Gutemberg Borges França

A Fire-Risk-Breakdown System for Electrical Power Lines in the North of Brazil

AbstractAnthropogenic or spontaneous fires (hotspots) are the main causes of unexpected breakdowns of electrical power lines in the northern region of Brazil. This research has tested, adapted, and implemented a preoperational system aiming to prevent electrical breakdowns for 382 km of electrical transmission lines in the state of Maranhao. The breakdown electrical fire risk is based on a combination of three variables: 1) the fire risk index, 2) the remotely sensed hotspot presence in the vicinity of electrical power lines, and 3) the vegetation stage. These variables are converted into Boolean variables, and their combination will classify the electrical fire risk as extreme, high, medium, low, or null. In regard to the system input variables, the fire risk index carries the highest representativeness in composition value of the breakdown electrical fire risk. Therefore, the results of two fire risk indices, calculated on the basis of the (a) Monte Alegre and (b) Angstrom methods, are presented and dis...

Oil spill fate forecast system for Ilha Grande Bay, Rio de Janeiro, Brasil

Baia da Ilha Grande is a bay located south Rio de Janeiro city, Brazil. It is a very important water body in regard to economic and ecological purposes, since it has a major commercial harbor, an oil terminal, many industries and two nuclear power plants. It is also one of the most important tourist resorts of Rio. An applied research study was made by the Marine and Atmospheric Processes Modeling Laboratory (LAMMA) of IJniversidade Federal do Rio de Janeiro to the Brazilian state oil company Petrobras in order to evaluate the hydrodynamic circulation in the bay and fate of oil spills that could take place in the Petrobras oil terminal, It was used the 3-D barc~clinicoceanic numerical model Princeton Ocean Model (POM) to simulate the current field using both tidal and wind forcing, The oil spill model Nicoil developed by LAMMA that considers the most important weathering processes was implemented in this region. Methods and results are presented,

Analyses of the Positive Bias of Remotely Sensed SST Retrievals in the Coastal Waters of Rio de Janeiro

This paper analyzes the large positive bias of sea surface temperature (SST) retrievals of selected remotely sensed algorithms recorded during the simultaneous occurrence of upwelling and atmospheric subsidence along the coastal waters of Rio de Janeiro, Brazil. The optimal estimator (OE) for retrieving SST and the multichannel (MCSST) and nonlinear (NLSST) estimators are compared using Advanced Very High Resolution Radiometer-3 data. The in situ SST (SSTbuoy) data set used to validate the remotely sensed SST retrievals was collected from five moored buoys (four in the open sea and one in coastal waters). The principal results of this paper are as follows. First, the sensitivity analyses show that OE is quite susceptible to the first-guess SST rather than to the humidity profiles. Second, the comparison between the SSTOE and 365 cloud-free SSTbuoy measurements in open sea waters presents an root mean squared error (RMSE), bias, and standard deviation (STD) with the intervals of [0.5, 0.6], [−0.51, 0.13], and [0.27, 0.48], respectively. Third, the MCSST, NLSST, and OE SST produce a positive bias that can reach 5 K during simultaneous upwelling and atmospheric subsidence in coastal waters. Such unexpected errors are due to low SST values and water vapor compression in the lower layer of the atmosphere related to a temperature inversion. Fourth, an alternative approach using SSTbuoy obtained on the previous day as a first guess instead of the climatological SST significantly improves the errors (SSTOE–SSTbuoy) by reducing RMSE, bias, and STD by 58% (from 3.30 to 1.39 K), 73% (from 3.00 to 0.80 K), and 19% (from 1.38 to 1.12 K), respectively.

Extraction of Tide Constituents by Harmonic Analysis Using Altimetry Satellite Data in the Brazilian Coast

This paper analyzes the seasurfaceheightdatasetfromthe TOPEX,Jason-1,andJason-2satellitesofa 19-yr time series in order to extract the tide harmonic constituents for the region limited by latitude 58N‐358 Sa nd longitude558‐208W.Theharmonicanalysisresultsimplementedherewerecomparedwiththetidalconstituents estimatedbythreeclassicaltidalmodels[i.e.,TOPEX/PoseidonGlobalInverseSolution7.2(TPXO7.2),Global Ocean Tide 4.7 (GOT4.7), and Finite Element Solution 2102 (FES2102)] and also with those extracted from in situ measurements. The Courtier criterion was used to define the tide regimes and regionally they are classified as semidiurnal between the latitude range from approximately 58 Nt o 228S, semidiurnal with diurnal inequality from228toabout298S,andmixedsouthwardoflatitude228S.Thecomparisonresultsamongalltideapproaches were done by analyzing the root-sum-square misfit (RSSmisfit) value. Generally, the RSSmisfit difference values are not higher than 12cm among them in deep-water regions. On the other hand, in shallow water, all models have presented quite similar performance, and the RSSmisfit values have presented higher variance thanthepreviousregion,as expected. Themajordiscrepancyresultswereparticularlynotedfor two tidegauges locatedinthelatituderangefrom58Nto28S.Thelatterwasinvestigatedandconclusionshavemainlypointedto the influence ofthe mouthof the Amazon River and the considerable distance between tide measurements and the satellite reference point, which make it quite hard to compare those results. In summary, the results have showed that all models presently generate quite reliable results for deep water; however, further study should done in order to improve them in shallow-water regions too.

