Publicações

Thunderstorms cause a significant number of faults in energy distribution systems. The possibility to forecast the thunderstorm frequency in the summer period with a reasonable anticipation (a few months) would be of great importance to support preventive actions for protection and recomposition of the electrical system, improving its performance and reliability. Thunderstorm day monthly records obtained in the last 50 years in the city of Campinas are used to develop a forecast method to predict the lightning activity with at least a month in advance in the area of operation of the Elektro power electric company in the state of São Paulo, in the Southeast region of Brazil. It was found that the thunderstorm day monthly records during the summer period are not correlated with large scale meteorological parameters that are considered to be associated to the convective activity in the South America such as the El Niño Southern Oscillation (ENSO), the South Atlantic Ocean surface temperature anomaly, the Pacific Decadal Oscillation (PDO) and the international sunspot number related to the solar activity, while they are good correlated with previous thunderstorm days if conveniently filtered to avoid high frequency changes. Based on this fact, a prediction method with a one month in advance was developed with a reasonable level of success. For larger periods the confidence of the results decreases significantly. The preliminary results of the method are supported by comparisons with lightning data obtained by the Brazilian Lightning Detection Network (BrasilDat) and interruptions data in the distribution network for the last years, indicating its high potential to support operation management.

A new lightning detection network using WeatherBug Total Lightning Sensors (WTLS) is being deployed in Brazil. The network can acquire detailed signals emitted from both IC and CG flashes and continuously sends information to a central server. A WTLS is composed of an antenna, a global positioning system (GPS) receiver, a high-accuracy GPS-based timing circuit, a digital signal processor (DSP), and onboard storage and internet communication equipment. The sensor is a wideband system with detection frequency ranging from 1Hz to 12MHz. The wide frequency range enables the sensor to not only detect strong CG strokes, but to also detect weak IC pulses. The sensor records whole waveforms of each flash and sends them back, in compressed data packets, to the central server. Instead of using some waveform parameters, the whole waveforms are used in locating the flashes and differentiating between IC and CG strokes. A 75-sensor network is being installed in the South, Southeast, Center and Northeast regions of the country and should operated simultaneously with the integrated lightning detection network (RINDAT), which is composed by 34 lowfrequency (LF) sensors using Vaisala technology, covering the Southeast and parts of the South and Center regions of the country. Details of the new network configuration and its main applications will be described.

Lightning monitoring technologies to support electric power applications have been developed and used for at least 30 years in many countries. The main technology available consists of Lightning Location Systems (LLS). They have been used in conjunction with specific computational tools by power utilities for many different applications dedicated to reduce the lightning impact on electric power systems. In most cases, the LLS operate in the LF frequency range and are designed to detect and locate mainly cloud-to-ground flashes. In general, most of the applications are concentrated in the operation and analysis of performance of distribution and transmission networks, involving the use of real time and historic data. The goal of this article is describe the most common uses of LLS data by power utilities. Examples of applications involving cloud lightning data obtained by these networks are also discussed. The potential use of LLS data is strongly dependent on the adequate knowledge by power utility personal of such aspects.

Recentemente, há um aumento de CO2 na atmosfera, decorrente do uso de combustíveis fósseis (carvão, petróleo, gás natural) em termelétricas, indústrias, automóveis e também da devastação e queima de florestas contribuindo para o aquecimento global. O objetivo do presente trabalho é estimar o fluxo de CO2 entre a superfície e a atmosfera, empregando uma rede neural artificial (RNA) Perceptron de Múltiplas Camadas (PMC). Os dados observacionais são assumidos como dados de satélites ambientais. O processo de inversão ocorre em 2 etapas: (a) estimativa de perfis de concentração de CO2 a partir de radiâncias, (b) identificação de fluxo de CO2 a partir da concentração obtida no item (a). Os resultados dos processos de inversão foram satisfatórios em que a RNA-PMC apresentou robustez ao estimar o fluxo de superfície entre o solo e a atmosfera com dados ruidosos.

The analysis of development activities in the surrounding protected natural areas contributes to identify the elements that question the functionality, the contradictions and the limits of sustainable development of these areas. In this context, this study examines the susceptibility to environmental risks of buffer zones of two protected natural areas in the State of São Paulo. This analysis was built from orbital date of the TM/Landsat-5 sensor and the Shuttle Radar Topographic Mission. During the time study, the buffer zones showed increased susceptibility to erosion and geomorphologic vulnerability of protected areas. This condition is primarily related to the conversion of natural areas into agricultural systems in regions with steep slopes. The observed scenario shows the need to adopt effective measures for the management and restoration of priority areas to ensure the biological and functional integrity of conservation units.

