Using the Meteosat-9 Images to the Detection of Deep Convective Systems in Brazil

Barbosa, Humberto Alves; Ertük, Aydin Gürol; Silva, Leandro Rodrigo Macedo da

doi:10.5935/2237-2202.20120007

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…To compare the model simulations with observations, the normalized difference vegetation index (NDVI), calculated using the methodology of Barbosa et al (2012), was used to evaluate the effects of aridity on the NEB vegetation (figures 3(e) and (f)). Our results were verified though comparison with MODIS NDVI accessed on http://neo.sci.gsfc.…”

Section: Resultsmentioning

confidence: 99%

The influence of oceanic basins on drought and ecosystem dynamics in Northeast Brazil

Pereira¹,

Justino²,

Malhado³

et al. 2014

Environ. Res. Lett.

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The 2012 drought in Northeast Brazil was the harshest in decades, with potentially significant impacts on the vegetation of the unique semi-arid caatinga biome and on local livelihoods. Here, we use a coupled climate-vegetation model (CCM3-IBIS) to: (1) investigate the role of the Pacific and Atlantic oceans in the 2012 drought, and; (2) evaluate the response of the caatinga vegetation to the 2012 climate extreme. Our results indicate that anomalous sea surface temperatures (SSTs) in the Atlantic Ocean were the primary factor forcing the 2012 drought, with Pacific Ocean SST having a larger role in sustaining typical climatic conditions in the region. The drought strongly influenced net primary production in the caatinga, causing a reduction in annual net ecosystem exchange indicating a reduction in amount of CO 2 released to the atmosphere.

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Section: Resultsmentioning

confidence: 99%

The influence of oceanic basins on drought and ecosystem dynamics in Northeast Brazil

Pereira¹,

Justino²,

Malhado³

et al. 2014

Environ. Res. Lett.

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“…View Over Brazil MSG covers much of Brazil enabling the use of its products and spectral bands for various types of applications. The same has been used in Brazil for generation of NDVI (Barbosa et al, 2006b;Barbosa and Ertük, 2009a;Borges, 2010;Barbosa et al, 2014;Silveira et al, 2015), estimation of soil temperature (Nieto et al, 2011), characterization of clouds and storms (Barbosa and Ertük, 2009b;Da Cruz, 2009;Barbosa et al, 2011Barbosa et al, , 2012. Such applications have been developed, in its most, in areas of study that are located in the Northeastern and Southeastern regions, which feature smaller angles of view and, consequently, higher image quality.…”

Section: Low Spatial Resolutionmentioning

confidence: 99%

Características e desafios de geração do NDVI para o Brasil a partir de dados do satélite de órbita geoestacionária Meteosat-10

Silveira¹,

Guimarães

Silva

2019

Rev. Bras. Geog. Fis.

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O monitoramento da cobertura do solo a partir de indicadores ambientais possibilita melhor conhecimento dos processos biofísicos e ecológicos, auxiliando na compreensão da dinâmica do uso da terra e na tomada de decisão. Satélites em órbita polar, em sua maioria, fornecem o NDVI (Normalized Difference Vegetation Index) com média resolução espacial (250 m a 1 km) a partir de composições de imagens (7, 10 ou 16 dias). Por outro lado, satélites em órbita geoestacionária podem fornecer o NDVI com baixa resolução espacial (1 a 3 km) e alta resolução temporal (10 a 25 minutos). Particularmente, o MSG (Meteosat Second Generation) mostra-se eficiente para o monitoramento da vegetação a partir do índice NDVI para regiões da África, apresentando alta correlação espacial com dados de satélites polares, sendo utilizado na análise e detecção do estresse hídrico da vegetação e da fenologia com vantagens significativas na análise de séries. No entanto, para o Brasil, existem limitações que precisam ser conhecidas para o desenvolvimento do produto NDVI MSG com qualidade. Este artigo tem como objetivo apresentar como o MSG é utilizado para o monitoramento de processos da superfície a partir do NDVI, com o intuito de identificar os desafios inerentes no desenvolvimento do produto para o Brasil. Para tanto, são apresentas as principais características do NDVI, o histórico do produto de cobertura global e uma discussão sobre os principais desafios do NDVI MSG para o Brasil, relacionados às características de baixa resolução espacial, visada sobre o Brasil, correções atmosférica e geométrica e características da imagem pancromática e canais espectrais. Palavras chave: Meteosat-10, NDVI, índice ambiental. Characteristics and challenges of NDVI generation to Brazil from Meteosat-10 geostationary orbit satellite data A B S T R A C T Monitoring land cover from environmental indicators enables a better understanding of biophysical and ecological processes, helping to understand the dynamics of land use to make decisions. Polar orbit satellites, for the most part, provide NDVI (Normalized Difference Vegetation Index) products with medium spatial resolution (250 m to 1 km) from image compositions (7, 10 or 16 days). On the other hand, geostationary orbit satellites provide NDVI with low spatial resolution (1 to 3 km) and high temporal resolution (10 to 25 minutes). Particularly, the MSG is efficient for the monitoring of vegetation from the NDVI index for regions of Africa, having a high spatial correlation with data from polar satellites, have been used in the analysis and detection of water stress of vegetation and phenology with significant advantages in the time series analysis. However, for Brazil, there are limitations that need to be known for developing NDVI MSG product quality. This paper aims to present how the MSG is used to monitor surface processes from the NDVI vegetation index, in order to identify the challenges inherent in product development for Brazil. For this, the main features of the NDVI are presented, the history of ...

