Ένα πράγμα που θέτει τους στατιστικολόγους πέρα από άλλους επιστήμονες είναι σχετική άγνοια του κοινού γενικά σχετικά με το τι είναι στην πραγματικότητα το πεδίο της στατιστικής. Ο κόσμος έχει μια μικρή γενική ιδέα του τι είναι η χημεία ή η βιολογία — αλλά τι είναι αυτό ακριβώς που κάνουν οι στατιστικολόγοι; Μία απάντηση στο ερώτημα αυτό έχει ως εξής: στατιστική είναι η επιστήμη που ασχολείται με τη συλλογή, περιληπτική παρουσίαση της πληροφορίας, παρουσίαση και ερμηνεία των δεδομένων. Τα δεδομένα είναι το κλειδί, φυσικά — τα πράγματα από τα οποία εμείς αποκτούμε γνώσεις και βγάζουμε αποφάσεις. Ένας πίνακας δεδομένων παρουσιάζει μια συλλογή έγκυρων δεδομένων, αλλά είναι σαφές ότι είναι εντελώς ανεπαρκής για την σύνοψη ή την ερμηνεία τους.Το πρόβλημα είναι ότι δεν έγιναν παραδοχές σχετικά με τη διαδικασία που δημιούργησε αυτά τα δεδομένα (πιο απλά, η ανάλυση είναι καθαρά μη παραμετρική, υπό την έννοια ότι δεν επιβάλλεται καμία τυπική δομή για τα δεδομένα). Επομένως, καμία πραγματική περίληψη ή σύνοψη δεν είναι δυνατή. Η κλασική προσέγγιση σε αυτή τη δυσκολία είναι να υποθέσουμε ένα παραμετρικό μοντέλο για την υποκείμενη διαδικασία, καθορίζοντας μια συγκεκριμένη φόρμα για την υποκείμενη πυκνότητα. Στη συνέχεια, μπορούν να υπολογιστούν διάφορα στατιστικά στοιχεία και μπορούν να παρουσιαστούν μέσω μιας προσαρμοσμένης πυκνότητας.Δυστυχώς, η ισχύς της παραμετρικής μοντελοποίησης είναι επίσης η αδυναμία της. Συνδέοντας ένα συγκεκριμένο μοντέλο, μπορούμε να έχουμε μεγάλα οφέλη, αλλά μόνο εάν το πρότυπο θεωρείται ότι ισχύει (τουλάχιστον κατά προσέγγιση). Εάν το υποτιθέμενο μοντέλο δεν είναι σωστό, οι αποφάσεις που θα αντλήσουμε από αυτό μπορεί να είναι χειρότερες από άχρηστες, οδηγώντας μας σε παραπλανητικές ερμηνείες των δεδομένων. / A thing that places the statisticians beyond other scientists is relative ignorance of public as generally speaking with regard to what it is in reality the field of statistics. The world does have a small general idea what is chemistry or biology - but what is precisely that statisticians do? An answer in this question has as follows: statistics is the science that deals with the collection, general presentation of information, presentation and interpretation of data. The data are the key, from which we acquire knowledge and make decisions. A table of data presents a collection of valid data, but it is obvious that it is completely insufficient for their synopsis or their interpretation. The problem is that no assumptions have been made about the process that created these data (more simply, the analysis is no parametric, under the significance that is no formal structure is imposed on the data). Consequently, no real summary or synopsis is possible. The classical approach in this difficulty is to assume a parametric model for the underlying process, determining a concrete form for the underlying density. Afterwards, can be calculated various statistical elements and a fitted density can manifest itself. The power of parametric modelling is also its weakness. By linking inference to a specific model, we can have big profits, but only if the model is true. If the assumed model is not correct, the decisions that we will draw from this can be worse than useless, leading us to misleading interpretations of data.

