During the first three-and-a-half years of the Tropical Rainfall Measuring Mission (TRMM), the TRMM satellite operated at a nominal altitude of 350 km. To reduce drag, save maneuvering fuel, and prolong the mission lifetime, the orbit was boosted to 403 km in August 2001. The change in orbit altitude produced small changes in a wide range of observing parameters, including field-of-view size and viewing angles. Due to natural climatic variability, it is not possible to evaluate possible changes in precipitation retrievals from the satellite data alone. We estimate changes in TRMM Microwave Imager (TMI) and the Precipitation Radar (PR) precipitation retrievals due to the orbit boost by comparing them with surface rain gauges on ocean buoys operated by the NOAA Pacific Marine Environment Laboratory (PMEL). For each rain gauge, we compute the bias between the satellite and the gauge for pre- and post-boost time periods. For the TMI, the satellite is biased ~12% low relative to the gauges during the pre-boost period and ~1.5% low during the post-boost period. The mean change in bias relative to the gauges is approximately 0.4 mm day^-1. The PR is biased significantly low relative to the gauges during both boost periods. The change in bias is rain rate dependent, with larger changes in areas with higher mean precipitation rates.

The tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data are used in this study to reveal diurnal variations of precipitation over the Tropics (30◦S − 30◦N) from January, 1998, to December 2002. The TMI data were used for the regions over oceans and islands and the PR data was used over continents. The observations are sorted regionally to examine the difference in diurnal cycle of rainfall over ocean, island, and continental regions. The rain rate is averaged over individual two hour intervals of local time in each region to include more observations in order to reduce the sampling error. F-test is used to determine those regions whose diurnal cycle is detected at the 95% confidence level. In most oceanic regions there is a maximum at 0400 LST - 0700 LST. The amplitude of diurnal variation over ocean regions with small total rain is a little higher than that of the ocean regions with heavy total rain. The diurnal cycle peaks at 0700 LST - 0800 LST over islands with rainfall variation similar to surrounding oceanic regions. A maximum at 1400 LST - 1500 LST was found in areas over continents with heavy total rain, while the maximum occured at 1900 LST - 2100 LST over continents with lesser total rain. The amplitudes of variation over continents with heavy total rain and with small total rain do not show significant differences. The diurnal cycle in in JJA (June, July, August) and DJF (December, January, February) varies with latitude over continents. A seasonal cycle of diurnal cycle can also be found in some oceanic regions. The diurnal cycle annual change is not evident over continents, while the diurnal cycle annual change over oceans exists in some regions. Island regions in this paper exhibit no evident seasonal and annual diurnal change.

The relationship between precipitation and large-flow is important to understand and characterize in the climate system. We examine statistical relationships between the Tropical Rainfall Measurement Mission (TRMM) 3B42 gridded precipitation and large-scale ow variables in the Tropics for 2000{2007. These variables include NCEP/NCAR Re-analysis sea surface temperatures (SSTs), vertical temperature pro files, omega, and moist static energy, as well as Atmospheric Infrared Sounder (AIRS) vertical temperatures and QuikSCAT surface divergence. We perform correlation analysis, empirical orthogonal function analysis, and logistic regression analysis on monthly, pentad, daily and near-instantaneous time scales. Logistic regression analysis is able to incorporate the non-linear nature of precipitation in the relation- ship. Flow variables are interpolated to the 0.25 degrees TRMM 3B42 grid and examined separately for each month to o set the effects of the seasonal cycle. January correlations of NCEP/NCAR Re-analysis SSTs and TRMM 3B42 precipitation have a coherent area of positive correlations in the Western and Central Tropical Pacific on all time scales. These areas correspond with the South Pacific Convergence Zone (SPCZ) and the Inter Tropical Convergence Zone (ITCZ). 500mb omega is negatively correlated with TRMM 3B42 precipitation across the Tropics on all time scales. QuikSCAT divergence correlations with precipitation have a band of weak and noisy correlations along the ITCZ on monthly time scales in January. Moist static energy, calculated from NCEP/NCAR Re-analysis has a large area of negative correlations with precipitation in the Central Tropical Pacific on all four time scales. The first few Empirical Orthogonal Functions (EOFs) of vertical temperature profiles in the Tropical Pacific have similar structure on monthly, pentad, and daily timescales. Logistic regression fit coefficients are large for SST and precipitation in four regions located across the Tropical Pacific. These areas show clear thresholded behavior. Logistic regression results for other variables and precipitation are less clear. The results from SST and precipitation logistic regression analysis indicate the potential usefulness of logistic regression as a non-linear statistic relating precipitation and certain ow variables.

