Made available in DSpace on 2015-05-14T12:09:33Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 7249641 bytes, checksum: 530262ccdecfb3b77d7356a51a29f342 (MD5) Previous issue date: 2014-05-15 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The spatial and temporal variability is a precipitation feature and constitutes a factor of complexity for developing rainfall studies. Moreover, the low density of rain gauge stations and errors in data collection in the field increase the difficulties in implementing studies in this research area. However, such researches are essential considering that it is from them that we can carry out flood and drought forecasts, understand the hydrological regime of rivers, soil moisture, temperature changes, among others. Thus, the spatial rainfall estimates obtained through satellites data are important because, although present uncertainties, when compared with punctual data measured in the field can provide good indicators of the spatial distribution of rainfall for a given area. In this research, we evaluate the potential of rainfall estimates from TRMM (Tropical Rainfall Measuring Mission) sensor to represent the spatio-temporal variability of precipitation in the State of Paraíba, in the Northeast of Brazil. In this study we considered daily time series of 14 years length of rainfall data collected by AESA (Agência Executiva de Gestão das Águas do Estado da Paraíba) in 269 rainfall gauges and rainfall data estimated from TRMM satellite for a spatial mesh of 198 grid points covering the Paraíba State and which have been interpolated to the rain gauge locations using the inverse squared distance method. Comparisons were made considering the accumulated rainfall in different periods of time: daily, three days, seven days and monthly. With respect to spatial factors, the comparisons were developed based on punctual values in rain gauges stations, areal averages over sub-basins and mesoregions, and topographic profile. The statistical analyzes of comparison between the observed and estimated rainfall were developed based on the average rainfall, the linear correlations, the mean absolute error and root mean square error considering each accumulated period. Regarding the daily precipitation, the majority of the rain gauges (91%) showed correlation coefficients ranging from 0.5 to 0.7. This correlation increases for considering 3 days-rainfall, with values ranging from 0.5 to 0.7 in 56% of rain gauges, and of 0.7-0.8 for 42% of rain gauges. For the 7 days-rainfall, 58% of the rain gauges presented correlations ranging from 0.7 to 0.8, while for the monthly rainfall 95% of the rain gauges obtained correlations higher than 0.8. Therefore, the results indicate that the TRMM satellite provides better estimates when data are accumulated in larger time intervals. The monthly analysis showed that March and April are the months with higher correlation between observed and estimated precipitation, and that in the first months of the year the estimated and observed values have better approximations for all types of analyzes. It was also verified a good estimation potential in the analysis of seasonal variability of precipitation. Moreover, it was observed that the satellite presents the largest errors in the areas with the largest amount of rainfall. In the sub-basins and in the mesoregions of the state the rainfall regime was estimated quite closely. We concluded that the TRMM satellite presents very good skill in reproducing the observed rainfall measured in the gauge stations over the Paraíba state, becoming an important data source for helping the water resources planning and decision making / A variabilidade temporal e espacial, que é um elemento característico da precipitação pluvial se configura como um fator de complexidade para as pesquisas sobre chuvas. Além disso, a baixa densidade de postos pluviométricos e os equívocos nos processos de coleta em campo aumentam as dificuldades na execução de estudos nessa área de pesquisa. No entanto, tais pesquisas são essenciais tendo em vista que é a partir delas que se pode fazer previsão de enchentes e estiagens, compreender o regime hidrológico dos rios, a umidade do solo, as mudanças de temperatura, dentre outras. Assim, as estimativas espaciais de precipitação realizadas por satélites são técnicas importantes, pois, embora contenham incertezas, quando comparadas com valores pontuais medidos em solo podem fornecer bons indicativos da distribuição espacial das chuvas para uma determinada área. Nesta pesquisa, avalia-se o potencial das estimativas de chuva do satélite TRMM, versão 7 e 3B42 (Tropical Rainfall Measuring Mission) para representar a variabilidade espaço-temporal da precipitação no Estado da Paraíba, no Nordeste do Brasil. No estudo considerou-se séries temporais de dados diários para um período de 14 anos (1998-2011) fornecidas pela AESA (Agência Executiva de Gestão das Águas do Estado da Paraíba) referentes a 269 postos pluviométricos e dados