[pt] Nas aplicações em computação gráfica e processamento de imagens, curvas e superfícies implícitas têm sido reconhecidas como a representação mais útil de objetos 2D ou 3D, principalmente porque elas permitem a descrição de formas complexas por uma fórmula. A maioria dos métodos implícitos usam curvas algébricas para aproximar globalmente a fronteira do objeto em uma imagem binária. Quando a forma do objeto é complexa, é comum elevar o grau da curva a fim de obter mais precisão na aproximação. Uma solução alternativa é decompor hierarquicamente o domínio em partes compactas e obter aproximações locais para o objeto em cada parte, e então juntar os pedaços com o objetivo de obter uma descrição global do objeto. O principal objetivo deste trabalho é apresentar um novo método de aproximação de curvas implícitas a partir de pontos esparsos que melhora o estado da arte / [en] In the field of computer vision and image analysis, implicit curves and surfaces have been recognized as the most useful representation for 2D or 3D objects, mainly because they allow description of shapes by a formula. Most of implicit methods uses algebraic curves to fit globally the frontier of the foreground in a binary image. When the foreground shape is complex, it is common to elevate the curve degree in order to obtain more precision on the approximation. An alternative solution is to decompose the domain hierarchicaly in compact parts and obtain local approximation for the object in each part, and then patch all together in order to obtain a global description of the object. The main objective of this work is to present a new method for implicit curve fitting from sparse point that improves the state of the art

Geographic research on the Corn Belt and other regional landscapes of the central U.S. has not to date identified quantitatively the climatic, edaphic, topographic, and economic characteristics that determine rural land cover, and that therefore govern land cover change. Using the USDA/NASS Cropland Data Layer, this study identifies these characteristics using Multivariable Fractional Polynomials within a logistic regression framework. It maps the suitability distribution for corn, soybeans, spring and winter wheat, cotton, grassland, and forest land covers that dominate the central U.S., at a 56m resolution across 16 central U.S. states. The non-linear logistic regression models are successful in identifying determinants of land cover with relative operating characteristic (ROC) scores that range from 0.769 for soybeans to 0.888 for forest, with a combined corn/soybean model achieving an ROC of 0.871. For corn and soybean models, when prior land cover of a pixel is added, predictability and ROC scores increase substantially (0.07-0.10), indicating a strong temporal feedback in land cover dynamics. This process also aids in the delineation of fields from pixels. Adding neighboring land covers, however, improves predictability and ROC scores only slightly (0.014-0.019), indicating a weak spatial feedback mechanism. By including annual crop prices within the logit models, economically marginal cropland that comes into crop production only when prices are high is identified in a spatially-explicit manner. This capacity improves further analyses of economic and environmental impacts of policies that affect crop prices. The sustainability of current rural land use trends in the central U.S. is highly dependent on the ability to adapt to changing climatic conditions of the 21st century. As the climate begins to shift towards longer growing seasons, more erratic rainfall patterns, and overall warmer temperatures, there is potential for major impacts on seven major land covers of the central U.S. Suitability landscapes of individual land covers (corn, soybeans, spring and winter wheat, cotton, grasslands, and forests) were utilized to determine the influence of climate change on these landscapes. Twenty-seven climate change projection scenarios based on three global climate models, three representative concentration pathways, and three time periods were applied to the land cover suitability maps utilizing raster regression. The area now identified as the Corn Belt is projected to see a dramatic shift in the suitable climate with a potential for a 30 percent increase in summer growing degree days. While the area where conditions are suitable for corn, soybeans and spring wheat are all expected to decrease, winter wheat has the potential to increase in suitable area. In order to maintain current geographic patterns of crop production, corn would need to be adapted to higher temperatures.

