Publicações

Este trabalho tem como objetivo contribuir com o estudo da precipitação na cidade de Salvador BA. Os totais mensais de precipitação obtidos pelo Instituto Nacional de Meteorologia no período de 51 anos (1961-2011) fundamentaram esta pesquisa. A variabilidade pluviométrica foi analisada com ênfase na determinação das classes da precipitação nas escalas anual, quadrimestral e mensal. Foram utilizados os testes de significância não paramétricos de Mann-Kendall e as estimativas de inclinação pelo método da Curvatura de Sen. Na classificação pluviométrica foi utilizado o Método dos Percentis (Quantis). Para a análise anual, os resultados mostraram que 1964 (1961) foi um ano classificado como muito chuvoso (muito seco). Na análise do quadrimestre mais chuvoso AMJJ (mais seco ASON), o ano de 1971 (1980) foi o mais extremo muito chuvoso (muito seco). Na análise mensal, o evento que obteve maior volume pluviométrico ocorreu em abril de 1984 com registros de 889,8 mm. A análise da tendência mostrou uma redução no volume de chuva na escala anual, entretanto, sem significância estatística. Grande parte dos eventos analisados ocorreram com condições oceânicas no Pacífico equatorial e/ou Atlântico Sul Tropical favoráveis (desfavoráveis) ao aumento (diminuição) do volume de chuva na área em estudo, tendo por base uma análise qualitativa.

O objetivo desta pesquisa foi avaliar destreza das projeções do clima presente (1960-1990), do modelo EtaHadCM3 das temperaturas máxima, média, mínima e precipitação. Avaliou-se mudanças na dinâmica das projeções climáticas para Pernambuco para os períodos de 2010-2040 e 2041-2070. Para a avaliação do desempenho dos cenários climáticos gerados pelo modelo Eta-HadCM3 foi realizada análise de regressão, calculado índice d para cada localidade na área de estudo, identificados e corrigidos os erros sistemáticos das projeções. As temperaturas máximas e mínimas apresentaram valores mais precisos, a média e a precipitação apresentaram valores mais exatos, porém a precipitação foi mais comprometida em nível de qualidade das saídas do modelo. Quanto à identificação dos erros, viu-se que a temperatura máxima foi subestimada e a mínima superestimada, mas que depois de corrigidas, os resultados foram satisfatórios. Com relação às projeções, identificou-se que a precipitação apresentou redução nos pontos estudados e as temperaturas apresentaram maiores valores, ambas de forma progressiva. Concluiu-se que as simulações climáticas regionais podem ser empregadas para estudos de mudanças climáticas no estado, porém os erros sistemáticos devem ser considerados, já que são inerentes às imperfeições dos modelos, para que diminuam as incertezas da modelagem em estudos de impactos das mudanças climáticas globais.

A Zona de Convergência Intertropical (ZCIT) é o mais proeminente fenômeno meteorológico da atmosfera equatorial. Conhecida desde o início do ciclo das grandes navegações ela começou a ser estudada no séc. XIX. De início, entendida como um tipo de frente equatorial ao estilo clássico da escola de Bjerknes, somente a partir da II Guerra Mundial é que recebeu a atual denominação. Vários estudos foram então realizados sobre a ZCIT. No Brasil, este sistema afeta diretamente o regime de chuvas de parte das Regiões Norte e Nordeste, sendo por isto permanentemente monitorada. No período de 2003 a 2016, o sistema apresentou taxas de precipitação (tprec) acima de 0.3 mm h-1 afetando o litoral do norte do Nordeste do Brasil entre os meses de Fevereiro a Abril. Ao longo dos 14 anos em estudo, o comportamento da tprec pouco se modificou tanto no que se refere ao deslocamento sazonal dos centros de máxima como na magnitude precipitada.