Nowcasting model of low wind profile based on neural network using SODAR data at Guarulhos Airport, Brazil

ABSTRACT A generalized regression neural network model was tested – as a nowcasting tool – to forecast the low wind profiles up to 45 min (i.e. at heights of 10, 100, 200, and 300 m) at the Guarulhos International Airport, São Paulo, Brazil. A data set representing over 4 years was generated from sonic detection and ranging and surface meteorological station, which registered vertical wind profiles with intervals from 10 m to approximately 500 m in height every 15 min, and surface meteorological variables were collected each minute, respectively. These data were simultaneously used to train, validate, and test the proposed model. The u and v forecasts generated at 300, 200, and 100 m were better than at 10 m, which could certainly be attributable to the surface roughness. In addition, the results also revealed that the performance of the model is time-dependent – decreasing over time – and that this may be correlated with the fact that the neural network is a statistical rather than physical model. The forecasts of wind components u and v are slightly biased (or closely matched to observations) at all heights, and forecast intervals with maximum values have median and average errors equal to 0.070 and −0.017 ms−1, respectively. The forecast model’s results were evaluated using the values of four categorical statistics: probability of detection; probability for non-events; bias; and false-alarm ratio, with respectable minimum and maximum values for u wind principal components equal to 0.841, 0.833, 0.159, 0.981 at 10 m for 45-min forecasts and 0.989, 0.987, 0.011, 0.999 at 300 m for 15-min forecasts, respectively.

Estimativa de Fluxo de Massa de Ar Via Diferenças de Temperatura das Superfícies do Mar e do Continente Comparadas com Dados Observacionais

This work conducted analysis of the behavior of the sea breeze at the surface and at altitude, in the city of Rio de Janeiro, using surface weather stations, computational model, atmospheric sounding and wind profiler. This analysis has established the non-linear correlation between the flow of breeze and the variation of the temperatures of the ocean and the continent and it was estimated the flow of wet mass carried by the sea breeze from the ocean to the interior of the continent, taking examples of observations in winter and summer days. The results showed that the mass flow of moist air is transported from the sea to the mainland is about three times higher in summer compared to winter. Featuring non-linear correlation of the variation of surface temperature with the intensity of the sea breeze.

Uma Avaliação da Variação da Temperatura da Superfície do Mar durante a Passagem do Furacão Catarina

This study intends to observe the sea surface temperature (SST) variation during the tropical cyclone event, which strikes the eastern coast of Brazil during March of 2004, named Catarina hurricane, using SST data estimated from two orbital sensor sources located in different part of electromagnetic spectrum that is microwave and thermal infrared. The results have shown that maximum SST cooling were approximately 2.0 o C in comparison with the local SST data from two subsequent years. And the results indicated that during the hurricane way, the SST cooling really occurred, reaching the maximum values of -1.00 o C and -0.99 o C, when compared with daily TMI sensor data and a daily composition using TMI and AVHRR sensors data (interpolated by Barness method), respectively. Only microwave SST data could qualitatively represent the SST behavior; however, the thermal SST representativeness can be irrelevant during such event as Catarina. The results are quite similar to those found in the literature.

Estimação do Impacto do El Niño/La Niña na Intensidade dos Ventos do Nordeste Brasileiro

Wind energy is now one of the most promising energy sources of the world for being clean and abundant. The study of phenomena that are related to changes in atmospheric circulation, such as El Nino, are extremely important for its ability to affect wind generation. In order to explore the possible effect of such phenomena in the winds of the Northeast region of Brazil, a statistical analysis to quantify this effect through the model Generalized Autoregressive Score (GAS) is performed. This allows the modeling of time series for different probability distributions. Thus, the GAS is applied to the wind speed series from the Gamma distribution. The model results showed that El Nino has influence on the behavior of the wind, even though it is small in magnitude.

Refinement of the surface current fields estimated by Remote Sensing

This study uses an objective analysis (OA) method to obtain more realistic data on ocean surface current estimated via Remote Sensing (RS) by OSCAR. This refinement employs surface current data estimated via drift buoys from PNBOIA. The incorporation of buoy data to OSCAR current fields was performed using an OA method based on the Gauss-Markov Theorem. The results show a slight improvement of OSCAR current fields. The interpolation allowed reducing the difference between OSCAR and buoy vectors an average of 0.0166ms-1 and 7.7892° for the intensity and direction, respectively. In a specific case, this gain came to 0.45ms-1 in intensity and 144° in direction, thus proving that the interpolation can change the current direction up to 144° and its intensity up to 0.45ms-1, making that the value of the current measured by RS gets closer to the value measured by the buoy.

Methodology for eddies recognition from satellite images

In this this work is presented a semi-automatically technique for eddies recognition using GOES Sea Surface Temperature (SST) data time series from 2003 to 2008. The methodology consists of the main steps as follows: 1) identify the main characteristics of the eddies and define the area of interest (such climatology was derived from SST images for the period of 2003 to 2008); 2) cluster SST images; and 3) develop a morphological classifier based on a structuring element for identification of eddies. As a part of pre-processing, the fuzzy c-means clustering has demonstrated good results, followed by a morphology classification. The results are quite good ones and have demonstrated that is possible to develop and implement an efficient classifier.