Durante as últimas décadas, o melhoramento genético em bovinos leiteiros no Brasil baseou-se somente na importação de material genético, resultando em ganhos genéticos de pequena magnitude para as características de interesse econômico. Dessa forma, existe a necessidade eminente de avaliações genéticas dos animais sob condições nacionais de ambiente, de maneira a se prover um aumento na produção de leite aliado à qualidade. Neste contexto, o conhecimento sobre a composição do leite é de extrema importância para o entendimento de como alguns fatores ambientais e, principalmente genéticos podem influenciar no aumento dos conteúdos de proteína (PROT), gordura (GOR) e ácidos graxos (AG) benéficos e na redução da contagem de células somáticas, visando a melhoria da qualidade nutricional deste produto. Diante disso, o objetivo desse trabalho foi predizer os teores de AG de interesse usando regressão linear bayesiana, bem como estimar componentes de variância, coeficientes de herdabilidade e comparar modelos de diferentes ordens de ajuste por meio de funções polinomiais de Legendre, sob modelos de regressão aleatória. Amostras de leite foram submetidas a análises de cromatografia gasosa e espectrometria em infravermelho médio para determinação dos ácidos graxos. A comparação dos resultados obtidos por ambos os métodos foi realizada por meio da correlação de Pearson, análise de Bland-Altman e regressão linear bayesiana e, posteriormente, equações de predição foram desenvolvidas para os ácidos graxos mirístico (C14:0) e linoléico conjugado (CLA), a partir de regressões lineares simples e múltipla bayesiana considerando-se prioris nãoinformativas e informativas. Polinômios ortogonais de Legendre de 1ª a 6ª ordens foram utilizados para o ajuste das regressões aleatórias das características. A predição dos AG por meio da aplicação da regressão linear foi viável, com erros de predição variando entre 0,01 e 4,84g por 100g de gordura para o C14:0 e 0,002 e 1,85 por 100g de gordura para o CLA, sendo neste caso os menores erros de predição obtidos quando adotada a regressão múltipla com priori não informativa. Os modelos que melhor se ajustaram para GOR, PROT, C16:0, C18:0, C18:1c9, CLA, saturados (SAT), insaturados (INSAT), monoinsaturados (MONO) e poliinsaturados (POLI) foi o de 1ª ordem, e para escore de célula somática (ESC) e C14:0 o de 2ª ordem. As estimativas de herdabilidade obtidas variaram de 0,08 a 0,11 para GOR; 0,28 a 0,35 para PROT; 0,03 a 0,22 para ECS; 0,12 a 0,31 para C16:0; 0,08 a 0,14 para C18:0; 0,24 a 0,43 para C14:0; 0,07 a 0,17 para C18:1c9; 0,13 a 0,39 para CLA; 0,14 a 0,31 para SAT; 0,04 a 0,14 para INSAT; 0,04 a 0,13 para MONO; 0,09 a 0,20 para POLI e 0,12 para PROD, nos modelos que melhor se ajustaram. Concluise que melhorias na qualidade nutricional do leite podem ser obtidas por meio da inclusão das características produtivas e do perfil de ácidos graxos em programas de seleção genética. / During the last decades, genetic improvement in dairy cattle in Brazil was based only on the importation of genetic material, resulting in small genetic gains for economic interest traits. There is a perceived need for genetic evaluation under national environment conditions to provide an increase in milk production allied to quality. In this context, the knowledge of the milk composition is very important for understanding how certain environmental factors and especially genetic factors may influence the increase in protein content (PROT), fat (FAT), beneficial fatty acids (FA) and in reducing somatic cell count, aiming to improve the nutritional quality of this product. The aim of this study was to predict the levels of interest FA using Bayesian linear regression and estimate the components of variance, coefficients of heritability and compare models with different orders of adjustment by Legendre polynomials functions, in random regression models. Milk samples were subjected to gas chromatography analysis and mid-infrared spectrometry for the determination of fatty acids. The comparison of the results obtained by both methods was performed using Pearson\'s correlation, Bland-Altman analysis and Bayesian linear regression, subsequently, prediction equations were developed for the fatty acids myristic (C14:0) and conjugated linoleic (CLA) from simple linear regressions and multiple Bayesian considering non-informative and informative priors. Legendre orthogonal polynomials from 1st to 6th orders were used to fit the random regression of the traits. That was viable the prediction of FA by applying the linear regression with prediction errors ranging from 0.01 to 4.84 g per 100 g of fat for C14:0 and 0.002 to 1.85 per 100 g of fat for CLA, in this case the smaller prediction errors obtained when adopted the multiple regression with non-informative priori. The models that best fit for FAT, PROT, C16:0, C18:0, C18:1C9, CLA, saturated (SAT), unsaturated (UNSAT), monounsaturated (MONO) and polyunsaturated (POLY) was the one of 1st order and for somatic cell scores (SCS) and C14:0 the one of 2nd order. The estimates of heritability ranged from 0.08 to 0.11 for FAT; 0.28 to 0.35 for PROT; 0.03 to 0.22 for SCS; 0.12 to 0.31 for C16:0; 0.08 to 0.14 for C18:0; 0.24 to 0.43 for C14:0; 0.07 to 0.17 for C18:1C9; 0.13 to 0.39 for CLA; 0.14 to 0.31 for SAT; 0.04 to 0.14 for UNSAT; 0.04 to 0.13 for MONO, 0.09 to 0.20 for POLY and 0.12 for PROD, in the models that best fit. We conclude that improvements in the nutritional quality of milk can be obtained through the inclusion of productive traits and fatty acid profile in genetic selection programs.

