Availability of precipitation data is very important in every aspect related to hydrology. Readings from the ground stations are reliable and are used in hydrological models to do various analysis. However, the predictions are always associated with uncertainties due to the limited number of ground stations, which requires interpolation of the data. Meanwhile, groundbreaking approach in capturing precipitation events from vantage point through satellites in space has created a platform to not only merge ground data with satellite estimates to produce more accurate result, but also to find the data where ground stations are not available or scarcely available. Nevertheless, the data obtained through these satellite missions needs to be verified on its temporal and spatial resolution as well as the uncertainties associated before we make any decisions on its basis. This study focuses on finding and evaluating data obtained from two multi-satellite precipitation measurements missions: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) ii) Global Precipitation Measurement (GPM) mission. GPM is the latest mission launched on Feb 28, 2014 after the successful completion of TRMM mission which collected valuable data for 17 years since its launch in November 1997. Both near real time and final version precipitation products for TMPA and GPM are considered for this study. Two study areas representing eastern and western parts of the United States of America (USA) are considered: i) Charlotte (CLT) in North Carolina ii) San Francisco (SF) in California. Evaluation is carried out for daily accumulated rainfall estimates and single rainfall events. Statistical analysis and error categorization of daily accumulated rainfall estimates were analyzed in two parts: i) Ten yeas data available for TMPA products were considered for historical analysis ii) Both TMPA and GPM data available for a ten-month common period was considered for GPM Era analysis. To study how well the satellite estimates with their finest temporal and spatial resolution capture single rainfall event and to explore their engineering application potential, an existing model of SF watershed prepared in Infoworks Integrated Catchment Model (ICM) was considered for hydrological simulation. Infoworks ICM is developed and maintained by Wallingford Software in the UK and SF watershed model is owned by San Francisco Public Works (SFPW). The historical analysis of TMPA products suggested overestimation of rainfall in CLT region while underestimation in SF region. This underestimation was largely associated with missed-rainfall events and negative hit events in SF. This inconsistency in estimation was evident in GPM products as well. However, in the study of single rainfall events with higher magnitude of rainfall depth in SF, the total rainfall volume and runoff volume generated in the watershed were over-estimated. Hence, satellite estimates in general tends to miss rainfall events of lower magnitude and over-estimate rainfall events of higher magnitude. From statistical analysis of GPM Era data, it was evident that GPM has been able to correct this inconsistency to some extent where it minimized overestimation in CLT region and minimized negative error due to underestimation in SF. GPM products fairly captured the hydrograph shape of outflow in SF watershed in comparison to TMPA. From this study, it can be concluded that even though GPM precipitation estimates could not quiet completely replace ground rain gage measurements as of now, with the perpetual updating of algorithms to correct its associated error, it holds realistic engineering application potential in the near future.