estimados pelo satélite TRMM numa malha espacial de 198 pontos que cobrem o Estado da Paraíba e que foram interpolados para os locais de observação de campo pelo método do inverso do quadrado da distância. As comparações foram realizadas considerando a chuva acumulada em diferentes períodos: diário, três dias, sete dias e mensal. Com relação aos fatores espaciais, os comparativos foram desenvolvidos com base em valores pontuais nos locais de observação, médias espaciais considerando sub-bacias, mesorregiões, e perfil topográfico. As análises estatísticas de comparação entre a chuva observada e a estimada foram desenvolvidas a partir das médias de chuva, das correlações lineares, do erro médio absoluto e da raiz do erro médio quadrático considerando cada período acumulado. Nas análises da chuva diária a maioria dos postos (91%) apresentou índices de correlação variando de 0,5 a 0,7. Esta correlação aumenta para os acumulados de 3 dias, com valores que variam de 0,5 a 0,7 em 56% dos postos pluviométricos e de 0,7 a 0,8 em 42% dos postos. Nos acumulados de 7 dias, 58% dos pluviômetros apresentaram correlações que variam de 0,7 a 0,8 e nos acumulados mensais 95% dos postos apresentam correlações superiores a 0,8. Portanto, os resultados indicam que o satélite TRMM apresenta melhores estimativas quando os dados estão acumulados em intervalos maiores de tempo. Na análise mensal verificou-se que março e abril são os meses mais significativos de estimação e que nos primeiros meses do ano os valores estimados e observados apresentam melhores aproximações para todos os tipos de análises. Identificou-se também bom potencial de estimação na análise da variabilidade sazonal de precipitação. Além disso, observou-se que o satélite apresenta os maiores erros para as áreas onde ocorrem os maiores volumes de chuva. Nas sub-bacias e nas mesorregiões do Estado, o regime de chuvas foi estimado com bastante fidelidade em todas as formas analisadas. Conclui-se que o satélite TRMM apresenta bom desempenho para reproduzir as chuvas observadas em pluviômetros no Estado da Paraíba, configurando-se como uma importante fonte de dados para o auxílio no planejamento e na tomada de decisões relativas aos recursos hídricos

We study the space-time characteristics of global tropical rainfall. The data used is from the Tropical Rainfall Measuring Mission (TRMM) and spans the years 2000-2009. Using anomaly fields constructed by removing a single mean and by subtracting the climatology of the ten year dataset, we extract the dominant modes of variability of tropical rainfall from an Empirical Orthogonal Function (EOF) analysis. To our knowledge, this is the first attempt at applying the EOF formal-ism to high spatio-temporal resolution global tropical rainfall. Spatial patterns and temporal indices obtained from the EOF analysis with single annual mean removed show large scale patterns associated with the seasonal cycle. Even though the seasonal cycle is dominant, the principal component (PC) time series show fluctuations at subseasonal scales. When the climatological mean is removed, spatial patterns of the dominant modes resemble features associated with tropical intraseasonal variability (ISV). Correspondingly, the signature of a seasonal cycle is relatively suppressed, and the PCs have prominent fluctuations at subseasonal scales. The significance of the leading EOFs is demonstrated by means of a novel ratio plot of the variance captured by the leading EOFs to the variance in the data. This shows that, in regions of high variability (which go hand in hand with high rainfall), the EOF/PC pairs capture a fair amount of the variance (up to 20% for the first EOF/PC pair) in the data. We then pursue an EOF analysis of the finest data resolution available. In particular, we per-form a regional analysis (a global analysis is beyond our present computational resources) of the tropics with 0.25◦×0.25◦, 3-hourly data. The regions we focus on are the Indian region, the Maritime Continent and South America. The spatial patterns obtained reveal a rich hierarchical structure to the leading modes of variability in these regions. Similarly, the PCs associated with these leading spatial modes show variability all the way from 90 days to the diurnal scale. With the results from EOF analysis in hand, we quantify the multiscale spatio-temporal structures encountered in our study. In particular, we examine the power spectra of the PCs and EOFs. A robust feature of the space and time spectra is the distribution of energy or variance across a range of scales. On the temporal front, aside from a seasonal and diurnal peaks, the variance scales as a power-law from a few days to the 90 day period. Similarly, below the planetary scale, from approximately 5000 km to 200 km the spatial spectrum also follows a power-law. Therefore, when trying to understand the variability of tropical rainfall, all scales are important, and it is difficult to justify a focus on isolated space and time scales.