Les précipitations présentent un impact socio-économique très important notamment dans les régions où les ressources hydriques sont rares et où les évènements pluvieux ont un caractère torrentiel. Plusieurs modèles hydrologiques ont vu le jour dans le but de prédire les débits qui sont d'une grande utilité pour la conception des barrages ou pour la prévision des inondations. Or, pour fournir des simulations de débit très proches de la réalité, ces modèles ont besoin de données pluviométriques acquises à grande résolution spatio-temporelle. De ce fait, il est intéressant pour ces modèles hydrologiques de recourir aux pluies estimées par les radars météorologiques satellitaires et au sol. Néanmoins, ces données doivent être validées avant toute utilisation. Le principal problème rencontré dans la validation des données radar météorologiques réside dans la grande différence d'échelle spatio-temporelle entre les données radar et les données fournies par les stations pluviométriques. Cette différence d'échelle ne peut pas être prise en compte par les méthodes conventionnelles de validation qui se limitent à calculer le coefficient de corrélation et à élaborer une relation régulière entre les deux types de données. L'objectif général de ce travail de recherche est la validation des pluies de surface estimées aussi bien par le radar satellitaire de TRMM que par le radar au sol NEXRAD WSR-88D afin d'améliorer l'échelle spatiale des simulations hydrologiques. Les données au sol utilisées pour la validation sont celles issues des stations pluviométriques du CNA ( Comisión Nacional del Agua ) et de la NOAA (National Oceanic and Atmospheric Administration ) localisées dans la région de Rio Escondido au Nord-Est du Mexique. Cette validation est réalisée en calculant les coefficients de corrélation entre les données de précipitation radar et les données au sol, en vérifiant l'existence de la propriété d'invariance d'échelle, et en évaluant la fiabilité des sorties du modèle hydrologique CEQUEAU utilisant les données radar comme entrée. La contribution principale de ce travail est d'utiliser la dimension fractale du champ de pluie comme outil de validation des estimations pluviométriques. Les résultats ont confirmé que les précipitations de surface estimées par le radar du satellite TRMM ne présentent pas les mêmes caractéristiques spatiales que celles des mesures fournies par les pluviomètres. Contrairement aux précipitations estimées par TRMM, les données du radar au sol sont compatibles avec un comportement d'échelle fractal et traduisent la variabilité intrinsèque du champ de pluie. C'est pourquoi elles ont été utilisées comme données d'entrée dans le modèle hydrologique CEQUEAU. Il en résulte des débits simulés avec un coefficient de Nash variant de -2,59 à 0,97. L'intérêt de ce résultat est qu'il montre l'utilité des données radar au sol pour les simulations des modèles hydrologiques et ce, particulièrement dans les zones où les pluies sont convectives, donc fortement variables et où les réseaux de pluviographes peuvent être insuffisants ou mal répartis. Cependant, la qualité de la simulation hydrologique dépend de l'échelle temporelle considérée et de l'évènement pluvieux choisi. Ainsi, ce travail a permis d'appliquer plusieurs méthodes de validation aux estimations radar des pluies de surface et de démontrer la pertinence de considérer les différences d'échelles spatio-temporelles dans la validation de ces données estimées.

