Os ciclones secundários são sistemas que ainda não são bem definidos e, assim, são fenômenos de difícil previsibilidade, necessitando de mais estudos para identificar os sinais que disparam seu desenvolvimento. Neste estudo realizou-se um levantamento climatológico e estudo numérico de ciclogênese secundária no sudoeste do Oceano Atlântico Sul com o objetivo de obter informações sobre a atuação destes sistemas e entender os processos dinâmicos envolvidos no seu desenvolvimento. Para o período entre 1980 e 2010, a climatologia mostra que uma média de 3,9 sistemas secundários se forma por ano no Oceano Atlântico Sul. Estes sistemas ocorrem com maior e menor frequência nos meses frios e quentes, respectivamente. Dois tipos distintos de ciclones secundários foram encontrados. TIPO1 que se forma a leste e na região da frente quente do ciclone primário. Estes sistemas se desenvolvem sob advecção quente nos baixos níveis e pouca influência de anomalias de vorticidade potencial (VP) de altos níveis; TIPO2 se desenvolve a oeste/noroeste do ciclone primário onde predomina forte advecção fria em baixos níveis. No entanto, fluxos de calor e umidade intensos contribuem para aquecer a baixa troposfera e em altos níveis são forçados por anomalias de VP. Simulações numéricas com o modelo Weather Research and Forecasting (WRF) indicam os fluxos de calor sensível e latente na superfície como mecanismos de intensificação dos ciclones secundários TIPO1 e TIPO2, sendo o fluxo de calor latente mais importante no abaixamento de pressão destes sistemas. Os experimentos numéricos mostram que o ciclone do TIPO2 não se desenvolve na ausência de anomalia de VP, enquanto que o TIPO1 se desenvolve mais fraco e atrasado no tempo. A análise por separação de fatores indica que a anomalia de VP e algum outro mecanismo não relacionado aos fatores avaliados nas simulações tiveram papel disparador no ciclone do TIPO1, enquanto a interação da anomalia de VP com os fluxos de superfície atuou como intensificador. No TIPO2, o desenvolvimento ocorreu unicamente pela atuação da anomalia de VP, a qual também agiu como um intensificador juntamente com os fluxos de calor e umidade, bem como os processos de interação entre estes dois fatores. / Secondary cyclones are systems that are not well defined yet and they are difficult to predict, requiring further studies to identify the signals that trigger their development. In this study we carried out a climatology and numerical study of secondary cyclogenesis over the southwestern South Atlantic Ocean in order to obtain information about these systems and understand the dynamic processes involved in its development. The climatology for the period 1980-2010 shows that an average of 3.9 secondary systems per year develops in the southwestern South Atlantic Ocean. These systems occur with more and less frequency in the colder and warmer months, respectively. Two distinct types of secondary cyclones were found. TYPE1 forms eastward and over the warm front region of the primary cyclone. These systems develop due to warm advection at lower levels and relatively weak influence of potential vorticity (PV) anomalies at upper levels. TYPE2 develops westward/northwestward of the primary cyclone where strong cold advection predominates at lower levels. However, in this type, the lower troposphere is heated due to intense heat and moisture fluxes and at upper levels it is forced by PV anomalies. Numerical simulations using the Weather Research and Forecasting model (WRF) indicate that the sensible and latent heat fluxes on surface act as intensification mechanisms for both TYPE1 and TYPE2 secondary cyclones and that the latent heat flux influences more on decreasing the pressure in these systems. The numerical experiments show that the cyclone TYPE2 does not develop in the absence of PV anomalies, while the TYPE1 does, but it is relatively weaker and delayed in time. Factors separation analysis indicates that the PV anomaly and some other mechanism unrelated to the factors evaluated in the simulations have a triggering role in the development of the secondary cyclone TYPE1, while the interaction of PV anomaly with surface fluxes acted to intensify the cyclone. The TYPE2 development occurred solely due to PV anomaly, which also acted to intensifying together with heat/moisture fluxes on surface as well as the interaction processes of these two factors.