Les précipitations ont une très grande variabilité sur une large gamme d'échelles tant spatiale que temporelle. Cette variabilité est une source importante d'incertitude pour la mesure, les applications et la modélisation, et au-delà pour la simulation et la prévision. De plus, les précipitations sont des processus extrêmement intermittents et possèdent plusieurs régimes d’invariance d’échelle. Le générateur de champ précipitant développé au cours de la thèse est basé sur la modélisation statistique de l’hétérogénéité et de l’intermittence des précipitations à fine échelle. L’originalité de la modélisation repose en partie sur l’analyse de données observées par un disdromètre à très fine résolution. Cette modélisation qui diffère des modèles existants dont la résolution est plutôt de l’ordre de la minute, voire de l’heure ou du jour, permet d’obtenir des simulations dont les propriétés sont réalistes sur une large gamme d’échelle. Ce simulateur permet de produire des séries chronologiques dont les caractéristiques statistiques sont similaires aux observations aussi bien à l’échelle de simulation (15s) qu’après dégradation (1h et 1 jour). Les propriétés multi-échelles du simulateur sont obtenues grâce à une approche hybride qui repose sur une simulation à fine échelle des évènements de pluie par un générateur multifractal associé à une simulation du support basée sur une hypothèse de type Poissonienne. Une étape de re-normalisation des taux de pluie assure l’adaptation du générateur à la zone climatique considérée.Le simulateur permet la génération de cartes 2D de lames d’eau. La méthodologie développée pour les séries chronologiques est étendue au cas 2D. Le simulateur stochastique multi-échelle 2D ainsi développé reproduit les caractéristiques géostatistiques et topologiques à la résolution de 1x1 km2. Ce générateur est utilisé dans le cadre d’une étude de faisabilité d’un nouveau système d’observation des précipitations en milieu urbain. Le principe de ce système repose sur l’utilisation de mesures opportunistes de l’affaiblissement subit par les ondes radios émises par les satellites géostationnaires TV-SAT dans la bande 10.7-12.7 GHz. De façon plus spécifique on suppose que les terminaux de réception TVSAT installés en ville chez les particuliers sont capables de mesurer de tels affaiblissements. A ce stade de l’étude nous ne disposons pas de telles observations. L’étude s’appuie donc sur des cartes de précipitations issues du générateur 2D et d’un réseau de capteur hypothétique. Le système d’observation envisagé permettra d’estimer les champs de précipitation (30x30 Km2) et avec une résolution spatiale de 0.5x0.5 Km2. / Precipitations are highly variable across a wide range of both spatial and temporal scales. This variability is a major source of uncertainty for the measurement and modeling, also for the simulation and prediction. Moreover, rainfall is an extremely intermittent process with multiple scale invariance regimes. The rain-field generator developed during the thesis is based on the fine-scale statistic modeling of rain by the mean of its heterogeneity and intermittency. The modeling originality partially rest on the analysis of fine-scale disdrometer data. This model differs from other existing models whose resolution is roughly a minute or even an hour or a day. It provides simulations with realistic properties across a wide range ofscales. This simulator produces time series with statistical characteristics almost identical to the observations both at the 15s resolution and, after degradation, at hourly or daily resolutions. The multi-scale properties of our simulator are obtained through a hybrid approach that relies on a fine scale simulation of rain events using a multifractal generator associated with a rain support simulation based on a Poissonian-type hypothesis. A final re-normalization step of the rain rate is added in order to adapt the generator to the relevant climate area. The simulator allows the generation of 2D water-sheets. The methodology developed in the first part is extended to the 2 Dimension case. The multi-scale 2D stochastic simulator thus developed can reproduce geostatistical and topological characteristics at the spatial resolution of 1x1 km2.This generator is used in the scope of the feasability study of a new observation system for urban area. The principle of this system is based on the opportunistic use of attenuation measurements provided by geostationary TV satellites which radio waves lay in the 10.7 to 12.7 GHz bandwidth. More specifically it is assumed that the SAT-TV reception terminals installed in private homes are able to measure such attenuations. At this stage of the study we do not have such observations. The study is therefore based on rainfall maps generated using the 2D generator in addition to a hypothetical sensor network. The considered observation system will allow to estimate precipitation fields (30 x 30 km2) with a spatial resolution of 0.5x0.5 km2.

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.