The purpose of this article is to present a simple method of identification of deep convective clouds using water vapor (WV) and thermal infrared (IR) brightness temperature differences from the multispectral images of Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) sensor. The use of this method is part of an international effort to calibrate the radiances of SEVIRI sensor for microphysical properties of deep convective systems. This approach was applied to the image from 08 September 2009 for the demonstration of its efficacy analysis. The results show that the difference values larger than -2º C for BT Differences (WV6.2 μm -WV7.3 μm) and + 50º C (IR3.9 μm -IR10.8 μm) were associated with areas of intense precipitation. The method can be easily implemented and effectively utilized in operational basis to monitor deep convective cloud clusters over Brazil.
Este estudo teve como objetivo de desenvolver uma metodologia baseada na aplicação de produtos GEONETCastEUMETCast para estimativa da produtividade da cana-de-açúcar utilizando-se de um modelo agrometeorológico-espectral. O estudo foi desenvolvido no município de Coruripe, localizado no estado de Alagoas, Brasil. O teste foi realizado num período de cinco meses, abril a agosto, do ano de 2010. Conclui-se que a metodologia utilizada indica ser útil para o apoio operacional de estimativa da produtividade da cana-de-açúcar, fornecendo valores médios de 37 a 40 t/ha. Palavras-chave: Spot Vegetation, Meteosat-9, Produtividade Safra, Ilwis
A B S T R A C TThis study aimed to develop a GEONETCast-EUMETCast product-based method of estimating the productivity of cane sugar using an agrometeorological-spectral model. The study was carried out in the Municipality of Coruripe, located in the state of Alagoas, Brazil. The test was performed over the period of five months, from April to August of 2010. It was concluded that the methodology is useful for developing estimates of operational support for the cane sugar productivity, providing mean values of 37 to 40 t/ha.
The Laboratory for Analyzing and Processing Satellite Images (LAPIS) of the University of Alagoas (UFAL) has been very active in the usage of Meteosat Second Generation (MSG) satellite data and products since 2007. These data and products are received in near-real time using simple and low cost ground reception infrastructure. Several examples of the ingest and display process for MSG satellite data and products are presented. The ingest of these satellite data and products is accomplished using a McIDAS-V, ILWIS and TerraMA2 tools. It is also shown how these MSG satellite-based products can be combined with other data. Results show that McIDAS-V, ILWIS and TerraMA2 are very useful tools to researchers and forecasters in the input and display process for MSG satellite data and products.
The Laboratory for Analyzing and Processing Satellite Images (LAPIS) of the University of Alagoas (UFAL) has been very active in the usage of Meteosat Second Generation (MSG) satellite data and products since 2007. These data and products are received in near-real time using simple and low cost ground reception infrastructure. Several examples of the ingest and display process for MSG satellite data and products are presented. The ingest of these satellite data and products is accomplished using a McIDAS-V, ILWIS and TerraMA2 tools. It is also shown how these MSG satellite-based products can be combined with other data. Results show that McIDAS-V, ILWIS and TerraMA2 are very useful tools to researchers and forecasters in the input and display process for MSG satellite data and products.
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