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / This paper analyzes the mesoscale model WRF (Weather Research And Forecast) to verify its reliability in use as a research tool in areas with potential for eolioeletric generation. The area chosen for study was a farm located in Maracanaà in the state of CearÃ. On the farm was installed an anemometer tower of 80 meters high with three anemometers, 1 windsock, 1 temperature sensor and a pyranometer, all sensors connected to a datalogger. The data collected in this tower were used for comparison with the data obtained through simulations in WRF. In the simulations the model was evaluated for two different climatic conditions in the region, the rainy and the dry seasons. The periods chosen to perform the simulations are: March/2012 (representing the rainy season) and November/2011 (representing the dry season). Was performed five sensitivity tests, which were exchanged in the parameterizations of the Planetary Boundary Layer (PBL), Surface Layer (CS) and Ground Surface Model (GSM). The simulation results were evaluated according to the Pearson's correlation method, that one has parameter values from -1 to 1 which presents an index of correlations ranging from bad (-1) to great (1). The simulation with the best performance in the dry and rainy periods presented values for correlations of 0.76 and 0.58, respectively, considered good and fair to the Pearson's parameters. The model was able to satisfactorily represent the local wind behavior for the dry season of the year, and more research is needed in the area to analyze how the model behaves in the representation of the rainy season. Thus, this model provides satisfactory results to be used as a tool for evaluate areas with potential for eolioeletric generation, more research is needed to fit better. / O presente trabalho analisa o modelo de mesoescala WRF (Weather Research and Forecast) para verificar a sua confiabilidade na utilizaÃÃo como ferramenta de investigaÃÃo de Ãreas com potencial para geraÃÃo eolioelÃtrica. A regiÃo escolhida para estudo foi uma fazenda localizada no municÃpio de MaracanaÃ, no estado do CearÃ. Na fazenda foi instalada uma torre anemomÃtrica de 80 metros de altura com 3 anemÃmetros, 1 biruta, 1 sensor de temperatura e um piranÃmetro, todos os sensores conectados a um datalogger. Os dados coletados nesta torre foram utilizados para comparaÃÃo com os dados obtidos atravÃs das simulaÃÃes no WRF. Nas simulaÃÃes o modelo foi avaliado para duas situaÃÃes climatolÃgicas distintas na regiÃo, o perÃodo chuvoso e o seco. Os perÃodos escolhidos para realizar as simulaÃÃes sÃo: marÃo/2012 (representando o perÃodo chuvoso) e novembro/2011 (representando o perÃodo seco). Foram realizados cinco testes de sensibilidade, nos quais foram permutadas as parametrizaÃÃes da Camada Limite PlanetÃria (CLP), Camada de SuperfÃcie (CS) e o Modelo de Solo SuperfÃcie (MSS). Os resultados das simulaÃÃes foram avaliados segundo o mÃtodo de correlaÃÃo de Pearson, mÃtodo este que possui parÃmetros de valores de -1 a 1 onde apresenta um indicativo de correlaÃÃes que vÃo de pÃssimas (-1) a Ãtimas (1). A simulaÃÃo com o melhor desempenho no perÃodo seco e chuvoso apresentaram valores de correlaÃÃes de 0,76 e 0,58, consideradas forte e moderada, para os parÃmetros de Pearson, respectivamente. O modelo conseguiu representar de forma satisfatÃria o regime de vento local para a estaÃÃo seca do ano, sendo necessÃrias mais pesquisas na Ãrea para analisar como o modelo se comporta na representaÃÃo do perÃodo chuvoso. Assim este modelo apresenta resultados satisfatÃrios para ser utilizado como ferramenta para avaliaÃÃo de regiÃes com potencial em geraÃÃo eolioelÃtrica, sendo necessÃrias mais pesquisas para ajustÃ-lo melhor.