Submitted by Viviane Lima da Cunha (viviane@biblioteca.ufpb.br) on 2017-07-06T14:30:18Z No. of bitstreams: 1 arquivototal.pdf: 6614647 bytes, checksum: 21e96f422787eaffc9d7176f0a15e007 (MD5) / Made available in DSpace on 2017-07-06T14:30:18Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 6614647 bytes, checksum: 21e96f422787eaffc9d7176f0a15e007 (MD5) Previous issue date: 2017-02-20 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this dissertation the beta regression models with variable dispersion and quantile regression are discussed. Therefore, an introduction was made with the objective of motivating its discussion in epidemiological studies, emphasizing the problematization around obesity. The application of these methods considered a real data set, obtained from public information sources, referring to adult obesity in the nations in the year 2014. After the descriptive analysis of the data it was verified that 50% of the nations present values of the proportion of obese adults greater than 0.20. In addition, viewing the obesity map by nation showed that the highest concentration of countries with the lowest obesity values is found in the continents of Asia and Africa. On the other hand, the highest concentrations of obese are found in the continents of America and Europe. Also, from the graphical analysis of the box-plot a possible difference in the proportions of obese adults between the continents of America and Europe with those of Africa and Asia was observed. After adjusting the beta and quantile regression models it was verified that the covariates average alcohol consumption in liters per person, percentage of insufficient physical activity and percentage of the population living in urban areas have a positive effect on the response variable. That is, individually such covariables tend to increase obesity values in the countries when the other covariables remain constant. In addition, the life expectancy variable in years presented a positive effect and was significant only for the variable regression beta regression model. Finally, analyzing the measures of prediction errors, it was verified that the estimates from the beta regression are more accurate when the mean square error and the total percentage error were evaluated. Therefore, for questions of predicting values for adult obesity in the nations in 2014, the beta regression model with variable dispersion was more suitable for this purpose. / Nesta dissertação são abordados os modelos de regressão beta com dispersão variável e de regressão quantílica. Para tanto, foi feita uma introdução com objetivo de motivar sua discussão em estudos epidemiológicos, enfatizando a problematização em torno da obesidade. A aplicação destes métodos considerou um conjunto de dados reais, obtidos a partir de fontes de informação pública, referente a obesidade adulta nas nações no ano de 2014. Após a análise descritiva dos dados verificou-se que 50% das nações apresentam valores da proporção de adultos obesos maiores do que 0.20. Além disso, visualizando o mapa da obesidade por nação constatou-se que a maior concentração de países com menores valores de obesidade encontra-se nos continentes da Ásia e África. Por outro lado, as maiores concentrações de obesos encontram-se nos continentes da América e Europa. Ainda, a partir da análise gráfica do box-plot foi observado uma possível diferença nas proporções de adultos obesos entre os continentes da América e Europa com os da África e Ásia. Após ajustar os modelos de regressão beta e quantílica verificou-se que as covariáveis consumo médio de álcool em litros por pessoa, porcentagem de atividade física insuficiente e porcentagem da população que vivem em áreas urbanas apresentam efeito positivo sobre a variável resposta. Ou seja, individualmente tais covariáveis tendem a aumentar os valores de obesidade nos países quando as demais covariáveis permanecem constantes. Além disso, a variável expectativa de vida em anos apresentou efeito positivo e foi significativa apenas para o modelo de regressão beta com dispersão variável. Por fim, analisando as medidas de erros de previsão verificou-se que as estimativas oriundas da regressão beta são mais precisas quando avaliado o erro quadrático médio e o erro percentual total. Portanto, para questões de predizer valores referentes a obesidade adulta nas nações em 2014 o modelo de regressão beta com dispersão variável se mostrou mais adequado para tal propósito.

Regression testing plays an important role during the software development life-cycle,especially during maintenance, it provides confidence that the modified parts of softwarebehave as intended and the unchanged parts have no affect by the modification. Regressiontest selection is used to select test cases from the test suites which have been used to test theprevious version of the software. In this thesis, we extend the traditional definition of a testcase with a log file, containing information of which events that occurred when the test casewas last executed. Based on the contents of this log file, we propose a method of regressiontest selection for multi-tasking real-time systems, able to determine which parts of softwarethat have not been affected by the modification. Therefore, the test cases designed for theunchanged parts do not need to be re-tested.