Assessing vulnerability and potential impacts associated with extreme discharges requires an accurate topographic description in order to estimate the extension of flooded areas. However, in most populated regions, topographic data obtained by in-situ measurements is not available. In this case, digital elevation models derived from remote sensing date are usually applied. Moreover, this digital elevation models have intrinsic errors that introduce bigger uncertainty in results than the associated to hydrological projections. On the other hand, estimations of flooded areas through remote sensing images provide accurate information, which could be used for the construction of river level-flooded area relationships regarding vulnerability assessment. In this work, this approach is applied for the city of Porto Velho in the Brazilian Amazonia to assess potential vulnerability to floods associated with climate change projections. The approach is validated using census data, provided by the Brazilian Institute of Geography and Statistics, and information about socio-economical injuries associated to historical floods, provided by the Brazilian Civil Defence. Hydrological projections under climate change are carried out using several downscaling of climate projections as inputs in a hydrological model. Results show more accurate estimation of flood impacts than the obtained using digital elevation models derivate from remote sensing data. This reduces uncertainties in the assessment of vulnerability to floods associated with climate change in the region.

O objetivo deste estudo é avaliar o balanço de água na bacia amazônica simulado pelo modelo EtaHadGEM2-ES para o período de 1985 a 2005, definido como clima presente. Para tanto, os componentes do balanço de umidade simulados pelo modelo são comparados aos campos gerados a partir de dados observacionais e de reanálises. De modo geral, o modelo Eta-HadGEM2-ES conseguiu reproduzir de maneira satisfatória as características climatológicas sobre a Amazônia e da América do Sul. Na escala sazonal, a precipitação simulada apresentou bias negativo na Amazônia durante o verão e positivo no inverno, especialmente nos setores oeste e sul da bacia. A evapotranspiração simulada é superestimada na bacia amazônica e na região Centro-Sul do Brasil durante o verão e subestimada no Nordeste do Brasil em ambas as estações. O modelo superestimou o transporte de vapor dágua da Amazônia para as latitudes superiores do continente. Quanto à convergência de umidade, valores superestimados (subestimados) foram observados no oeste (centro) da Amazônia. Na média anual, foi verificado que não ocorreu o fechamento do balanço de água simulado pelo modelo na bacia amazônica desbalanço de umidade da ordem de 69%. ABSTRACT: The goal of this study was to evaluate the Amazonian water budget via an Eta-HadGEM2-ES model simulation for the present climate period, 1985-2005. Here, the water budget components simulated by the model were compared to observational data and reanalysis. In general, the Eta-HadGEM2-ES model was able to reproduce satisfactorily the climatology of the Amazon and South America. On the seasonal scale in the Amazon, simulated rainfall had a negative bias during the summer and was positively biased in the winter, especially in the areas west and south of the basin. Simulated evapotranspiration was overestimated in the Amazon basin and the Central-South region of Brazil during the summer and underestimated in the Northeast of Brazil in both seasons. The model overestimated the Amazonian water vapor transport to the upper latitudes of the South American continent. As for convergence, overestimated values (underestimated) are observed in the west (center) of the Amazon. When averaged annually, it was found that there was no closure of the water budget as simulated by the model in the Amazon basin moisture imbalance in the order of 69%.

Vcmax is the rate of maximum velocity of carboxylation of plants and is considered one of the most critical parameters for changes in vegetation in face of global changes and it has a direct impact on gross primary productivity. Physiological processes are considered the main sources of uncertainties in dynamic global vegetation models (DGVMs). The Caatinga biome, in the semiarid region of northeastern Brazil, is extremely important due to its biodiversity and endemism. In a field work realized in an area of preserved Caatinga forest, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of a native species. These results of Vcmax measurements in Caatinga were compared with parameterization of models, revealing that Vcmax is not well adjusted in several DGVMs. Also, the values obtained in the Caatinga field experiments were very close to empirical values obtained in the Northern hemisphere (Austria). These ecophysiological measurements can contribute in understanding of this biome.

