The southeastern coast of Brazil is frequently affected by meteorological disturbances such as cold fronts, which are sometimes associated with intense extratropical cyclones. These disturbances cause oscillations on the sea surface, generating low-frequency motions. The relationship of these meteorologically driven forces in low frequency to the storm-surge event is investigated in this work. A method to predict coastal sea level variations related to meteorological events that use a neural network model (NNM) is presented here. Pressure and wind values from NCEP–NCAR reanalysis data and tide gauge time series from the Cananéia reference station in São Paulo State, Brazil, were used to analyze the relationship between these variables and to use them as input to the model. Meteorological influences in the sea level fluctuations can be verified by filtering the astronomical tide frequencies for periods lower than tidal cycles (periods higher than 24 h). Thus, a low-pass filter was applied in the tide gauge and meteorological time series for periods lower than tides to identify more readily the interactions between coastal sea level response and atmospheric-driven forces. Statistical analyses on time and frequency domain were used. Maxima correlations and coherence between the low-frequency sea level and meteorological series could be defined using the time lag of the NNM input variables. The model was tested for 6-, 12-, 18-, and 24-hourly forecasts, and the results were compared with filtered sea level values. The results show that this model is able to capture the effects of atmospheric and oceanic interactions. It can be considered to be an efficient model for predicting the nontidal residuals and can effectively complement the standard constant harmonic analysis model. A case study of a storm that impacted coastal areas of southeastern Brazil in March 1998 was analyzed and indicates that the neural network model can be effectively utilized in the Cananéia region.

Statistical analysis of extreme values is applied to wind data from National Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis grid points over the ocean region bounded at 23°S and 40°W and 42°W towards the south and southeastern Brazilian coast. The period of analysis goes from 1975 to 2006. The generalized extreme value and generalized Pareto distributions are employed for annual and daily maxima, respectively. The Pareto-Poisson point process characterization is also used to analyze peaks over threshold. Return levels for 10, 25, 50, and 100 years are calculated at each grid point. However, most of the reanalysis data fall within 1-10-year return periods, suggesting that hazardous wind speed with low probability (return periods of 50-100) have rarely measured in this period. Wide confidence intervals on these levels show that there is not enough information to make predictions with any degree of certainty to return periods over 100 years. Low extremal index (θ) values are found for excess wind speeds over a high threshold, indicating the occurrence of consecutively high peaks. In order to obtain realistic uncertainty information concerning inferences associated with threshold excesses, a declustering method is performed, which separates the excesses into clusters, thereby rendering the extreme values more independent.

Investigations surrounding the variability of productivity in upwelling regions are necessary for a better understanding the physical-biological coupling in these regions by monitoring systems of environmental impacts according to the needs of the regional coastal management. Using a spatial and temporal database from National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric (NCAR) Research reanalysis, Quick Scatterometer vector wind, and surface stations from the Southeast coast of Brazil, we investigate the meteorological influences due to the large-scale systems in the variability of the nutrient and larvae concentration, and chlorophyll a, describing statistically relationships between them in upwelling regions. In addition, we used multivariate analysis, such as PCA and clustering to verify spatial and temporal variances and describe more clear the structure and composition of the ecosystem. Correlation matrix analyses were applied for different water masses present in the study area to identify the relations between physical and biogeochemical parameters in a region, where frequently upwelling occur. Statistical approaches and seasonal variability show that the period of November to March is more sensitive to nutrients (1.20 mg/m(3) for chlorophyll a, 2.20 μmol/l for total nitrogen and 5.5 ml/l for DO) and larvae concentrations (120 org/m(3) for most of the larvae, except for cirripedia that presented values around 370 org/m(3)) relating to the influence of large and mesoescale meteorological patterns. The spatial and temporal variables analyzed with multivariate approach show meaningful seasonality variance of the physical and biological samples, characterizing the principal components responsible for this variance in spring and summer (upwelling period), emphasizing the monitoring of species as crustaceans and mussels that are present in the local economy. Then, the spring and summer season are characterized by high productivity due to the occurrence of upwelling in this period.

A variabilidade do nível do mar observado e a maré meteorológica na Baía de Paranaguá-PR foram analisadas, neste trabalho, com os dados maregráficos utilizados na Parte 1 e os dados meteorológicos de reanálise do "National Centers for Environmental Prediction" (NCEP) e do "National Center Atmospheric Research" (NCAR) pontos de grade no oceano, próximos ao local de estudo, referentes ao mesmo período. As componentes de alta freqüência contidas nos dados de reanálise foram retiradas com o filtro passa-baixa de Thompson, descrito na Parte 1, adaptado para intervalos de 6 horas. Analisou-se as influências das variáveis meteorológicas mais remotas, nas sobre-elevações e abaixamentos do nível do mar observado, utilizando dados de reanálise de pressão e vento. Conforme descrito na Parte 1, as séries foram analisadas, estatisticamente, no domínio do tempo e da freqüência. A série maregráfica filtrada de Cananéia (SP), utilizada para verificar a existência de correlação com a série de Paranaguá, confirmou os estudos de Mesquita (1997) para o litoral Sudeste. Essa correlação foi verificada devido à proximidade da estação de Cananéia ao ponto de grade relativo à pressão. A Rede Neural Artificial (RNA) desenvolvida na Parte 1 foi, então, utilizada com os dados de reanálise, mantendo-se a mesma arquitetura de rede com as máximas correlações entre as variáveis de entrada e saída, ajustando-se os parâmetros de taxa de aprendizado e momento para alcançar o melhor desempenho. Os resultados obtidos com ambas as fontes de dados foram comparados e a eficiência da rede foi semelhante à Parte 1 para as simulações de 6h e 12 h. Para as simulações de 18h e 24h, os resultados foram inferiores como os encontrados para a estação de superfície, sugerindo também, o desenvolvimento de outras arquiteturas de rede, visando melhorar as previsões para períodos maiores. Os resultados obtidos com os dados de reanálise sugerem a sua utilização na falta de estações meteorológicas convencionais próximas a estações maregráficas.

