one, three and six-hour totals during typhoon events. The models are based on SOM, back-propagation neural network (BPNN) or linear regression to investigate the characteristics of satellite imagery information and its influence on rainfall forecast. Twenty-seven typhoon events are collected from 2000 to 2007. The available data are GMS-5/MTSAT remotely sensed data, hourly rainfall data of sixteen rainfall gauge stations of the Shihmen watershed, wind velocity and atmospheric pressure data of three meteorological observation stations. In order to investigate the characteristics and compare the performance among the different models, we design different cases for forecasting the rainfall totals in the daytime and the whole day. Six different models, multivariate linear regression model (MLR), back-propagation neural network (BP), self-organizing map linking with BP (SOMBP), self-organizing map linking with linear regression (SOMMLR), SOMBP linking with BP (SOMBPI+BP) and SOMMLR linking with BP linear regression (SOMMLRI+BP), are constructed to forecast rainfall totals. Seven different combinations of the inputs are used to investigate the effect of rainfall forecast. The results show that (1) the MLR and BP models have nice performances when the input variable only include the past rainfall totals of gauge stations, (2) SOM indeed has the ability to extract patterns from satellite data, (3) SOM can improve results when the rainfall totals are joined, (4) the wind velocity and atmospheric pressure data are helpless for rainfall forecast. The satellite imagery information is indeed helpful to improve the accurate of rainfall forecast.

碩士
國立中央大學
大氣科學學系
106
The Tropical Rainfall Potential (TRaP) technique presented by Kidder et al. in 2005, shifting rainfall distribution from satellite retrieval, and forecasting rainfall for tropical cyclone. Chen(2010) improved TRaP rainfall forecast practicality by adding orographic effect with historical rainfall distribution(I-TRaP). Since I-TRaP forecast uses rainfall distribution from satellite, how to get better rainfall distribution is an important issue. There is only single satellite rainfall product in past study, limited by temporal resolution. For many study, The performance of multi-satellite rainfall products with high spatial-temporal resolution(0.1°-0.25°, 0.5-3h) are getting better recently but less discussed on heavy rainfall especially for typhoon. This study compares few common multi-satellite products (GSMaP, IMERG, PERSIANN) with typhoon heavy rainfall in the North-West Pacific, GSMaP is better. There are different performance between convective and stratiform rainfall. Indeed, the PMW retrieval fail to classification in rainfall type determination during microwave rainfall retrieving, but not cause rainfall error. In addition, compare liquid water content and rainfall error, the PMW retrieval still cannot estimate liquid water accurately in moderate to heavy rainfall. Apply GSMaP to I-TRaP and calculate typhoon rainfall forecast over Taiwan. In order to highlight satellite rainfall distribution, modify earlier method only revising total rainfall and using historical rainfall distribution, calculate rainfall regression by individual point. This method will predict more heavy rainfall but more false alarm. Compare earlier I-TRaP using SSMIS, GSMaP with high spatial-temporal resolution is more useful for I-TRaP forecast, and more prediction of heavy rainfall.

碩士
國立中央大學
大氣物理研究所
102
Weather satellite observations are used widely for the quantitative precipitation forecast (QPF) of typhoon rainfall because they can provide relevant atmospheric parameters over both the ocean and land. Microwave observations by satellites have become the main data in forecasting tropical rainfall potential (TRaP) in a 24-h period (Kidder et al., 2005). The improved Tropical Rainfall Potential (I-TRaP) technique presented by Liu et al. in 2011 is a useful method for typhoon quantitative precipitation estimation and a powerful tool for rain-band monitoring before the typhoon makes landfall in Taiwan. However, the method only provides single prediction which may pose a difficulty when using single sensor or time segment data. To smooth the random error made by single forecast and quantify the uncertainties in prediction, this study seeks to adopt ensemble forecasts to help to provide more reliable predictions. In other words, the goal of this study is to construct a new ensemble I-TRaP technique for typhoon rainfall potential. Besides that, to consider about different rainfall types within a typhoon, the rain-band of a typhoon is separated into two parts: inner rain-band (circulation affected rainfall) and outer rain-band (terrain affected rainfall). This study again constructed another ensemble potential model called the Ensemble I-TRaP B model (“B” stands for “bi-types”), to consider the two rainfall types. Then, the results including that the performances of both ensemble methods in 24-h QPE and the ability of forecasts for different time periods are investigated. There are 77 typhoons from 2001 to 2012 used for long term statistics. Comparing to the I-TRaP model, the ensemble technique (i.e. Ensemble I-TRaP model), and the new model which additionally considering the two rainfall types (i.e. Ensemble I-TRaP B models) can both promote the correlation coefficient from 0.53 to 0.62, and decrease root-mean-square from 81.68 mm to 64.05 mm and to 63.76 mm respectively. It shows that this ensemble technique is useful for improving the rainfall pattern estimation in short accumulated periods, and moreover, it did better forecasting in long periods with a higher correlation coefficient. The results suggest that using the ensemble technique may improve I-TRaP, and considering rainfall types can again promote the rainfall amount prediction.

Thesis (Ph. D.)--Florida State University, 2003.
Advisor: Dr. Eric A. Smith, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Mar. 02, 2003). Includes bibliographical references.

Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting, and climate diagnostic applications. This information also aids in the refinement of physical assumptions within algorithms by identifying geographical regions and seasons where existing algorithm physics may be incorrect or incomplete. Examination of relative errors between independent estimates derived from satellite microwave data is particularly important over regions with limited surface-based equipments for measuring rain rate such as the global oceans and tropical continents. In this context, analysis of microwave based satellite datasets from the Tropical Rainfall Measuring Mission (TRMM) enables to not only provide information regarding the inherent uncertainty within the current TRMM products, but also serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) . Most of the TRMM uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year). Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. This is more so over land regions, where the highly varying land surface emissivity offers a myriad of complications, hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present thesis fosters the development of uncertainty analysis using instantaneous satellite orbital data products (latest version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard TRMM satellite, namely TRMM Microwave Imager (TMI) and precipitation radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Two approaches are taken up to examine uncertainty. While the first approach analyses independent contribution of error from these orbital data products, the second approach examines their combined effect. Based on the first approach, analysis conducted over the land regions of Mahanadi basin, India investigates three sources of uncertainty in detail. These include 1) errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. The second approach is hinged on evaluating the performance of rainfall estimates from each of these orbital data products by accumulating them within a spatial domain and using error decomposition methodologies. Microwave radiometers have taken unprecedented satellite images of earth’s weather, proving to be a valuable tool for quantitative estimation of precipitation from space. However, as mentioned earlier, with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. One such source of uncertainty is contributed by improper detection of rainfall signature within radiometer footprints. To date, the most-advanced passive microwave retrieval algorithms make use of databases constructed by cloud or numerical weather model simulations that associate calculated microwave brightness temperature to physically plausible sample rain events. Delineation of rainfall signature from microwave footprints, also known as rain/norain classification (RNC) is an essential step without which the succeeding retrieval technique (using the database) gets corrupted easily. Although tremendous advances have been made to catapult RNC algorithms from simple empirical relations formulated for computational expedience to elaborate computer intensive schemes which effectively discriminate rainfall, a number of challenges remain to be addressed. Most of the algorithms that are globally developed for land, ocean and coastal regions may not perform well for regional catchments of small areal extent. Motivated by this fact, the present work develops a regional rainfall detection algorithm based on scattering index methodology for the land regions of study area. Performance evaluation of this algorithm, developed using low frequency channels (of 19 GHz, 22 GHz), are statistically tested for individual case study events during 2011 and 2012 Indian summer monsoonal months. Contingency table statistics and performance diagram show superior performance of the algorithm for land regions of the study region with accurate rain detection observed in 95% of the case studies. However, an important limitation of this approach is comparatively poor detection of low intensity stratiform rainfall. The second source of uncertainty which is addressed by the present thesis, involves prediction of overland rainfall using TMI low frequency channels. Land, being a radiometrically warm and highly variable background, offers a myriad of complications for overland rain retrieval using microwave radiometer (like TMI). Hence, land rainfall algorithms of TRMM TMI have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in TMI ocean algorithm). In the present study, sensitivity analysis is conducted using spearman rank correlation coefficient as the indicator, to estimate the best combination of TMI low frequency channels that are highly sensitive to near surface rainfall rate (NSR) from PR. Results indicate that, the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors, but also aids in surface noise reduction over a predominantly vegetative land surface background. Further, the variations of rainfall signature in these channel combinations were seldom assessed properly due to their inherent uncertainties and highly non linear relationship with rainfall. Copula theory is a powerful tool to characterize dependency between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this work proposes a regional model using Archimedean copulas, to study dependency of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from TMI and PR. Studies conducted for different rainfall regimes over the study area show suitability of Clayton and Gumbel copula for modeling convective and stratiform rainfall types for majority of the intraseasonal months. Further, large ensembles of TMI Tb (from the highly sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, 95th) of both convective and stratiform rainfall types. Comparatively greater ambiguity was observed in modeling extreme values of convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal superior performance of the proposed copula based technique. Another persistent source of uncertainty inherent in low earth orbiting satellites like TRMM arise due to sampling errors of non negligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. This study investigates sampling uncertainty of seasonal rainfall estimates from PR, based on 11 years of PR 2A25 data product over the Indian subcontinent. A statistical bootstrap technique is employed to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall was found to exhibit seasonal variations. To give a practical demonstration of the implications of bootstrap technique, PR relative sampling errors over the sub tropical river basin of Mahanadi, India were examined. Results revealed that bootstrap technique incurred relative sampling errors of <30% (for 20 grid), <35% (for 10 grid), <40% (for 0.50 grid) and <50% (for 0.250 grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. In order to study the effect of sampling type on relative sampling uncertainty, the study compares the resulting error estimates with those obtained from latin hypercube sampling. Based on this study, it may be concluded that bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in in-situ validation data. One of the important goals of TRMM Ground Validation Program has been to estimate the random and systematic uncertainty associated with TRMM rainfall estimates. Disentangling uncertainty in seasonal rainfall offered by independent observations of TMI and PR enables to identify errors and inconsistencies in the measurements by these instruments. Motivated by this thought, the present work examines the spatial error structure of daily precipitation derived from the version 7 TRMM instantaneous orbital data products through comparison with the APHRODITE data over a subtropical region namely Mahanadi river basin of the Indian subcontinent for the seasonal rainfall of 6 years from June 2002 to September 2007. The instantaneous products examined include TMI and PR data products of 2A12, 2A25 and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products was quantified based on the performance metrics derived from the contingency table. For the seasonal daily precipitation over 10x10 grids, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition technique, performed to disentangle systematic and random errors, testified that the multiplicative error model representing rainfall from 2A12 algorithm, successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results indicate that even though the radiometer derived 2A12 is known to suffer from many sources of uncertainties, spatial and temporal analysis over the case study region testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications in decision making, prior to incorporating the resulting orbital products for basin scale hydrologic modeling. The current missions of GPM envision a constellation of microwave sensors that can provide instantaneous products with a relatively negligible sampling error at daily or higher time scales. This study due to its simplicity and physical approach offers the ideal basis for future improvements in uncertainty modeling in precipitation.