Traditional stochastic approaches for synthetic generation of weather variables often assume a prior functional form for the stochastic process, are often not capable of reproducing the probabilistic structure present in the data, and may not be uniformly applicable across sites. In an attempt to find a general framework for stochastic generation of weather variables, this study marks a unique departure from the traditional approaches, and ushers in the use of data-driven nonparametric techniques and demonstrates their utility. Precipitation is one of the key variables that drive hydrologic systems and hence warrants more focus . In this regard, two major aspects of precipitation modeling were considered: (I) resampling traces under the assumption of stationarity in the process, or with some treatment of the seasonality, and (2) investigations into interannual and secular trends in precipitation and their likely implications. A nonparametric seasonal wet/dry spell model was developed for the generation of daily precipitation. In this the probability density functions of interest are estimated using non parametric kernel density estimators. In the course of development of this model, various nonparametric density estimators for discrete and continuous data were reviewed, tested, and documented, which resulted in the development of a nonparametric estimator for discrete probability estimation. Variations in seasonality of precipitation as a function of latitude and topographic factors were seen through the non parametric estimation of the time-varying occurrence frequency. Nonparametric spectral analysis, performed on monthly precipitation, revealed significant interannual frequencies and coherence with known atmospheric oscillations. Consequently, a non parametric, nonhomogeneous Markov chain for modeling daily precipitation was developed that obviated the need to divide the year into seasons. Multivariate nonparametric resampling technique from the nonparametrically fitted probability density functions, which can be likened to a smoothed bootstrap approach, was developed for the simulation of other weather variables (solar radiation, maximum and minimum temperature, average dew point temperature, and average wind speed). In this technique the vector of variables on a day is generated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the current day generated from the wet/dry spell model.

Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-10-16T11:30:38Z No. of bitstreams: 1 ENYEDJA KERLLY MARTINS DE ARAÚJO CARVALHO - TESE (PPGRN) 2018.pdf: 1578519 bytes, checksum: f04ba6184fedb18c9ab04049246487a8 (MD5) / Made available in DSpace on 2018-10-16T11:30:38Z (GMT). No. of bitstreams: 1 ENYEDJA KERLLY MARTINS DE ARAÚJO CARVALHO - TESE (PPGRN) 2018.pdf: 1578519 bytes, checksum: f04ba6184fedb18c9ab04049246487a8 (MD5) Previous issue date: 2018-02-27 / CNPq / As variáveis climáticas têm gerado preocupação crescente quanto aos potenciais efeitos à saúde humana, especialmente aqueles relacionados às doenças transmissíveis, pois constituem importante causa de morbimortalidade e afligem milhões de pessoas em diferentes regiões do mundo, especialmente em países subdesenvolvidos. Nesse sentido, a presente pesquisa tem por objetivo analisar a influencia das variáveis meteorológicas (precipitação, temperatura e umidade relativa do ar) e o número de casos acometidos de doenças respiratórias (asma e pneumonia) nos municípios de Campina Grande, PB e Patos, PB, no período de 1998 a 2016. Para tanto, foi realizado um estudo epidemiológico, retrospectivo e descritivo com abordagem quantitativa. Os dados das variáveis meteorológicas adotados nesta pesquisa foram obtidos nas estações meteorológicas do Estado da Paraíba, disponíveis no Instituto Nacional de Meteorologia (INMET). Os dados de internações hospitalares por asma e pneumonia foram obtidos no Sistema de Informações hospitalares (SIH/SUS), disponibilizados pelo Departamento de Informação do Sistema Único de Saúde (DATASUS). O tratamento estatístico utilizado foi composto pelas medidas de associação, correlação de Pearson (r) e pelo Coeficiente de Determinação (R²) para verificar a correlação entre os casos de asma e pneumonia com as variáveis meteorológicas (precipitação, temperatura e umidade do ar). As correlações obtidas constataram que os elementos climáticos interferem em boa medida para o aumento de internações dessas doenças, levando-se em consideração o aspecto da sazonalidade e os valores médios mensais da precipitação, temperatura e umidade do ar. Portanto, as maiores incidência de casos dessas