Os ciclones secundários são sistemas que ainda não são bem definidos e, assim, são fenômenos de difícil previsibilidade, necessitando de mais estudos para identificar os sinais que disparam seu desenvolvimento. Neste estudo realizou-se um levantamento climatológico e estudo numérico de ciclogênese secundária no sudoeste do Oceano Atlântico Sul com o objetivo de obter informações sobre a atuação destes sistemas e entender os processos dinâmicos envolvidos no seu desenvolvimento. Para o período entre 1980 e 2010, a climatologia mostra que uma média de 3,9 sistemas secundários se forma por ano no Oceano Atlântico Sul. Estes sistemas ocorrem com maior e menor frequência nos meses frios e quentes, respectivamente. Dois tipos distintos de ciclones secundários foram encontrados. TIPO1 que se forma a leste e na região da frente quente do ciclone primário. Estes sistemas se desenvolvem sob advecção quente nos baixos níveis e pouca influência de anomalias de vorticidade potencial (VP) de altos níveis; TIPO2 se desenvolve a oeste/noroeste do ciclone primário onde predomina forte advecção fria em baixos níveis. No entanto, fluxos de calor e umidade intensos contribuem para aquecer a baixa troposfera e em altos níveis são forçados por anomalias de VP. Simulações numéricas com o modelo Weather Research and Forecasting (WRF) indicam os fluxos de calor sensível e latente na superfície como mecanismos de intensificação dos ciclones secundários TIPO1 e TIPO2, sendo o fluxo de calor latente mais importante no abaixamento de pressão destes sistemas. Os experimentos numéricos mostram que o ciclone do TIPO2 não se desenvolve na ausência de anomalia de VP, enquanto que o TIPO1 se desenvolve mais fraco e atrasado no tempo. A análise por separação de fatores indica que a anomalia de VP e algum outro mecanismo não relacionado aos fatores avaliados nas simulações tiveram papel disparador no ciclone do TIPO1, enquanto a interação da anomalia de VP com os fluxos de superfície atuou como intensificador. No TIPO2, o desenvolvimento ocorreu unicamente pela atuação da anomalia de VP, a qual também agiu como um intensificador juntamente com os fluxos de calor e umidade, bem como os processos de interação entre estes dois fatores. / Secondary cyclones are systems that are not well defined yet and they are difficult to predict, requiring further studies to identify the signals that trigger their development. In this study we carried out a climatology and numerical study of secondary cyclogenesis over the southwestern South Atlantic Ocean in order to obtain information about these systems and understand the dynamic processes involved in its development. The climatology for the period 1980-2010 shows that an average of 3.9 secondary systems per year develops in the southwestern South Atlantic Ocean. These systems occur with more and less frequency in the colder and warmer months, respectively. Two distinct types of secondary cyclones were found. TYPE1 forms eastward and over the warm front region of the primary cyclone. These systems develop due to warm advection at lower levels and relatively weak influence of potential vorticity (PV) anomalies at upper levels. TYPE2 develops westward/northwestward of the primary cyclone where strong cold advection predominates at lower levels. However, in this type, the lower troposphere is heated due to intense heat and moisture fluxes and at upper levels it is forced by PV anomalies. Numerical simulations using the Weather Research and Forecasting model (WRF) indicate that the sensible and latent heat fluxes on surface act as intensification mechanisms for both TYPE1 and TYPE2 secondary cyclones and that the latent heat flux influences more on decreasing the pressure in these systems. The numerical experiments show that the cyclone TYPE2 does not develop in the absence of PV anomalies, while the TYPE1 does, but it is relatively weaker and delayed in time. Factors separation analysis indicates that the PV anomaly and some other mechanism unrelated to the factors evaluated in the simulations have a triggering role in the development of the secondary cyclone TYPE1, while the interaction of PV anomaly with surface fluxes acted to intensify the cyclone. The TYPE2 development occurred solely due to PV anomaly, which also acted to intensifying together with heat/moisture fluxes on surface as well as the interaction processes of these two factors.