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“…The MSG-SEVIRI BTs from the water vapor (6.2 and 7.3 µm) and the thermal-IR (10.8 and 8.7 µm) channels are exploited as auxiliary variables in the implementation of the kriging with external drift method. We consider: (i) BT differences between thermal-IR at 10.8 µm and water vapor at 6.2 µm channels, as this information is useful to identify deep-convection areas [14][15][16][17]; (ii) BT differences between water vapor at 6.2 and 7.3 µm channels, to recognize areas of intense precipitation [18]; and finally (iii) BT difference between 8.7 µm and 10.8 µm (thermal-infrared) channels, useful to discriminate liquid and ice cloud that could be associated to mid-level stratiform cloud (nimbostratus) and convective clouds, respectively.…”

Section: Msg-sevirimentioning

confidence: 99%

“…(i) BT differences between the 10.8 µm (thermal-infrared) and the 6.2 µm (water vapour) channels, to identify deep-convection areas [11,[14][15][16][17]; (ii) BT differences between the 7.3 µm (water vapour) and the 6.2 µm channels, to recognize areas of intense precipitation [17,18]; (iii) BT difference between 8.7 µm and 10.8 µm (thermal-infrared) channels, useful to discriminate liquid and ice cloud that could be associated with mid-level stratiform cloud (nimbostratus) and convective clouds, respectively.…”

Section: Choice Of the Auxiliary Variables For Kriging With External mentioning

confidence: 99%

Downscaling of Satellite OPEMW Surface Rain Intensity Data

et al. 2018

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This paper presents a geostatistical downscaling procedure to improve the spatial resolution of precipitation data. The kriging method with external drift has been applied to surface rain intensity (SRI) data obtained through the Operative Precipitation Estimation at Microwave Frequencies (OPEMW), which is an algorithm for rain rate retrieval based on Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations. SRI data have been downscaled from coarse initial resolution of AMSU-B/MHS radiometers to the fine resolution of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) flying on board the Meteosat Second Generation (MSG) satellite. Orographic variables, such as slope, aspect and elevation, are used as auxiliary data in kriging with external drift, together with observations from Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI) in the water vapor band (6.2 µm and 7.3 µm) and in thermal-infrared (10.8 µm and 8.7 µm). The validation is performed against measurements from a network of ground-based rain gauges in Southern Italy. It is shown that the approach provides higher accuracy with respect to ordinary kriging, given a choice of auxiliary variables that depends on precipitation type, here classified as convective or stratiform. Mean values of correlation (0.52), bias (0.91 mm/h) and root mean square error (2.38 mm/h) demonstrate an improvement by +13%, −37%, and −8%, respectively, for estimates derived by kriging with external drift with respect to the ordinary kriging.which hinders the investigation of spatial variability. This calls for the development of techniques to improve the spatial resolution of satellite rainfall remote sensing data.A large number of downscaling methods has been developed and applied in the last few years. Most of these methods are based on the correlation between rainfall and environmental information such as latitude, longitude, altitude, slope, aspect and other orographic characteristics. In particular, Next Generation Radar (NEXRAD) daily precipitation fields were downscaled from 16 km to 4 km by considering orographic effects on precipitation distribution [3]. This method consists of three parts, namely the rain-pixel clustering, the multivariate regression and the random cascade. In [4], a statistical downscaling method-based on the relationships between precipitation and both terrain factors (e.g., slope, aspect and roughness) and meteorological conditions (e.g., humidity and temperature)-was proposed to disaggregate the Tropical Rainfall Measuring Mission (TRMM) 3B42 from 25 km to 1 km. Other studies introduced the positive relation between vegetation and precipitation through the use of the Normalized Difference Vegetation Index (NDVI). Among these, Ref.[5] explored the relation between TRMM rainfall estimates and NDVI at different spatial scales; the derived relation has then been used to develop a downscaling method based on an exponential regression model. In [6], a multiple linear regression model wa...

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Using the Meteosat-9 Images to the Detection of Deep Convective Systems in Brazil

Cited by 4 publications

References 5 publications

The influence of oceanic basins on drought and ecosystem dynamics in Northeast Brazil

The influence of oceanic basins on drought and ecosystem dynamics in Northeast Brazil

Características e desafios de geração do NDVI para o Brasil a partir de dados do satélite de órbita geoestacionária Meteosat-10

Downscaling of Satellite OPEMW Surface Rain Intensity Data

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