Submitted by Mario BC (mario@bc.ufrpe.br) on 2016-05-31T16:24:30Z No. of bitstreams: 1 Jose Wesley Lima Silva.pdf: 2387251 bytes, checksum: 617d26e69a59dee1d8bcb33e79bdba13 (MD5) / Made available in DSpace on 2016-05-31T16:24:30Z (GMT). No. of bitstreams: 1 Jose Wesley Lima Silva.pdf: 2387251 bytes, checksum: 617d26e69a59dee1d8bcb33e79bdba13 (MD5) Previous issue date: 2016-02-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The objective of this study was to quantify and test different regression models to estimate the biomass and the amount of carbon from the aerial parts of Eucalyptus clones planted in the Northeastern semi-arid region, and select the best equations based on R2 aj, the Akaike information criterion (AIC), Furnival Index (FI), the graphical analysis of the residuals and through the Shapiro-Wilk, Breusch- Pagan and Durbin Watson tests. The database came from an experiment with 15 Eucalyptus spp. clones conducted at the Experimental Station of the Agricultural Research Institute of Pernambuco – IPA, located in the municipality of Araripina - PE. Through a completely random sampling process 75 trees were selected, in which were determined the fresh weight and leaf samples collected were samples of leaf, branch, bark and bole to determine the average wood density, biomass and carbon content. The most productive clone in terms of biomass and carbon was the hybrid E. urophylla natural crossing. The average plant biomass accumulation was 59.64 t h−1 and the amount of carbon 24.96 t h−1. The adjustment of the regression models showed that each partition presented particular behavior of dry biomass production and total carbon. It was not possible to select a common model that represents all of parts of the trees. For the variable biomass, the models of Schumacher and Hall, Spurr, the logistics and the exponential model 11 presented the best fits. For the amount of organic carbon the models 6 and exponential 11 presented best results. / O objetivo deste trabalho foi quantificar e testar diferentes modelos de regressão, para estimar a biomassa e a quantidade de carbono das partes aéreas de clones de Eucalyptus plantados na região semiárida nordestina, e selecionar as melhores equações com base no R2 aj, nos critérios de informação de Akaike (AIC), no Índice de Furnival (IF), pela análise gráfica dos resíduos e por meio dos testes de Shapiro-Wilk, Breusch-Pagan e Durbin-Watson. A base de dados foi proveniente de um experimento com 15 clones de Eucalyptus spp. realizado na Estação Experimental da Empresa Pernambucana de Pesquisa Agropecuária – IPA, localizado no município de Araripina – PE. Por meio do processo de amostragem inteiramente aleatória foram cubadas 75 árvores, nas quais se determinaram os pesos frescos, bem como foram coletadas amostras de folhas, galhos, casca e fuste para determinação da densidade média da madeira, biomassa e teor de carbono. O clone mais produtivo em termo de biomassa e carbono foi o híbrido de E. urophylla cruzamento natural. O acúmulo de biomassa médio da plantação foi de 59,64 t h−1 e da quantidade de carbono 24,96 t h h−1. No ajuste dos modelos de regressão, verificou-se que cada partição apresentou comportamento particular de produção de biomassa seca, carbono total, não sendo possível selecionar um modelo comum que representasse todas elas. Para a variável biomassa o os modelos de Shumarcher e Hall, de Spurr, o logístico e o exponencial modelo 11 foram os que melhor se ajustaram. Para a quantidade de carbono orgânico o modelo 6 e o exponencial 11 se ajustaram a maior parte dos componentes aéreos.

Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009. / Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) / Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) / Made available in DSpace on 2013-06-03T19:20:55Z (GMT). No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) Previous issue date: 2009 / Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto. / In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data can be measured and it can be verified that the most influent variables affecting the SD are the maximum temperature and the relative humidity. After verifying this result, statistical models were developed aiming to calculate, based on the environmental data provided by the meteorological database and without waste of time and money, the approximate averages of SD for each seasonal station of the south region of Brazil, thus providing a better estimative of life-cycle for concrete structures.