This study evaluates the simulation of tropical precipitation by the Community Climate Model, Version 3, developed at the National Center for Atmospheric Research. For an evaluation of the annual cycle of precipitation, monthly-mean precipitation rates from an ensemble of CCM3 simulations are compared to those computed from observations of the TRMM satellite over a 44-month period. On regional and sub-regional scales, the comparison fares well over much of the Eastern Hemisphere south of 10◦S and over South America. However, model - satellite differences are large in portions of Central America and the Caribbean, the southern tropical Atlantic, the northern Indian Ocean, and the western equatorial and southern tropical Pacific. Since precipitation in the Tropics is the primary source of latent energy to the general circulation, such large model - satellite differences imply large differences in the amount of latent energy released. Differences are seasonally-dependent north of 10◦N, where model wet biases occur in realistic wet seasons or model-generated artificial wet seasons. South of 10◦N, the model wet biases exist throughout the year or have no recognizable pattern. For an evaluation of the diurnal cycle of precipitation, hourly-averaged precipitation rates from the same ensemble of simulations and for the same 44-month period are compared to observations from the Tropical Rainfall Measuring Mission (TRMM) satellite. Comparisons are made for 15◦ longitude ?? 10◦ latitude boxes and for larger geographical areas within the Tropics. The temporally- and spatially-averaged hourly precipitation rates from CCM3 and from TRMM are fit to the diurnal harmonic by the method of linear leastsquares regression, and the phases and the amplitudes of the diurnal cycles are compared. The model??s diurnal cycle is too strong over major land masses, particularly over South America (by a factor of 3), and is too weak over many oceans, particularly the northwestern Tropical Pacific (by a factor of 2). The model-satellite phase differences tend to be more homogeneous. The peak in the daily precipitation in the model consistently precedes the observations nearly everywhere. Phase differences are large over Australia, Papua New Guinea, and Saharan Africa, where CCM3 leads TRMM by 4 hours, 5 to 6 hours, and 9 to 11 hours respectively. A model sensitivity experiment shows that increasing the convective adjustment time scale in the model??s deep convective parameterization reduces its positive amplitude bias over land regions but has no effect on the phase of the diurnal cycle.

O uso da técnica de assimilação de dados meteorológicos é extremamente importante para a correção de imprecisões nos dados que compõem as condições iniciais e de fronteira dos modelos de previsão do tempo. Neste trabalho, faz-se uso da técnica de assimilação de dados 3DVAR contida no modelo de mesoescala WRF (Weather Research and Forecasting), o objetivo principal do trabalho é analisar o impacto da assimilação de dados meteorológicos de diversas fontes de dados (GTS – Sistema Global de Telecomunicações, estações automáticas, dados radar) no modelo WRF. Para analisar a consistência da assimilação de dados no WRF verificou-se a diferença entre a análise com e sem assimilação de dados. Confirmando a consistência da mesma, foram realizados os procedimentos necessários para gerar os prognósticos com assimilação de dados para cada caso individualmente. Os experimentos com assimilação de dados foram realizados para cada tipo de dado e em conjunto, possibilitando assim fazer uma análise do impacto que cada dado tem na previsão. Os resultados foram comparados entre si espacialmente utilizando dados do modelo global GFS (Global Forecast System) e satélite da Missão de Medida da Chuva Tropical (TRMM). A variável da precipitação acumulada foi comparada e validada espacialmente com os dados do TRMM, constatou-se para o caso do mês de janeiro uma superestimação dos valores acumulados para algumas regiões e para o caso do mês de abril uma subestimação, isso se deve ao fato da frequência temporal dos dados do satélite TRMM, pois provavelmente elas não foram compatíveis com o horário das precipitações. Quando comparado com o volume de chuva pontual com os dados da estação automática a maioria dos processamentos mostrou-se eficaz. Também no estudo de caso ocorrido no mês de janeiro a inserção de dados assimilados possibilitou uma melhora na intensidade e localização da célula convectiva. As variáveis da temperatura e do vento foram comparadas espacialmente com as análises do modelo GFS. A variável da temperatura ora apresentou valores superiores, ora inferiores ao modelo GFS, mesmo assim os resultados foram satisfatórios, uma vez que, foi possível simular temperaturas superiores antes da passagem do sistema e inferiores após a passagem do mesmo. Para o campo de vento houve uma pequena discrepância em todas as simulações em relação a magnitude, porém a direção do vento foi plotada de forma coerente, simulando até o ciclone presente no caso do mês de abril. Para o perfil vertical da temperatura e temperatura do ponto de orvalho o