No description available.

A better understanding of the performance in precision of physical parameterizations in NWP models is necessary for improving forecasts of tornadic outbreaks. For this study, WRF simulations of tornadic outbreaks were run using configurations of three microphysics, three convective physics, and two PBL physics schemes. Each configuration was subjected to ten iterations of SKEBS. The means of the ten perturbation members of each parameterization configuration were bootstrapped for SB CAPE, SB CIN, and 0-3km SRH to find 95% confidence interval widths at each grid point. Maps of these spreads provided a spatial analysis of the uncertainty. Analyses on correlations and clusters were performed to determine how the configurations related spatially and in magnitude. These uncertainties were further bootstrapped to compare the mean of each configuration in boxplots. The effect on the uncertainty produced by each configuration varied according to the diagnostic variable being analyzed.

This study examines the dynamics and predictability of the mesoscale convective vortex (MCV) event of 10-13 June 2003 which occurred during the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX). The MCV formed from a preexisting upper-level disturbance over the southwest United States on 10 June and matured as it traveled northeastward. The BAMEX field campaign provided a relatively dense collection of upper air observations through dropsondes on 11 June during the mature stage of the vortex. While several previous studies have focused on analysis of the dynamics and thermodynamics of observed and simulated vortices, few have addressed the ability to predict MCVs using numerical models. This event is of particular interest to the study of MCV dynamics and predictability given the anomalously strong and long-lived nature of the circulation and the dense data set. The first part of this study explores the dynamics of this MCV through an in-depth analysis of data from the profiler network and BAMEX dropsonde observations, in addition to the conventional surface and sounding observations as well as radar and satellite images. Next, issues relating to model performance are addressed through anevaluation of two state-of-the-art mesoscale models with varying resolutions. It is determined that the ability of a forecast model to accurately predict this MCV event is directly related to its ability to simulate convection. It is also shown that the convective-resolving Weather Research and Forecast (WRF) model with horizontal grid increments of 4 km displays superior performance in its simulation of this MCV event. Finally, an ensemble of 20 forecasts using mesoscale model MM5 with horizontal grid increments of 10 km are employed to evaluate probabilistically the dynamics and predictability of the MCV through the examination of the ensemble spread as well as the correlations between different forecast variables among ensemble members. It is shown that after MCV development, the ensemble mean performs poorly while individual ensemble members with good forecasts of convection at all stages of the MCV also forecast the midlevel vortex well. Furthermore, correlations among ensemble members generally support the findings in the observational analysis and in previous literature.

This study examines the dynamics and predictability of the mesoscale convective vortex (MCV) event of 10-13 June 2003 which occurred during the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX). The MCV formed from a preexisting upper-level disturbance over the southwest United States on 10 June and matured as it traveled northeastward. The BAMEX field campaign provided a relatively dense collection of upper air observations through dropsondes on 11 June during the mature stage of the vortex. While several previous studies have focused on analysis of the dynamics and thermodynamics of observed and simulated vortices, few have addressed the ability to predict MCVs using numerical models. This event is of particular interest to the study of MCV dynamics and predictability given the anomalously strong and long-lived nature of the circulation and the dense data set. The first part of this study explores the dynamics of this MCV through an in-depth analysis of data from the profiler network and BAMEX dropsonde observations, in addition to the conventional surface and sounding observations as well as radar and satellite images. Next, issues relating to model performance are addressed through anevaluation of two state-of-the-art mesoscale models with varying resolutions. It is determined that the ability of a forecast model to accurately predict this MCV event is directly related to its ability to simulate convection. It is also shown that the convective-resolving Weather Research and Forecast (WRF) model with horizontal grid increments of 4 km displays superior performance in its simulation of this MCV event. Finally, an ensemble of 20 forecasts using mesoscale model MM5 with horizontal grid increments of 10 km are employed to evaluate probabilistically the dynamics and predictability of the MCV through the examination of the ensemble spread as well as the correlations between different forecast variables among ensemble members. It is shown that after MCV development, the ensemble mean performs poorly while individual ensemble members with good forecasts of convection at all stages of the MCV also forecast the midlevel vortex well. Furthermore, correlations among ensemble members generally support the findings in the observational analysis and in previous literature.

The influence of deployment of planned wind farms on the power output and energy yield of wind farms located in close proximity at downwind sites is investigated. The atmospheric model Weather Research and Forecasting (WRF) has been employed to simulate wind resource and energy yield from wind farms in the Baja California region of Northern Mexico. Accuracy of predicted wind speed and wind turbine energy supply are evaluated against full-scale measurements from a met-mast and from each of five 2 MW turbines at the La Rumorosa wind-farm. For this wind farm location, wind speed distribution is predicted to within 1.4% and the energy supply from the farm predicted to within 5.25%. Accuracy depends on the boundary layer model and atmospheric dataset employed. Wind farms are modelled using the scheme developed by Fitch et al. (2012) in which a momentum sink and turbulent kinetic energy source are defined as a function of the turbine thrust coefficient and power output, each of which vary with wind speed as defined by the manufacturer. Planned farms of up to 72 MW installed capacity are defined in terms of turbine number, rated power and spacing at four sites such that each farm operates with a typical capacity factor. For a single farm of 2 MW turbines located 10 km upwind, wind speed at the case study wind-farm is reduced by 3.00% and power output reduced by up to 5.84%. These deficits increase if 5 MW turbines are deployed rather than 2 MW turbines due to the development of a longer far-wake. The net energy supply from several sites in the region is assessed.

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.