A quantificação da evapotranspiração é uma tarefa essencial para a determinação do balanço hídrico em uma bacia hidrográfica e para o estabelecimento do déficit hídrico de uma cultura. Nesse sentido, o presente trabalho aborda a análise da evapotranspiração de referência (ETo) para a região de Jaboticabal-SP. O comportamento do fenômeno na região foi estudado a partir da interpretação de dados de uma bateria de 12 lisímetros de drenagem (EToLis) e estimativas teóricas por 10 equações diferentes disponíveis na literatura. A análise estatística de correlação indica que as estimativas da ETo por equações teóricas comparadas à EToLis medida em lisímetro de drenagem não apresentaram bons índices de comparação e erro. Admitindo que a operação dos lisímetros não permitiu a determinação da ETo com boa confiabilidade, propôs-se um ajuste local das demais metodologias de estimativa da ETo, através de auto-regressão (AR) dos ruídos destas equações em comparação com uma média anual estimada pela equação de Penman-Monteith (EToPM), tomada como padrão, em períodos quinzenal e mensal. O ajuste através de regressão linear simples também foi analisado. Os resultados obtidos indicam que a radiação efetiva é a variável climática de maior importância para o estabelecimento da ETo na região. A estimativa pela equação de Penman-Monteith apresentou excelente concordância com as equações de Makkink (1957) e do balanço de energia. Os ajustes locais propostos apresentaram excelentes resultados para a maioria das equações testadas, dando-se destaque às equações da radiação solar FAO-24, de Makkink (1957), de Jensen-Haise (1963), de Camargo (1971), do balanço de radiação, de Turc (1961) e de Thornthwaite (1948). O ajuste por regressão linear simples é de mais fácil execução e apresentou excelentes resultados. / The quantification of the evapotranspiration is an essential task for the determination of the water balance in a watershed and for the establishment of the culture´s water deficit. Therefore, the present work describes the analysis of the reference evapotranspiration (ETo) for the region of Jaboticabal-SP. The phenomenon behavior in the region was studied based on the interpretation of 12 drainage lysimeters data (EToLis) and on theoretical estimates for 10 different equations available in the Literature. An statistical analysis indicated that the theoretical ETo estimates compared with the EToLis did not present good indices of comparison and error. Admitting that the lysimeters operation did not allow a reliable ETo determination, a local adjustment of the theoretical methodologies for ETo estimate was considered. An auto-regression (AR) of the noises of these equations in comparison with the annual average estimate for the Penman-Monteith equation (EToPM), taken as standard, has been performed in fortnightly and monthly periods. The adjustment through simple linear regression has also been analyzed. The obtained results indicate that the effective radiation is the most important climatic variable for the establishment of the ETo in the region. The Penman-Monteith estimate presented excellent correlation to the estimates by Makkink (1957) equation and the energy balance. The local adjustments presented excellent results for the majority of the tested equations, specially for the solar radiation FAO-24, Makkink (1957), Jensen-Haise (1963), Camargo (1971), radiation balance, Turc (1961) and Thornthwaite (1948) equations. The adjustment by simple linear regression is of easier execution and also presented excellent results.

Predictive anisotropy is a phenomenon referring to unequal predictability of surface wind components in different directions. This study addresses the question of whether predictive anisotropy resulting from statistical prediction is influenced by physical factors or by types of regression methods (linear vs nonlinear) used to construct the statistical prediction. A systematic study of statistical predictability of surface wind components at 2109 land stations across the globe is carried out. The results show that predictive anisotropy is a common characteristic for both linear and nonlinear statistical prediction, which suggests that the type of regression method is not a major influential factor. Both strong predictive anisotropy and poor predictability are more likely to be associated with wind components characterized by relatively weak and non-Gaussian variability and in areas characterized by surface heterogeneity. An idealized mathematical model is developed separating predictive signal and noise between large-scale (predictable) and local (unpredictable) contributions to the variability of surface wind, such that small signal-to-noise ratio (SNR) corresponds to low and anisotropic predictability associated with non-Gaussian local variability. The comparison of observed and simulated statistical predictability by Regional Climate models (RCM) and reanalysis in the Northern Hemisphere indicates that small-scale processes that cannot be captured well by RCMs contribute to poor predictability and strong predictive anisotropy in observations. A second idealized mathematical model shows that spatial variability in specifically the minimum directional predictability, resulting from local processes, is the major contributor to predictive anisotropy. / Graduate