impacto da assimilação de dados foi pequeno, porém ambas as simulações representaram de forma coesa os perfis quando comparados com o perfil observado. Em suma, o estudo comprova que, embora se tenha algumas incoerências assimilação 3DVAR contribui de modo significativo nas previsões do tempo do modelo WRF. / The use of meteorological data assimilation technique is extremely important for the correction of the imprecisions of observational data for the initial and boundary conditions of weather forecasting models. In the present work it is used the 3DVAR data assimilation technique of the mesoscale model WRF system (Weather Research and Forecasting) aiming the analysis of the impact of the assimilation of meteorological data from several data sources (GTS - Global Telecommunication System, automatic surface stations network and radar) in the WRF model. To analysis the consistency of the data in the WRF assimilation it has been gathered the difference between analysis, with and without data assimilation. Confirming its consistency the procedures required, to generate predictions with data assimilation for each individual case were performed. The data assimilation experiments were performed for each data type as well as including all of them allowing, therefore, the analysis of the impact of each over the forecast. The results were compared and validated using data from the spatially global forecasting model GFS (Global Forecast System), satellite and the mission of the Tropical Rain Measurement (TRMM) data. The cumulative rainfall variable was compared spatially with data from TRMM, where it has been observed, in the case of January, an overestimation of the accumulated values for some regions and an underestimation for the case of April. These have been occurred because of temporal frequency of the TRMM satellite data - which probably because were not compatible with the precipitation time occurrence. Comparison between the accumulated precipitation with data from automatic station presented mostly effective results. Also, in the case study of the January with assimilated data, produced an improvement in the intensity as well as in the location of the convective cell. The wind and temperature variables were compared with the spatially GFS’s analysis. The higher temperature variable values presented alternated, from higher and lower values compared to the GFS results. The results were nevertheless unsatisfactory, because the simulated temperatures presented prior to passing the frontal system and after passing it. For the wind field there was a small discrepancy in all simulations regarding the magnitude, but the wind direction was plotted consistently simulating up to the present in the case of April cyclone. For the vertical profiles of temperature and dew point temperature the impact of data assimilation was small, but both simulations made represented good profiles, compared with the observed values. In summary, the study shows that, although there were some inconsistencies, compared with the observations, the 3DVAR assimilation contributes significantly to WRF model forecasts.

O uso da técnica de assimilação de dados meteorológicos é extremamente importante para a correção de imprecisões nos dados que compõem as condições iniciais e de fronteira dos modelos de previsão do tempo. Neste trabalho, faz-se uso da técnica de assimilação de dados 3DVAR contida no modelo de mesoescala WRF (Weather Research and Forecasting), o objetivo principal do trabalho é analisar o impacto da assimilação de dados meteorológicos de diversas fontes de dados (GTS – Sistema Global de Telecomunicações, estações automáticas, dados radar) no modelo WRF. Para analisar a consistência da assimilação de dados no WRF verificou-se a diferença entre a análise com e sem assimilação de dados. Confirmando a consistência da mesma, foram realizados os procedimentos necessários para gerar os prognósticos com assimilação de dados para cada caso individualmente. Os experimentos com assimilação de dados foram realizados para cada tipo de dado e em conjunto, possibilitando assim fazer uma análise do impacto que cada dado tem na previsão. Os resultados foram comparados entre si espacialmente utilizando dados do modelo global GFS (Global Forecast System) e satélite da Missão de Medida da Chuva Tropical (TRMM). A variável da precipitação acumulada foi comparada e validada espacialmente com os dados do TRMM, constatou-se para o caso do mês de janeiro uma superestimação dos valores acumulados para algumas regiões e para o caso do mês de abril uma subestimação, isso se deve ao fato da frequência temporal dos dados do satélite TRMM, pois provavelmente elas não foram compatíveis com o horário das precipitações. Quando comparado com o volume de chuva pontual com os dados da estação automática a maioria dos processamentos mostrou-se eficaz. Também no estudo de caso ocorrido no mês de janeiro a inserção de dados assimilados possibilitou uma melhora na intensidade e localização da célula convectiva. As variáveis da temperatura e do vento foram comparadas espacialmente com as análises do modelo GFS. A variável da