internações hospitalares ocorreram entre os meses de abril a agosto, período em que ocorreram as menores temperaturas do ar e as maiores umidades relativas do ar. As principais contribuições configuram-se como fatores essenciais para compreender as consequências que as mudanças nas variáveis climáticas podem ocasionar na saúde da população dos municípios estudados. Como recomendação destaca-se a limitação do estudo que se relaciona às subnotificações das doenças, falta de oportunidade no registro, diagnósticos incorretos, dentre outros, que podem ocorrer junto às repartições responsáveis pelo fornecimento de informações de saúde. / Climatic variables generated increasing concern about the potential effects on human health, especially those related to communicable diseases, as they are an important cause of morbidity and mortality and afflict millions of people in different regions of the world, especially in underdeveloped countries. In this sense, the present research aims to analyze the influence of meteorological variables (precipitation, temperature and relative air humidity) and the number of cases of respiratory diseases (asthma and pneumonia) in the municipalities of Campina Grande, PB and Patos, PB , from 1998 to 2016. For that, a retrospective and descriptive epidemiological study was carried out with a quantitative approach. The data of the meteorological variables adopted in this research were obtained in the meteorological stations of the State of Paraíba, available at the National Institute of Meteorology (INMET). Data from hospital admissions for asthma and pneumonia were obtained from the Hospital Information System (SIH / SUS), made available by the Department of Information of the National Health System (DATASUS). The statistical treatment was composed of the association measures, Pearson Correlation (r) and the Coefficient of Determination (R²) to verify the correlation between the cases of asthma and pneumonia with the meteorological variables (precipitation, temperature and humidity of the air). The correlations obtained verified that the climatic elements interfere to a great extent for the increase of hospitalizations of these diseases, taking into account the aspect of the seasonality and the average monthly values of precipitation, temperature and humidity of the air. Therefore, the highest incidence of hospitalizations occurred between April and August, when the lowest air temperatures and the highest relative air humidity occurred. The main contributions are essential factors to understand the consequences that changes in climatic variables can cause in the health of the population of the studied municipalities. As a recommendation, we highlight the limitation of the study that relates to underreporting of diseases, lack of opportunity in the registry, incorrect diagnoses, among others, that may occur with the offices responsible for providing health information.

Les générateurs stochastiques de temps sont des modèles numériques capables de générer des séquences de données climatiques de longueur souhaitée avec des propriétés statistiques similaires aux données observées. Ces modèles sont de plus en plus utilisés en sciences du climat, hydrologie, agronomie. Cependant, peu de générateurs permettent de simuler plusieurs variables, dont les précipitations, en différents sites d’une région. Dans cette thèse, nous proposons un modèle original de générateur stochastique basé sur un champ gaussien multivarié spatio-temporel. Un premier travail méthodologique a été nécessaire pour développer un modèle de covariance croisée entièrement non séparable adapté à la nature spatio-temporelle multivariée des données étudiées. Cette covariance croisée est une généralisation au cas multivarié du modèle non séparable spatio-temporel de Gneiting dans le cas de la famille de Matérn. La démonstration de la validité du modèle et l’estimation de ses paramètres par maximum de vraisemblance par paires pondérées sont présentées. Une application sur des données climatiques démontre l’intérêt de ce nouveau modèle vis-à-vis des modèles existants. Le champ gaussien multivarié permet la modélisation des résidus des variables climatiques (hors précipitation). Les résidus sont obtenus après normalisation des variables par des moyennes et écarts-types saisonniers, eux-mêmes modélisés par des fonctions sinusoïdales. L’intégration des précipitations dans le générateur stochastique nécessite la transformation d’une composante du champ gaussien par une fonction d’anamorphose. Cette fonction d’anamorphose permet de gérer à la fois l’occurrence et l’intensité