Estimativa das temperaturas médias diárias, diurnas e noturnas a partir das temperaturas horárias

Vieira, Hamilton Justino; Back, Álvaro José; Lopes, Fábio; Morais, Heverly

doi:10.1590/s0102-77862012000300007

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…The differentiation order d is the number of differences required to turn a non-stationary series into a stationary one; it is used to remove the effects of tendencies. The number of autoregressive orders p specifies which previous values of series will be used to predict the current values; and the number of orders of moving averages q specifies how the deviations of average of series for previous values are used to predict the current values (Espinosa et al, 2010;Vieira et al, 2012).…”

Section: Autoregressive Integrated Moving Average Technique -Arimamentioning

confidence: 99%

Previsão e Modelagem das Ocorrências de Incêndios no Pantanal

Viganó

Souza

Neto

et al. 2018

Rev. bras. meteorol.

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Uncontrolled fire points and forest fires in the Pantanal cause great damage to local flora and fauna. The prediction of these events is of paramount importance as it may mitigate or even avoid disasters in the Pantanal ecosystem. Given that fire prevention should be constant, the identification of fire foci is essential for immediate intervention. The objective of this study was to evaluate the forest fires in the Pantanal of Mato Grosso do Sul associated with the meteorological variables and to perform a forecast modeling. The environmental variables involved in the process were provided by the database of the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (INPE) and from the meteorological database for teaching and research of the National Institute of Meteorology (INMET). A close relationship was observed between the meteorological variables temperature, relative humidity and solar radiation and the occurrence of foci and the resulting correlations were satisfactory for the application of forecasting models. The Multiple Linear Regression technique presented fit of 41% and the Integrated Self-Regressive Analysis of Moving Average technique, 66.5%, and a general performance of 68.4%, thus making the latter the most recommended methodology for forecasting.Keywords: environmental monitoring, pantanal biome, prevention of fire points, fire forecast. Previsão e Modelagem das Ocorrências de Incêndios no Pantanal ResumoAs queimadas e incêndios descontrolados que ocorrem no Pantanal causam grandes prejuízos à fauna e flora locais. A previsão desses eventos é de grande importância por possibilitar que as catástrofes no ecossistema do Pantanal sejam amenizadas ou, até mesmo, evitadas. Tendo em vista que a prevenção contra incêndios deve ser constante, a identificação de focos de queimadas é essencial para uma intervenção imediata. Este estudo teve como objetivo avaliar as ocorrências de queimadas e incêndios no Pantanal Sul-Mato-Grossense, associadas às variáveis meteorológicas e realizar uma modelagem de previsão. As variáveis envolvidas nesse processo foram extraídas da base de dados do Centro de Previsão de Tempo e Estudos Climáticos do Instituto Nacional de Pesquisas Espaciais (INPE) e do banco de dados meteorológicos para ensino e pesquisa do Instituto Nacional de Meteorologia (INMET). Foram observadas que a temperatura, umidade relativa e radiação solar, possuem um relacionamento estreito com a ocorrência dos focos e as correlações resultantes foram satisfatórias para a aplicação das modelagens de previsão. A técnica de Regressão Linear Múltipla apresentou 41% de ajustamento e a técnica de Análise Auto-regressiva Integrada de Médias Móveis apresentou ajustamento de 66,5% e desempenho geral de 68,4%, tornando-a a metodologia mais recomendada para a previsão.Palavras-chave: monitoramento ambiental, bioma pantanal, prevenção de queimadas, previsão de fogo.

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Section: Autoregressive Integrated Moving Average Technique -Arimamentioning

confidence: 99%

Previsão e Modelagem das Ocorrências de Incêndios no Pantanal

Viganó

Souza

Neto

et al. 2018

Rev. bras. meteorol.

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“…T ng was precisely estimated in several Brazilian locations that cover the continental range of the country (Table ). It is a straightforward method for assessing the T ng based only on four input variables (latitude, day of the year, maximum and minimum air temperatures), which by its simplicity and precision differs from all other published methods on the issue, as Parton and Logan (), Vieira, Back, Lopes, and Morais (), and Dupuis (). The use of mean night‐time temperature was, therefore, relevant allowing the use of Ruiz model with better accuracy and precision for expressing the pathogen biology accordingly.…”