temperatura ora apresentou valores superiores, ora inferiores ao modelo GFS, mesmo assim os resultados foram satisfatórios, uma vez que, foi possível simular temperaturas superiores antes da passagem do sistema e inferiores após a passagem do mesmo. Para o campo de vento houve uma pequena discrepância em todas as simulações em relação a magnitude, porém a direção do vento foi plotada de forma coerente, simulando até o ciclone presente no caso do mês de abril. Para o perfil vertical da temperatura e temperatura do ponto de orvalho o impacto da assimilação de dados foi pequeno, porém ambas as simulações representaram de forma coesa os perfis quando comparados com o perfil observado. Em suma, o estudo comprova que, embora se tenha algumas incoerências assimilação 3DVAR contribui de modo significativo nas previsões do tempo do modelo WRF. / The use of meteorological data assimilation technique is extremely important for the correction of the imprecisions of observational data for the initial and boundary conditions of weather forecasting models. In the present work it is used the 3DVAR data assimilation technique of the mesoscale model WRF system (Weather Research and Forecasting) aiming the analysis of the impact of the assimilation of meteorological data from several data sources (GTS - Global Telecommunication System, automatic surface stations network and radar) in the WRF model. To analysis the consistency of the data in the WRF assimilation it has been gathered the difference between analysis, with and without data assimilation. Confirming its consistency the procedures required, to generate predictions with data assimilation for each individual case were performed. The data assimilation experiments were performed for each data type as well as including all of them allowing, therefore, the analysis of the impact of each over the forecast. The results were compared and validated using data from the spatially global forecasting model GFS (Global Forecast System), satellite and the mission of the Tropical Rain Measurement (TRMM) data. The cumulative rainfall variable was compared spatially with data from TRMM, where it has been observed, in the case of January, an overestimation of the accumulated values for some regions and an underestimation for the case of April. These have been occurred because of temporal frequency of the TRMM satellite data - which probably because were not compatible with the precipitation time occurrence. Comparison between the accumulated precipitation with data from automatic station presented mostly effective results. Also, in the case study of the January with assimilated data, produced an improvement in the intensity as well as in the location of the convective cell. The wind and temperature variables were compared with the spatially GFS’s analysis. The higher temperature variable values presented alternated, from higher and lower values compared to the GFS results. The results were nevertheless unsatisfactory, because the simulated temperatures presented prior to passing the frontal system and after passing it. For the wind field there was a small discrepancy in all simulations regarding the magnitude, but the wind direction was plotted consistently simulating up to the present in the case of April cyclone. For the vertical profiles of temperature and dew point temperature the impact of data assimilation was small, but both simulations made represented good profiles, compared with the observed values. In summary, the study shows that, although there were some inconsistencies, compared with the observations, the 3DVAR assimilation contributes significantly to WRF model forecasts.

O uso da técnica de assimilação de dados meteorológicos é extremamente importante para a correção de imprecisões nos dados que compõem as condições iniciais e de fronteira dos modelos de previsão do tempo. Neste trabalho, faz-se uso da técnica de assimilação de dados 3DVAR contida no modelo de mesoescala WRF (Weather Research and Forecasting), o objetivo principal do trabalho é analisar o impacto da assimilação de dados meteorológicos de diversas fontes de dados (GTS – Sistema Global de Telecomunicações, estações automáticas, dados radar) no modelo WRF. Para analisar a consistência da assimilação de dados no WRF verificou-se a diferença entre a análise com e sem assimilação de dados. Confirmando a consistência da mesma, foram realizados os procedimentos necessários para gerar os prognósticos com assimilação de dados para cada caso individualmente. Os experimentos com assimilação de dados foram realizados para cada tipo de dado e em conjunto, possibilitando assim fazer uma análise do impacto que cada dado tem na previsão. Os resultados foram comparados entre si espacialmente utilizando dados do modelo global GFS (Global Forecast System) e satélite da Missão de Medida da Chuva Tropical (TRMM). A variável da precipitação acumulada foi comparada e validada espacialmente com os dados do TRMM, constatou-se para o caso do mês de janeiro uma superestimação dos valores acumulados para algumas regiões e para o caso do mês de abril uma subestimação, isso se deve ao fato da