des précipitations. La composante correspondante du champ gaussien correspond ainsi à un potentiel de pluie, corrélé aux autres variables par la fonction de covariance croisée développée dans cette thèse. Notre générateur stochastique de temps a été testé sur un ensemble de 18 stations réparties en zone à climat méditerranéen (ou proche) en France. La simulation conditionnelle et non conditionnelle de variables climatiques journalières (températures minimales et maximales, vitesse moyenne du vent, rayonnement solaire et précipitation) pour ces 18 stations soulignent les bons résultats de notre modèle pour un certain nombre de statistiques / Stochastic weather generators are numerical models able to simulate sequences of weather data with similar statistical properties than observed data. However, few of them are able to simulate several variables (with precipitation) at different sites from one region. In this thesis, we propose an original model of stochastic generator based on a spatio-temporal multivariate Gaussian random field. A first methodological work was needed to develop a completely non separable cross-covariance function suitable for the spatio-temporal multivariate nature of studied data. This cross-covariance function is a generalization to the multivariate case of spatio-temporal non-separable Gneiting covariance in the case of the family of Matérn. The proof of the validity of the model and the estimation of its parameters by weighted pairwise maximum likelihood are presented. An application on weather data shows the interest of this new model compared with existing models. The multivariate Gaussian random field allows the modeling of weather variables residuals (excluding precipitation). Residuals are obtained after normalization of variables by seasonal means and standard deviations, themselves modeled by sinusoidal functions. The integration of precipitation in the stochastic generator requires the transformation of a component of the Gaussian random field by an anamorphosis function. This anamorphosis function can manage both the occurrence and intensity of precipitation. The corresponding component of the Gaussian random field corresponds to a rain potential, correlated with other variables by the cross-covariance function developed in this thesis. Our stochastic weather generator was tested on a set of 18 stations distributed over the Mediterranean area (or close) in France. The conditional and non-conditional simulation of daily weather variables (maximum and minimum temperature, average wind speed, solar radiation and precipitation) for these 18 stations show good result for a number of statistics.

The present work had the objective of identifying the correlation between the temporal series of incidence of dengue cases and meteorological variables in the city of Maceió, Alagoas, through the investigation of periodicity variations in the whole time series of cases of dengue incidence and Of the temporal series of the time variables and of the detection of the corresponding time delay periods of the meteorological variables that correlate with the incidence of dengue cases. For the analysis and understanding of these aspects, real monthly data were used for the meteorological variables precipitation, cloudiness, relative air humidity, maximum temperature, average temperature, minimum air temperature, insolation, maximum speed and average wind, selected between January From December 1998 to December 2015 at Maceió Climatological Automatic Station, from INMET's BDMEP. For the same study period, data were also obtained on the number of notified cases of dengue in the municipality, available free of charge through the TABNET / DATASUS database. To identify the dominant periodicity in the signals of the time series of the study, continuous wavelet analysis was used. In order to investigate the relation between the time series, the criterion of crossed wavelet, wavelet coherence and phase angle was used. It was found that all variables presented individual dominant peaks with a scale of approximately 10 to 14 months. However, the cloudiness and insolation variables also showed concentrated energy in a scale less than 6 months. All cross-power spectra also showed dominant periodicity with approximately annual scale, that is, both the series of dengue incidence and the series of meteorological variables had the same high energy. It is worth noting that the variables precipitation, cloudiness and average air temperature also presented periodicity on a scale of 5 to 7 months. When the approximately 12-month scale was analyzed in