After the integration of three regional lightning detection networks (SIDDEM and RINDAT) in June 2005, the Brazilian lightning detection network (BrasilDAT) covered almost 50% of Brazil except to the Amazon basin and the Northeastern Brazil. For the last 6 years (2005-2010), the BrasilDAT network has been detecting cloud-to-ground (CG) lightning flashes over the mid-southern part of Brazil with relative high accuracy (estimated average detection efficiency of 80% and average location accuracy of less than 1km). In this paper, the CG lightning distribution in Southern and Southeastern Brazil will be presented based on the 6-year data from BrasilDAT. Furthermore, detection efficiency (DE) corrections were provided to compensate variations due to sensor faults or changes in network geometry. There is evidences that the topography and the occurrence of frontal systems (which are common in the Southeastern Brazil) are responsible for the spatial distribution of the CG flashes observed. The increase in the number of CG flashes observed in the central-southern Paraná and western Rio Grande do Sul might be related to the occurrence of Mesoscale Convective Systems (MCS) which are very large time-persistent meteorological systems characterized by high CG flash rates.

A 12 sensors trial network, called Brazilian Total Lightning Detection Network (BTLN), combining advanced lightning detection technologies with modern electronics was installed in the Southeastern Brazil in 2010/2011. The network is composed of 12 WeatherBug Lightning Sensor (WLS), each of them with a GPS-based timing circuit, a digital signal processor (DSP) and onboard storage and internet communication equipment. The WLS is a wideband system with detection frequency ranging from 1Hz to 12MHz. The wide frequency range enables the sensor to not only detect strong CG strokes, but to also detect weak IC pulses. The sensor records whole waveforms of each flash and sends them back, in compressed data packets, to the central server. Instead of using only the peak pulses, the whole waveforms are used in locating the flashes and differentiating between IC and CG strokes. Sophisticated digital signal processing technologies are employed on the server side to ensure high-quality detections and to eliminate false locations. The main goal of this paper is present the preliminary data provided by the trial network since December 2010.

Variations of the network performance indicate the limitations of the lightning data provided by lightning location networks. One of the most important is the detection efficiency (DE), given as the ratio of the number of detected events by the events that actually occur. And of course variations of the network DE affect the analyses (mainly spatial) using the resulting lightning data. On this way, the most recent version (fourth generation) of the relative detection efficiency model (RDEM) for the Brazilian Lightning Detection Network (BrasilDAT) is presented: the RDEM4. Some improvements on the previous version (Naccarato and Pinto. 2008) are made: (1) more consistent filtering of essential solutions and sensors, (2) relative detection efficiency distributions for each individual sensor assessed as a function of distance, peak current and azimuth, (3) network detection efficiency estimation based on the daily status reports, which provides a ultimate accuracy on the DE computation. The results show considerable enhancements compared to from the previous version (RDEM3). The correction of the number of CG lightning based on the peak current distributions shown also a considerable effect on the final CG lightning density maps increasing the CG lightning density values over the entire network coverage with a significant improvement over areas close to the network borders.

Este trabalho tem como objetivo o aperfeiçoamento dos algoritmos para a determinação da cobertura efetiva de nuvens em imagens do satélite geoestacionário GOES. A cobertura efetiva de nuvens é utilizada para alimentar o modelo de transferência radiativa BRASIL-SR utilizado para o mapeamento de recursos de energia solar no território brasileiro. A radiação solar fornece energia para todos os seres vivos e para alguns sistemas de sensores (passivos); o homem contribui de forma direta para as constantes mudanças climáticas ao meio ambiente, a utilização de satélites artificiais, vem sendo cada vez mais explorada na utilização em pesquisas cientificas, tendo como grande foco as áreas meteorológicas. As nuvens são o principal fator de modulação da irradiação solar incidente na superfície e desempenham um papel fundamental no balanço enérgico do planeta. As nuvens, apresentam importância no controle da temperatura na superfície uma vez que está relacionada com a capacidade de reflexão da energia solar. Além disso, a observação das nuvens também são relevantes para observar e obter informações sobre fenômenos meteorológicos como furações, ciclones, frentes frias. O modelo Brasil-SR foi desenvolvido no INPE com o apoio da Universidade Federal de Santa Catarina (UFSC) e do instituto de pesquisa GKSS/Alemanha. O modelo Brasil-SR utiliza informações climatológicas de temperatura, visibilidade atmosférica, albedo de superfície e umidade relativa em conjunto com a cobertura efetiva de nuvens para parametrizar os processos radiativos que ocorrem na atmosfera. O modelo Brasil-SR permitiu a geração de um banco de dados confiáveis sobre o potencial de energia solar no Brasil e resultou na publicação do Atlas Brasileiro de Energia Solar. As atividades desenvolvidas estão em fase inicial em razão de que a bolsista se integrou ao projeto em Fevereiro/2011. Neste período, foi desenvolvida as competências para desenvolvimento de algoritmos utilizando o pacote computacional MATLAB para o processamento de imagens do satélite GOES. Os primeiros resultados são esperados para o fim de 2011. Dados observados em estações meteorológicas em operação em aeroportos nacionais serão utilizados para validação dos algoritmos em desenvolvimento. Esta etapa de comparação com dados de superfície deverá ocorrer a partir de Setembro/2011.