frequência temporal dos dados do satélite TRMM, pois provavelmente elas não foram compatíveis com o horário das precipitações. Quando comparado com o volume de chuva pontual com os dados da estação automática a maioria dos processamentos mostrou-se eficaz. Também no estudo de caso ocorrido no mês de janeiro a inserção de dados assimilados possibilitou uma melhora na intensidade e localização da célula convectiva. As variáveis da temperatura e do vento foram comparadas espacialmente com as análises do modelo GFS. A variável da temperatura ora apresentou valores superiores, ora inferiores ao modelo GFS, mesmo assim os resultados foram satisfatórios, uma vez que, foi possível simular temperaturas superiores antes da passagem do sistema e inferiores após a passagem do mesmo. Para o campo de vento houve uma pequena discrepância em todas as simulações em relação a magnitude, porém a direção do vento foi plotada de forma coerente, simulando até o ciclone presente no caso do mês de abril. Para o perfil vertical da temperatura e temperatura do ponto de orvalho o impacto da assimilação de dados foi pequeno, porém ambas as simulações representaram de forma coesa os perfis quando comparados com o perfil observado. Em suma, o estudo comprova que, embora se tenha algumas incoerências assimilação 3DVAR contribui de modo significativo nas previsões do tempo do modelo WRF. / The use of meteorological data assimilation technique is extremely important for the correction of the imprecisions of observational data for the initial and boundary conditions of weather forecasting models. In the present work it is used the 3DVAR data assimilation technique of the mesoscale model WRF system (Weather Research and Forecasting) aiming the analysis of the impact of the assimilation of meteorological data from several data sources (GTS - Global Telecommunication System, automatic surface stations network and radar) in the WRF model. To analysis the consistency of the data in the WRF assimilation it has been gathered the difference between analysis, with and without data assimilation. Confirming its consistency the procedures required, to generate predictions with data assimilation for each individual case were performed. The data assimilation experiments were performed for each data type as well as including all of them allowing, therefore, the analysis of the impact of each over the forecast. The results were compared and validated using data from the spatially global forecasting model GFS (Global Forecast System), satellite and the mission of the Tropical Rain Measurement (TRMM) data. The cumulative rainfall variable was compared spatially with data from TRMM, where it has been observed, in the case of January, an overestimation of the accumulated values for some regions and an underestimation for the case of April. These have been occurred because of temporal frequency of the TRMM satellite data - which probably because were not compatible with the precipitation time occurrence. Comparison between the accumulated precipitation with data from automatic station presented mostly effective results. Also, in the case study of the January with assimilated data, produced an improvement in the intensity as well as in the location of the convective cell. The wind and temperature variables were compared with the spatially GFS’s analysis. The higher temperature variable values presented alternated, from higher and lower values compared to the GFS results. The results were nevertheless unsatisfactory, because the simulated temperatures presented prior to passing the frontal system and after passing it. For the wind field there was a small discrepancy in all simulations regarding the magnitude, but the wind direction was plotted consistently simulating up to the present in the case of April cyclone. For the vertical profiles of temperature and dew point temperature the impact of data assimilation was small, but both simulations made represented good profiles, compared with the observed values. In summary, the study shows that, although there were some inconsistencies, compared with the observations, the 3DVAR assimilation contributes significantly to WRF model forecasts.

A região amazônica desempenha papel fundamental na regulação do clima, tanto em escala regional quanto em escala global. A precipitação na região é bastante heterogênea, sobretudo devido à vasta extensão territorial da Amazônia. Dentre os sistemas responsáveis pela precipitação, alguns se destacam como eventos extremos de tempestades, como pode ser verificado em diversos estudos anteriores. Contudo, diferentemente das latitudes médias, na região tropical não há um conjunto de definições amplamente conhecido e difundido para identificação de tempo severo. O presente estudo busca identificar um critério para identificação de tempo severo na região amazônica a partir da base de dados Precipitation Features (PF) 1998 a 2012 - gerados e armazenados pela Universidade de Utah, com base nos dados do satélite TRMM. Além disso, identificar características sinóticas associadas ao ambiente de ocorrência destes eventos, através de composições com dados