correlated power spectra, the cloudiness variable, maximum wind speed, mean wind velocity and sunshine did not show a positive influence on the change in dengue incidence, that is, they did not increase the number of disease. Overall, when analyzed at approximately annual scale, the increase in the incidence of dengue cases in Maceió presented a temporal delay of 3 weeks when correlated to precipitation, temporal delay of 6 to 11 weeks relative to relative humidity, 15 weeks when Correlated to the maximum air temperature, 12 to 15 weeks in relation to the average air temperature, and a time delay of 9 to 14 weeks when correlated to the minimum air temperature. Thus, the increase in the occurrence of dengue cases is strictly associated with the duration of the dominant periodicity of precipitation and the maximum temperature of the air, being placed as indicators of dengue cases in this scale. However, it is important to note that for scales less than 6 months, the meteorological variables cloudiness and sunshine are indicated as indicators that presented good results, with the incidence of dengue cases presenting a temporal delay of approximately 5 weeks in relation to these two meteorological variables. / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O presente trabalho teve o objetivo de identificar a correlação entre as séries temporais de incidência de casos de dengue e de variáveis meteorológicas na cidade de Maceió, Alagoas, através da investigação das variações da periodicidade em toda a série temporal dos casos de incidência da dengue e das séries temporais das variáveis temporais e, da detecção dos períodos de atraso de tempo correspondentes das variáveis meteorológicas que se correlacionam com a incidência de casos da dengue. Para a análise e compreensão destes aspectos foram utilizados dados mensais reais das variáveis meteorológicas precipitação, nebulosidade, umidade relativa do ar, temperatura máxima, temperatura média, temperatura mínima do ar, insolação, velocidade máxima e média do vento, selecionados entre o período de janeiro de 1998 a dezembro de 2015 da Estação Automática Climatológica de Maceió, provenientes do BDMEP do INMET. Para o mesmo período de estudo, também foram obtidos dados sobre o número de casos notificados de dengue no município, disponibilizados gratuitamente através de banco de dados TABNET/DATASUS. Para identificar a periodicidade dominante nos sinais das séries temporais do estudo, foi utilizada a análise wavelet contínua. Para investigar a relação entre as séries temporais, foi utilizado o critério da wavelet cruzada, da coerência wavelet e do ângulo de fase. Foi constatado que todas as variáveis apresentaram picos dominantes individuais com escala de aproximadamente 10 a 14 meses. Entretanto, as variáveis nebulosidade e insolação apresentaram também energia concentrada em escala menor que 6 meses. Todos os espectros de potência cruzada mostraram também periodicidade dominante com escala aproximadamente anual, ou seja, tanto a série de incidência de dengue quanto as séries das variáveis meteorológicas tiveram mesma alta energia. Vale observar que as variáveis precipitação, nebulosidade e a temperatura média do ar apresentaram também periodicidade em escala de 5 a 7 meses. Quando analisada a escala de aproximadamente 12 meses nos espectros de potência correlacionada, a variável nebulosidade, velocidade máxima do vento, velocidade média do vento e insolação não mostraram influência positiva na mudança de incidência de dengue, ou seja, não aumentaram o número de casos da doença. No geral, quando analisada a escala aproximadamente anual, o aumento da incidência de casos de dengue em Maceió apresentou um atraso temporal de 3 semanas quando correlacionada à precipitação, atraso temporal de 6 a 11 semanas com relação à umidade relativa do ar, 15 semanas quando correlacionada à temperatura máxima do ar, 12 a 15 semanas com relação à temperatura média do ar, e um atraso temporal de 9 a 14 semanas quando correlacionada à temperatura mínima do ar. Desse modo, o aumento da ocorrência de casos de dengue está estritamente associado com a duração da periodicidade dominante da precipitação e da temperatura máxima do ar, sendo colocadas como indicadores de casos de dengue nessa escala. Entretanto, é importante ressaltar que para escalas menores que 6 meses, as variáveis meteorológicas nebulosidade e insolação são apontadas como indicadores que apresentaram bons resultados, com a incidência de casos de dengue apresentando um atraso temporal de aproximadamente 5 semanas em relação à estas duas variáveis meteorológicas.