Le plateau des Guyanes est une région qui est caractérisée à 90% d’une forêt tropicale primaire et compte pour environ 20% des réserves mondiales d’eau douce. Ce territoire naturel, au vaste réseau hydrographique, montre des intensités pluviométriques annuelles atteignant 4000 mm/an ; ce qui fait de ce plateau une des régions les plus arrosées du monde. De plus les précipitations tropicales sont caractérisées par une variabilité spatiale et temporelle importante. Outre les aspects liés au climat, l’impact des précipitations dans cette région du globe est important en termes d’alimentation énergétique (barrages hydroélectriques). Il est donc important de développer des outils permettant d’estimer quantitativement et qualitativement et à haute résolution spatiale et temporelle les précipitations dans cette zone. Cependant ce vaste espace géographique est caractérisé par un réseau de stations pluviométriques peu développé et hétérogène, ce qui a pour conséquence une méconnaissance de la répartition spatio-temporelle précise des précipitations et de leurs dynamiques.Les travaux réalisées dans cette thèse visent à améliorer la connaissance des précipitations sur le plateau des Guyanes grâce à l’utilisation des données de précipitations satellites (Satellite Precipitation Product : SPP) qui offrent dans cette zone une meilleure résolution spatiale et temporelle que les mesures in situ, au prix d’une qualité moindre en terme de précision.Cette thèse se divise en 3 parties. La première partie compare les performances de quatre produits d’estimations satellitaires sur la zone d’étude et tente de répondre à la question : quelle est la qualité de ces produits au Nord de l’Amazone et sur la Guyane française dans les dimensions spatiales et temporelles ? La seconde partie propose une nouvelle technique de correction de biais des SPP qui procède en trois étapes : i) utiliser les mesures in situ de précipitations pour décomposer la zone étudiée en aires hydro-climatiques ii) paramétrer une méthode de correction de biais appelée quantile mapping sur chacune de ces aires iii) appliquer la méthode de correction aux données satellitaires relatives à chaque aire hydro-climatique. On cherche alors à répondre à la question suivante : est-ce que le paramétrage de la méthode quantile mapping sur différentes aires hydro-climatiques permet de corriger les données satellitaires de précipitations sur la zone d’étude ? Après avoir montré l’intérêt de prendre en compte les différents régimes pluviométriques pour mettre en œuvre la méthode de correction QM sur des données SPP, la troisième partie analyse l’impact de la résolution temporelle des données de précipitations utilisées sur la qualité de la correction et sur l’étendue spatiale des données SPP potentiellement corrigeables (données SPP sur lesquelles la méthode de correction peut s’appliquer avec efficacité). Concrètement l’objectif de cette partie est d’évaluer la capacité de notre méthode à corriger sur une large échelle spatiale le biais des données TRMM-TMPA 3B42V7 en vue de rendre pertinente l’exploitation de ce produit pour différentes applications hydrologiques.Ce travail a permis de corriger les séries satellites journalières à haute résolution spatiale et temporelle sur le plateau des Guyanes selon une approche nouvelle qui utilise la définition de zones hydro-climatiques. Les résultats positifs en terme de réduction du biais et du RMSE obtenus grâce à cette nouvelle approche, rendent possible la généralisation de cette nouvelle méthode dans des zones peu équipées en pluviomètres. / The Guiana Shield is a region that is characterized by 90% of a primary rainforest and about 20% of the world’s freshwater reserves. This natural territory, with its vast hydrographic network, shows annual rainfall intensities up to 4000 mm/year; making this plateau one of the most watered regions in the world. In addition, tropical rainfall is characterized by significant spatial and temporal variability. In addition to climate-related aspects, the impact of rainfall in this region of the world is significant in terms of energy supply (hydroelectric dams). It is therefore important to develop tools to estimate quantitatively and qualitatively and at high spatial and temporal resolution the precipitation in this area. However, this vast geographical area is characterized by a network of poorly developed and heterogeneous rain gauges, which results in a lack of knowledge of the precise spatio-temporal distribution of precipitation and their dynamics.The work carried out in this thesis aims to improve the knowledge of precipitation on the Guiana Shield by using Satellite Precipitation Product (SPP) data that offer better spatial and temporal resolution in this area than the in situ measurements, at the cost of poor quality in terms of precision.This thesis is divided into 3 parts. The first part compares the performance of four products of satellite estimates on the study area and attempts to answer the question : what is the quality of these products in the Northern Amazon and French Guiana in spatial and time dimensions ? The second part proposes a new SPP bias correction technique that proceeds in three steps: i) using rain gauges measurements to decompose the studied area into hydro climatic areas ii) parameterizing a bias correction method called quantile mapping on each of these areas iii) apply the correction method to the satellite data for each hydro-climatic area. We then try to answer the following question : does the parameterization of the quantile mapping method on different hydro-climatic areas make it possible to correct the precipitation satellite data on the study area ? After showing the interest of taking into account the different rainfall regimes to implement the QM correction method on SPP data, the third part analyzes the impact of the temporal resolution of the precipitation data used on the quality of the correction and the spatial extent of potentially correctable SPP data (SPP data on which the correction method can be applied effectively). In summary, the objective of this section is to evaluate the ability of our method to correct on a large spatial scale the bias of the TRMM-TMPA 3B42V7 data in order to make the exploitation of this product relevant for different hydrological applications.This work made it possible to correct the daily satellite series with high spatial and temporal resolution on the Guiana Shield using a new approach that uses the definition of hydro-climatic areas. The positive results in terms of reduction of the bias and the RMSE obtained, thanks to this new approach, makes possible the generalization of this new method in sparselygauged areas.

We present an evaluation of daily estimates from three near real-time quasi-global Satellite Precipitation Products-Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) Morphing Technique (CMORPH)-over the African continent, using the Global Precipitation Climatology Project one Degree Day (GPCP-1dd) as a reference dataset for years 2001 to 2013. Different types of errors are characterized for each season as a function of spatial classifications (latitudinal bands, climatic zones and topography) and in relationship with the main rain-producing mechanisms in the continent: the Intertropical Convergence Zone (ITCZ) and the East African Monsoon. A bias correction of the satellite estimates is applied using a probability density function (pdf) matching approach, with a bias analysis as a function of rain intensity, season and latitude. The effects of bias correction on different error terms are analyzed, showing an almost elimination of the mean and variance terms in most of the cases. While raw estimates of TMPA show higher efficiency, all products have similar efficiencies after bias correction. PERSIANN consistently shows the smallest median errors when it correctly detects precipitation events. The areas with smallest relative errors and other performance measures follow the position of the ITCZ oscillating seasonally over the equator, illustrating the close relationship between satellite estimates and rainfall regime.

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.