da reanálise CFSR-NCEP, bem como parâmetros importantes na identificação de tempestades. Utilizando o subconjunto PCTF do Nível 2 da base de dados PF, o critério estabelecido para identificação de casos severos compreende sistemas com: 80 pixels ou mais PCT85 GHz <250 K; 1 pixel ou mais com PCT85 GHz < 100 K; volume de chuva convectiva maior do que 1000 mm/h km2 e pelo menos um registro de raio. Comparando os sistemas selecionados pelo critério com os Sistemas Convectivos de Mesoescala já catalogados é possível notar que a distribuição sazonal é semelhante, embora as estações com maior número de casos sejam as estações de transição (primavera e outono, 429 e 223 casos respectivamente). Analisando as altas taxas de raios destes sistemas, fica evidente que o critério realmente seleciona casos severos. Com a região amazônica dividida em seis sub-regiões e os casos acumulados por trimestre (JFM, AMJ, JAS, OND) sub-região Southern Amazonia (SA) contabiliza o maior número de casos, com um total de 271 para o período do estudo, sendo OND o trimestre com maior ocorrência (135), o menor AMJ (29). O mês de outubro chama atenção para esta sub-região como o mês com maior número de casos, totalizando 59, dos quais 83% ocorrem a partir das 12 horas local. Estes casos foram investigados nas composições de reanálise, assim como os casos a partir de 12 horas local de outubro da sub-região Central Amazonia (CA). De forma geral: 1) SA tem maior área com cisalhamento médio mais intenso (8 m/s) do que CA, principalmente para 00Z, 06Z e 12Z; 2) valores médios de divergência positiva do vento em 200 hPa mostram-se mais significativos para CA do que para SA; 3) convergência do vento em 950 hPa é mais evidente para SA do que para CA e 4) CA é predominantemente mais úmida em baixos níveis do que SA. Histogramas com valores pontuais para cada um destes casos, em ambas as sub-regiões, são apresentados no intuito de auxiliar a identificação destes casos por previsores. O critério de identificação de tempo severo na Amazônia mostra-se eficiente, sendo o cisalhamento do vento entre 500-850 hPa e a convergência do vento em 950 hPa os como parâmetros mais importantes na região SA, onde há maior ocorrência de tempestades severas. / The Amazon region plays a key role in climate regulation, both at the regional scale and on a global scale. Rainfall in the region is very heterogeneous, mainly because of the vast size of the Amazon. Among the systems responsible for rainfall, some stand out as extreme storm events, as can be seen in many previous studies. However, unlike the mid-latitudes, in the tropical region there is no widely acknowledged set of conditions for severe weather identification. This study seeks to identify a criterion for identifying severe weather in the Amazon region from the database Precipitation Features (PF) - 1998-2012 - generated and stored by the University of Utah, based on the TRMM satellite data. This study will also attempt to identify synoptic features associated with the occurrence of these events through compositions using the reanalysis NCEP CFSR data. Using the PCTF subset of Level 2 of PF database, the criteria established for identifying severe cases include: 1) systems with 80 or more pixels PCT85 GHz <250 K; 2) systems with one or more pixel with PCT85 GHz <100 K; 3) systems with convective rain volume greater than 1000 km2 mm/h and 4) at least one record of lightning. Comparing the systems selected by this criterion with the Mesoscale Convective Systems already cataloged it can be seen that the seasonal distribution is similar, although the stations with the highest number of cases are the transition seasons (spring and fall, 429 and 223 cases, respectively). Analyzing high rates of rays found in these systems, it is clear that the criterion truly selects severe cases. With the Amazon region divided into six sub-regions and cases accumulated by quarter (JFM, AMJ, JAS, OND) South of the Amazon sub region (SA) accounts for the largest number of cases, with a total of 271 for the period of study, OND quarter with higher occurrence (135), the lowest AMJ (29). The month of October draws attention to this sub-region as the month with the highest number of cases, totaling 59, of which 83% occur after 12 local time. These cases have been investigated in compositions, as well as cases observed after 12 local time in October for Amazon Central subregion (CA). In general: 1) SA has larger area with average stronger shear (8 m/s) than AC, especially for 00Z, 06Z and 12Z; 2) average wind positive divergence values at 200 hPa were more significant for CA than for SA; 3) Wind convergence at 950 hPa is more obvious for SA than at CA and 4) is predominantly CA moster at low levels than SA. Histograms with specific values for each of these cases, both sub regions are presented in order to help identify predictors for these cases. The severe weather identification criterion in the Amazon proves efficient, while the wind shear between 500-850 hPa and wind convergence in 950 hPa stand out as important parameters in the SA region, where there is greater occurrence of severe storms.