With the substantial growth of solar photovoltaic installations worldwide, forecasting irradiance becomes a critical step in providing a reliable integration of solar electricity into electric power grids. In Singapore, the number of PV installation has increased with a growth rate of 70% over the past 6 years. Within the next decade, solar power could represent up to 20% of the instant power generation. Challenges for PV grid integration in Singapore arise from the high variability in cloud movements and irradiance patterns due to the tropical climate. For a thorough analysis and modeling of the impact of an increasing share of variable PV power on the electric power system, it is indispensable (i) to have an accurate conversion model from irradiance to solar power generation, and (ii) to carry out irradiance forecasting on various time scales. In this work, we demonstrate how common assumptions and simplifications in PV power conversion methods negatively affect the output estimates of PV systems power in a tropical and densely-built environment such as in Singapore. In the second part, we propose and test a novel hybrid model for short-term irradiance forecasting for short-term intervals. The hybrid model outperforms the persistence forecast and other common statistical methods.

This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.

The wind energy share is growing fast in Brazil and a better understanding of wind speed vertical profiles is essential for accurate power density estimates. The wind shear is highly variable in space and time, being influenced by surface layer stability. Studies concerning wind vertical profiles may have a significant contribution to the Brazilian energy sector. However, wind observational data are very scarce and most of them were acquired in automated weather stations. This work investigates the wind vertical profile data acquired at a wind mast in a semi-arid region of Brazilian Northeastern region, correlating it to stability conditions of the surface boundary layer, surface roughness and friction velocity. The results indicated that strong winds (>7 m sec−1) overcome the stability effects, allowing a better estimate of roughness length, friction velocity and exponential coefficient. In a second step, the wind speed at 50 m agl. Were estimated from wind measurements at 25 m agl. The wind estimates were compared to observations, showing that exponential and logarithmic approaches were able to simulate wind profile. The exponential approach presented the lowest BIAS for rainy season, but overestimated high wind velocities. Finally, a sensitivity analysis demonstrated how uncertainties on roughness length and exponential coefficient impacts on BIAS deviation and reduce the confidence on wind power density estimates. The results demonstrated the importance in understanding how wind vertical profile is related to atmospheric stability condition in order to get reliable values for the shear parameters from mast observations. It is still more critical if wind database is short-term and when dealing with lower wind speeds.

Recognized as one of the world’s most vital natural and cultural resources, the Amazon faces a wide variety of threats from natural resource and infrastructure development. Within this context, rigorous scientific study of the region’s complex social-ecological system is critical to inform and direct decision-making toward more sustainable environmental and social outcomes. Given the Amazon’s tightly linked social and ecological components and the scope of potential development impacts, effective study of this system requires an easily accessible resource that provides a broad and reliable data baseline. This paper brings together multiple datasets from diverse disciplines (including human health, socio-economics, environment, hydrology, and energy) to provide investigators with a variety of baseline data to explore the multiple long-term effects of infrastructure development in the Brazilian Amazon.

There has been a growing demand on energy sector for short-term predictions of energy resources to support the planning and management of electricity generation and distribution systems. The purpose of this work is establishing a methodology to produce solar irradiation forecasts for the Brazilian Northeastern region by using Weather Research and Forecasting Model (WRF) combined with a statistical post-processing method. The 24 h solar irradiance forecasts were obtained using the WRF model. In order to reduce uncertainties, a cluster analysis technique was employed to select areas presenting similar climate features. Comparison analysis between WRF model outputs and observational data were performed to evaluate the model skill in forecasting surface solar irradiance. Next, model-derived short-term solar irradiance forecasts from the WRF outputs were refined by using an artificial neural networks (ANNs) technique. The output variables of the WRF model representing the forecasted atmospheric conditions were used as predictors by ANNs, adjusted to calculate the solar radiation incident for the entire Brazilian Northeastern (NEB) (which was divided into four homogeneous regions, defined by the Ward method). The data used in this study was from rainy and dry seasons between 2009 and 2011. Several predictors were tested to adjust and simulate the ANNs. We found the best ANN architecture and a group of 10 predictors, in which a deeper analyzes were carried out, including performance evaluation for Fall and Spring of 2011 (rainy and dry season in NEB, mainly in the northern section). There was a significant improvement of the WRF model forecasts when adjusted by the ANNs, yielding lower bias and RMSE, and an increase in the correlation coefficient.