Section: Discussionmentioning

confidence: 99%

Climatic favourability zones for Eucalyptus rust in Brazil

et al. 2016

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Brazilian forest-based industries are supported by more than 5.5 million hectares of Eucalyptus growing under different climatic conditions with different degrees of favourability for rust Puccinia psidii, including both traditional and expanding areas, where such disease is of major concern for the industry, foresters and scientists. The main objective of this study was to define favourable climatic zones for Eucalyptus rust in Brazil with the following aims: (i) to develop a spatial method for estimating the mean night-time temperature (T ng ); (ii) to assess and validate a Eucalyptus rust model;and (iii) to map Eucalyptus rust favourability zones in Brazil based on the proposed model. A straightforward method, based only on latitude, day of the year, maximum and minimum air temperatures, was developed to estimate T ng , which is a key variable for a proper application of Ruiz rust model. Based on 37 field experiments with natural rust occurrence, it was observed that climatic conditions are determining factors for disease severity. Significant correlations between disease severity observed in the field and climatic conditions were found: a negative correlation with temperature (r = .50, p < .01) and a positive correlation with relative humidity (r = .89, p < .0001). A significant correlation (r = .81, p < .0001) between the normalized infection index, produced from Ruiz model, and the average rust score was also observed. Once the model was validated under field conditions, it was applied using historical average data of air temperature and leaf wetness duration to obtain monthly Eucalyptus rust favourability maps for the whole country. These final maps show that favourable climatic zones for Eucalyptus rust are extremely dynamic, with high temporal and spatial variability in Brazil and that climatic conditions should be considered for expansion of Eucalyptus to new areas, in breeding programmes, and for defining the most suitable seasons for forest establishment in each climate of the country. These results provide forestry managers with practical tools to reduce uncertainty about the expected severity of Eucalyptus rust in Brazil.

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“…Segundo Vieira et al (2012), no ano de 2011 foi realizada a estimação das médias de temperaturas diárias do estado de Santa Catarina utilizando um modelo de RLM.…”

Section: Introductionunclassified

Redes Neurais Artificiais na Previsão de Queimadas e Incêndios no Pantanal (Artificial Neural Networks in Prediction of Forest Fires and Burns in the Pantanal)

Viganó¹,

Souza²,

Cristaldo³

et al. 2017

Rev. Bras. Geog. Fis.

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Artigo recebido em 05/04/2017 e aceito em 31/09/2017 R E S U M O O bioma pantaneiro é acometido anualmente por um grande número de queimadas e incêndios. Prever esses eventos é de suma importância, uma vez que, prejuízos à fauna e à flora poderiam ser minimizados e catástrofes evitadas. Uma intervenção imediata do poder público na mitigação desses eventos passa, essencialmente, pela previsão do número de focos e das áreas queimadas, e posteriormente, na localização desses focos e na identificação das áreas. Dados precisos sobre as variáveis ambientais, em tempo real, podem ser obtidos através do sensoriamento remoto, aliado aos sistemas de informações geográficas, às técnicas de inteligência artificial e estatística aplicada, favorecendo às tomadas de decisão na previsão. O objetivo deste estudo foi aplicar a técnica de Redes Neurais Artificiais (RNAs) para a previsão dos focos e das áreas queimadas no Pantanal Sul-Mato-Grossense. O centro de previsão de tempo e estudos climáticos do Instituto Nacional de Pesquisas Espaciais (INPE), bem como, o Instituto Nacional de Meteorologia (INMET) possuem bancos de dados do Pantanal, dos quais, as variáveis envolvidas nesse processo foram extraídas. Utilizando as RNAs do tipo Multilayer Perceptron, com algoritmo Retropropagation de aprendizagem foi possível prever o valor do número de focos com um ajuste de 84,8% utilizando um conjunto de variáveis meteorológicas como preditoras e, de 99,4% usando como preditora, somente a série temporal do número de focos. No entanto, o ajuste passou a ser 90,3% ao se realizar a previsão da área queimada, utilizando as mesmas variáveis, e de 98,6%, usando como preditora, somente os dados da área queimada. Palavras-chave: previsão de fogo, monitoramento ambiental, inteligência artificial. Artificial Neural Networks in Prediction of Forest Fires and Burns in the Pantanal A B S T R A C TOn a year basis the Pantanal biome is affected by a large number of fires and fire points. Predicting these events is of paramount importance, since damage to fauna and flora could be minimized and disasters might be avoided. Immediate intervention of the government in mitigating these events essentially depends on the identification and location of fire outbreaks. Accurate and reliable data on environmental variables in real time can be obtained by means of remote sensing coupled with geographic information systems, techniques of artificial intelligence and applied statistics, which would favor the decision-making in foreseeing fire foci and burned area. The aim of this study was to apply the technique of artificial neural networks to predict the fire foci and burned areas in the Pantanal Sul-Mato-Grossense. The center for weather forecast and climate studies of the National Institute for Space Research (INPE) and the National Institute of Meteorology (INMET) afford meteorological database of the Pantanal region, from which the environmental variables involved in this process were extracted. By using Multilayer Perceptron Artificial Neural Networks with Back...

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Estimativa das temperaturas médias diárias, diurnas e noturnas a partir das temperaturas horárias

Cited by 4 publications

References 4 publications

Previsão e Modelagem das Ocorrências de Incêndios no Pantanal

Previsão e Modelagem das Ocorrências de Incêndios no Pantanal

Climatic favourability zones for Eucalyptus rust in Brazil

Redes Neurais Artificiais na Previsão de Queimadas e Incêndios no Pantanal (Artificial Neural Networks in Prediction of Forest Fires and Burns in the Pantanal)

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