Dans la zone intertropicale, les précipitations sont le principal marqueur climatique saisonnier et déterminent très largement l’hydrologie de surface et de nombreuses activités anthropiques. Le bassin amazonien est caractérisé par divers régimes régionaux de précipitations, dont la variabilité spatiale et temporelle est forte. De nombreux travaux ont montré que cette variabilité est liée à des forçages externes de large échelle, comme les températures de surface de l’océan. L’étude des précipitations dans cette région porte le plus souvent sur les tendances ou les extrêmes pluviométriques. En revanche, la détection d’années similaires constituant des sous-régimes régionaux et leur lien avec une configuration océano-atmosphérique particulière a été, jusqu’à présent, peu abordée. L’objectif principal de cette thèse est ainsi de créer une typologie des sous-régimes de précipitations régionaux dans le bassin amazonien et de les mettre en relation avec le contexte océano-atmosphérique pouvant en partie les expliquer. Dans ce but, des données issues de 205 pluviomètres répartis sur 5 pays du Bassin Amazonien ont été sélectionnées et soumises à une série de tests statistiques et de reconstruction. Cette thèse utilise également des données de nébulosité (Outgoing Longwave Radiation), de flux d’humidité et de température de surface de l’océan ainsi que des données satellitaires (TRMM3B42 version 7) qui permettent de compléter les informations sur la variabilité spatiale des pluies.Au sein de chacune des sept régions amazoniennes déterminées dans ce travail, deux à quatre sous-régimes de précipitations ont été détectés. Parmi les vingt-six sous-régimes, vingt sont associés à des anomalies de circulation des flux d’humidité et de température de surface des océans. Les sous-régimes de pluies de la moitié nord et les Andes de l’ouest du bassin sont le plus liés à des anomalies océaniques. De plus, comme cela est régulièrement décrit, des déficits ou excédents correspondent souvent à des phases El Niño ou La Niña, mais cette thèse met également en évidence le rôle important de l’Atlantique, en particulier sud, sur le déplacement de la ZCIT et sur les flux d’humidité ; et elle souligne également le lien entre la temporalité des événements océaniques et celle des anomalies de pluies.Le produit TRMM 3B42 V7 permet d’aller plus loin dans l’analyse de la variabilité spatiale intra-régionale des pluies de la région Nord-est du bassin amazonien et de relativiser la cohérence spatiale des sous-régimes de précipitations de cette région. / Precipitations are the main seasonal climate marker between the tropics and largely determine surface hydrolosy as well as many anthropogenic activities. The Amazon Basin is characterized by various regional rainfall patterns, whose spatial and temporal variability is high. Numerous studies have shown that this variability is related to large scale external forcing, such as sea surface temperatures. The analysis of precipitation in this region is generally related to trends or extreme of rainfall. However, the detection of similar years associated with regional sub-regimes and the analysis of their links with a specific ocean-atmosphere configuration has only been fewly addressed until now. The main objective of this thesis is to create a typology of regional precipitation sub-régimes in the Amazon Basin and to link them to ocean-atmosphere areas able to partly explain them. For that purpose data from 205 raingauges in 5 countries of the Amazon Basin were selected and submitted to a series of statistical tests and reconstruction. Outgoing longwave radiation, specific humidity, sea surface temperature, as well as satellite data (TRMM 3B42 version 7) were also used with the aim of improving the understanding of the spatial rainfall variability.Within each of the seven Amazon regions identified in this work, two to four precipitation sub-regimes were detected. Among the twenty six sub-regimes, twenty are associated with specific humidity and sea surface temperature anomalies. The precipitation sub-regimes of the northern half and the westernmost Andes of the Amazon Basin are most closely related to oceanic anomalies. Moreover, as previously described in the literature, reduction or surplus of rain often correspond to El Niño or La Niña phases, but this thesis also highlights the important role of the Atlantic, more specifically the southern part, on the move of the ITZC and on specific humidity. This work also stresses the link between the temporality of ocean events anomalies and rainfall anomalies.The TRMM 3B42 v7 product allows to enhance the analysis of the spatial variability of rainfall at the intra-regional scale of the North region of the Amazon Basin and to relativize the spatial coherence of its precipitation sub-regimes.