Oscillations in sea level due to meteorological forces related to wind and pressure affect the regular tides and modify the sea level conditions, mainly in restricted waters such as bays. Investigations surrounding these variations and the biological and chemical response are important for monitoring coastal regions mainly where upwelling shelf systems occur. A spatial and temporal database from Quick Scatterometer satellite vector wind, surface stations from the Southeast coast of Brazil and surface seawater data collected in Anjos Bay, Arraial do Cabo city, northeast of Rio de Janeiro State were used to investigate the meteorological influences in the variability of the dissolved oxygen, nutrients, meroplankton larvae and chlorophyll-a concentrations. Multivariate statistical approaches such as Principal Component Analysis (PCA) and Clustering Analysis (CA) were applied to verify spatial and temporal variances. A correlation matrix was also verified for different water masses in order to identify the relationship between the above parameters. A seasonal variability of the meteorological residual presents a well-defined pattern with maximum peaks in autumn/winter and minimum during spring/summer with negative values, period of occurrence of upwelling in this region. This lowering of the sea level is in accordance with the increasing of nutrients and meroplankton larvae for the same period. CA showed six groups and an importance of the zonal and meridional wind variability, including these variables in a single cluster. PCA retained eight components, explaining 64.10% of the total variance of data set. Some clusters and loadings have the same variables, showing the importance of the sea-air interaction.

Since viruses are able to influence the trophic status and community structure they should be accessed and accounted in ecosystem functioning and management models. So, this work met a set of biological, chemical and physical time series in order to explore the correlations with marine virioplankton community across different trophic gradients. The case studied is the Arraial do Cabo upwelling system, northeast of Rio de Janeiro State in Southeast coast of Brazil. The main goal is to evolve three type of artificial neural network (ANN) by genetic algorithm (GA) optimization to predict virioplankton abundance and dynamic. The input variables range from the abundance of phytoplankton, bacterioplankton and its ratios acquired by one in situ and another ex situ flow cytometers. These data were collected with weekly frequency from August 2006 to June 2007. Our results show viruses being highly correlated to their host, and that GA provided an efficient method of optimizing ANN architectures to predict the virioplankton abundance. The RBF-NN model presented the best performance to an accuracy of 97% for any period in the year. A discussion and ecological interpretations about the system behavior is also provided.

The southeastern Brazilian coast is a vulnerable region to the development of severe storms, mainly caused by the passage of cold fronts and extratropical cyclones. In the last decades, there has been an increase in the occurrence of subtropical cyclones. This study investigates trends and climatic variations, analyzing surface meteoceanographic series at six grid points from the reanalysis databases of ERA-Interim and ERA5 (European Center for Medium-Range Weather Forecasts-ECMWF) from 1979 to 2018 over the ocean region bounded, approximately, at 18°S, 25°S and 37ºW, 45ºW (between the states of Espírito Santo, Rio de Janeiro and São Paulo). Non-parametric statistical tests and the generalized extreme value distribution are employed for annual, seasonal and daily maxima/minima. The numbers of occurrence of extreme values, as well as the extremal index are also estimated in order to better understand the behavior of extremes. Annual maximum sea-surface temperature anomalies of the ERA-Interim databases show very low negative values, mainly at the beginning of measurements (between 1979 and 1982), leading to high positive trend values. The results are compared to the updated data from ERA5 which have anomalies that are more homogeneous with positive trends but without statistical significance. The other meteorological series of the ERA-Interim does not present discrepancies. Only the maximum anomalies of air temperature have significant annual and seasonal positive trends at grid points near the coast of Rio de Janeiro and São Paulo. Despite that the analyses for pressure and wind speed anomalies do not indicate significant trends, they present increases in the interdecadal pattern of the numbers of occurrence of extreme percentiles for almost every grid point. Return levels for 10, 25, 50, 75, and 100 years are estimated at each grid point and many maximum/minimum peaks are close to the return levels for 100-year return periods. The extremal index suggests average cluster sizes associated with no predominance of clustering for the extreme percentiles, which represents weak dependence between the exceedances. These results characterize some independence between extreme meteorological events such as the event that has been taking place in the region. The occurrence of maximum daily wind speed peaks calculated in austral spring, whose values exceeded the previous ones, is identified at three grid points near the southeast Brazilian coast, caused by the passage of the subtropical cyclone "Deni," which occurred in November 2016.

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