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.

The atmosphere has often been considered "chaotic" when in fact the "chaos" is a manifestation of the models that simulate it, which do not include all the physical mechanisms that exist within it. A weather prediction cannot be perfectly verified after a few days of integration due to the inherent nonlinearity of the equations of the hydrodynamic models. The innovative ideas of Lorenz led to the use of the ensemble forecast, with clear improvements in the quality of the numerical weather prediction. The present study addresses the statement that "even with perfect models and perfect observations, the 'chaotic' nature of the atmosphere would impose a finite limit of about two weeks to the predictability of the weather" as the atmosphere is not necessarily "chaotic", but the models used in the simulation of atmospheric processes are. We conclude, therefore, that potential exists for developments to increase the horizon of numerical weather prediction, starting with better models and observations. Keywords: atmospheric modeling; chaos; numerical weather prediction ResumoA atmosfera tem sido muitas vezes considerada "caótica" quando de fato o "caos" é uma manifestação dos modelos que a simulam, os quais não incluem todos os mecanismos físicos nela existentes. Uma previsão do tempo não se verifica perfeitamente depois de alguns dias de integração devido a não linearidade inerente às equações dos modelos da hidrodinâmica. As ideias inovadoras de Lorenz conduziram ao uso da previsão por conjunto, com melhorias flagrantes na qualidade das previsões. O presente estudo se contrapõe à afirmação de que "mesmo com modelos e observações perfeitas, a natureza 'caótica' da atmosfera imporia um limite finito de cerca de duas semanas para a previsibilidade do tempo", uma vez que a atmosfera não é necessariamente "caótica", mas sim os modelos usados na simulação de seus processos. Conclui-se, portanto, que há espaço para o desenvolvimento no sentido de aumentar os horizontes da previsão numérica do tempo, a partir de melhores modelos e melhores observações. Palavras-chave: modelagem atmosférica; caos; previsão numérica do tempo A n u á r i o d o I n s t i t u t o d e G e o c i ê n c i a s -U F R J

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.

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This paper is a review covering several physical factors that affect and define the behavior of the climate of the Amazonia and Northeastern Brazil. This review show the changes that may be debited to the presence and action of man, in conjunction with the fluctuations that nature imposes on the climate of these two regions in particular. The conclusion is the perception that it is possible to act so smart on the tropical South-American environment for the well being of the same man who until now was a destructive agent. However the risk remains if we exceed the threshold that would make the damage irreparable.

The present work deals with the controversy between the human understanding of the behavior of real atmosphere and the weather prediction in response to numerical models. Themes like the Chaos Theory, Eulerian and Lagrangian models, and atmospheric waves are considered, reviewed and criticized.

Satellite data enabled the Intergovernmental Panel on Climate Change (IPCC), through Report V, to indicate that the regional distribution of sea ice has been reducing in the Northern hemisphere high latitudes. This study assimilated that reduction into a general circulation model of intermediate complexity to simulate the tropical rainfall response. The Northern hemisphere tropospheric wind field simulations presented a clear similarity to the Northern Annular Mode negative phase. In particular, the meridional wind anomalies of the Northern hemisphere Ferrel cell suggest that the energy upsurge due to the boreal sea ice decrease results in an increase in the amplitude of the Rossby waves, thus connecting the polar zone to the tropics. The 500 hPa vertical motion and the rainfall distribution in the tropical belt simulations show a southward displacement of the Atlantic Intertropical Convergence Zone and also the South Atlantic Convergence Zone. Although several studies indicate the Intertropical Convergence Zone is shifted towards the hemisphere most heated by climatic variations, the apparent disagreement with our results can be understood by considering that some continental sectors in the Northern Hemisphere mid-latitudes have shown cooling in recent years, probably in response to the boreal sea ice decrease.

This study seeks to demystify the claim that the 'atmospheric chaos' imposes a two-week limit on reliable weather forecasts. 'Deterministic chaos' indeed occurs due to the use of nonlinear numerical models for these forecasts. This 'deterministic chaos' does impose time limits on valid predictions, but it also facilitates, through the ensemble forecasting technique, the use of interesting statistical indicators that define regions and the duration these predictions are more or less reliable. Recently published articles show that the 'uncertainties' in the initial conditions are an inherent difficulty in meteorological observations and have nothing to do with the atmospheric behavior. These studies demonstrate two important aspects regarding 'uncertainties' in data used to initialize models. First, to achieve improvements in numerical weather forecasts, these 'uncertainties' must be skillfully introduced in the large scale and not in the small scale. Secondly, the numerical models must include equations or parameterizations that reproduce nature's ways that let different scales 'interact', that is, the models should reproduce how the energy of different atmospheric modes 'travels'.In the 1960s and 1970s the academic meteorology community debated if more degrees of freedom could reduce the system instabilities in forecast model equations. With this line of reasoning the articles of Charney (1963) and Lorenz (1963) should be highlighted. Charney thought that with more degrees of freedom, the system of equations could stabilize, and thus extend the effective forecast limits. However, at that time, Lorenz, using a very simple convection model based on an approximate system of nonlinear ordinary differential equations, discovered that two runs of the model starting from slightly different initial conditions gave surprisingly divergent responses after a non-long period of integrations. Lorenz called this unexpected result 'deterministic chaos'. Reinforcing the Lorenz results, Kalnay (2003) stated that nothing could be done to ameliorate the models because, as the atmosphere was 'chaotic', the fourteen-day predictability limit could not be surpassed.Nowadays, nonlinear models are used instead of linear to forecast weather, because nonlinear models produce more 'realistic' or substantiated results. This is probably because nonlinear models take into account the disturbance products in the advection and others nonlinear terms, which are important to simulate the real atmosphere and which have aperiodic solutions, contrary to linear models. This study revisits the question of atmospheric predictability, suggesting the research community should invest its effort in two approaches. The first should endeavor to find modeling strategies that better reproduce the realistic ways large-scale interact with the small-scale in geophysical fluid systems, especially the atmosphere, in what are here named 'better models'. The second approach, more evident to the meteorological community, is to strongly invest to obtain more and 'bette...