Estimativa da radiação de onda longa atmosférica horária na região de Araripina-PE
The knowledge of atmospheric longwave radiation is of great importance in meteorological studies, and consequently in applications in the industrial energy sector and applications in agriculture, in addition to one of the most complex issues in the world that is global warming, since this radiation...
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| Formato: | bachelorThesis |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/42896 |
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| Resumo: | The knowledge of atmospheric longwave radiation is of great importance in meteorological studies, and consequently in applications in the industrial energy sector and applications in agriculture, in addition to one of the most complex issues in the world that is global warming, since this radiation is linked to temperature and atmospheric gases. Because it is the component of the radiation balance more complex to measure, the equipments have a high cost, which makes their use unfeasible. Therefore, some researchers have created models that estimate longwave radiation. Since the models created are developed from the geographic and climatic data of the region of each researcher, the objective of this work was to parameterize the models of Swinbank (1963), Idso & Jackson (1969), Idso (1981), Silver (1996) and Duarte (2006) for the data obtained in the Brazilian semi-arid region and to evaluate its performance by analyzing the mean absolute error, root mean square error, relative mean percentage error, Pearson's coefficient and Willmott's coefficient. The data were collected throughout the year 2017 in a reserved area of Petrobrás in Araripina-PE, where they were processed in hourly averages and separated into general data groups, clear and partly cloudy days, and on dry and rainy days, for see if there is an improvement in performance. The model of Swinbank (1963) presented the worst performance in all the data groups, while the other models presented values of errors and correlation coefficients very close, especially the model of Idso (1981) presented the lowest values of errors and the highest Pearson correlation values. Although the separate data performed slightly better than the overall data, it was not significant for data to be classified. |
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