Potential evapotranspiration estimates for northeast Brazil using GOES-8 data

Bastos, Eduardo J. de Brito; Souza, Jaidete Monteiro de; Rão, Tantravahi Venkata Ramana

doi:10.1590/s1415-43662000000300008

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Due to regional characteristics, ET rate is distinct in large areas, that is, the seashore coast of the northern Brazil and the inner regions have a higher temperature than along the eastern coast. Furthermore, the east coast is more affected by meteorological systems, such as the sea breeze, easterly waves and cold fronts that comes from the south region of the country; while the atmospheric circulation over the Northeastern region is mainly drawn by the orographyc effects 13 . In the middle-east, the annual cycle of the ET is well defined, with maximum values occurring during January and May and the minimum one in September (dry period) varying between 1-2 mm/day in the north of Minas Gerais State.…”

Section: Resultsmentioning

confidence: 99%

Evapotranspiration estimation in the Brazil using NDVI data

Rossato

Alvalá

Ferreira

et al. 2005

Remote Sensing for Agriculture, Ecosystems, and Hydrology VII

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The purpose of this study is to analyze the monthly mean spatial and temporal distribution of evapotranspiration (ET) during the 1981-2000 period in Brazil using remotely sensed data. The methodology involves the use of a relationship between ET and Normalized Difference Vegetation Index (NDVI). ET was estimated for the main Brazilian biomes through the Penman-Monteith method using climatological data from 194 Brazilian meteorological stations during the 1961-1990 period. NDVI data for the July 1981 to July 2000 period was obtained from Advanced Very High Resolution Radiometer (AVHRR) sensor on board the NOAA satellite. A relatively high correlation coefficient between ET and NDVI (r=0.81) was found, showing a near linear relationship involving these variables. Also, the monthly mean ET over Brazil was estimated using NDVI data. The results showed that the ET rate in the Amazon region is not well defined because the maximum values occur after the rainy season, while for the Northeast Brazil, the highest ET values occur in according to period of rainy season. The annual cycle of ET is most defined in the Central region, with maximum values occurring in January to May period and minimum in September. In the South and Southeast regions, the annual cycle ET does not change very much. Finally, this study suggests that NDVI is an important variable for indirectly monitoring ET over large areas, thus with great potential for agronomical and climatic applications.

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Section: Resultsmentioning

confidence: 99%

Evapotranspiration estimation in the Brazil using NDVI data

Rossato

Alvalá

Ferreira

et al. 2005

Remote Sensing for Agriculture, Ecosystems, and Hydrology VII

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“…The authors reported daily mean errors generally within ±10 W m −2 with standard deviations lower than 20 W m −2 . The GL1.2 database has contributed to the validation of numerical weather prediction models and data assimilation systems [29,30], to climatological and environmental studies [31][32][33], to hydrological modeling [34], to potential evapotranspiration estimation [35] and, last but not least, to solar resource assessment [36].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network

et al. 2020

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The GL (GLobal radiation) physical model was developed to compute global solar irradiance at ground level from (VIS) visible channel imagery of geostationary satellites. Currently, its version 1.2 (GL1.2) runs at Brazilian Center for Weather Forecast and Climate Studies/National Institute for Space Research (CPTEC/INPE) based on GOES-East VIS imagery. This study presents an extensive validation of GL1.2 global solar irradiance estimates using ground-based measurements from 409 stations belonging to the Brazilian National Institute of Meteorology (INMET) over Brazil for the year 2016. The INMET reasonably dense network allows characterizing the spatial distribution of GL1.2 data uncertainties. It is found that the GL1.2 estimates have a tendency to overestimate the ground data, but the magnitude varies according to region. On a daily basis, the best performances are observed for the Northeast, Southeast, and South regions, with a mean bias error (MBE) between 2.5 and 4.9 W m−2 (1.2% and 2.1%) and a root mean square error (RMSE) between 21.1 and 26.7 W m−2 (10.8% and 11.8%). However, larger differences occur in the North and Midwest regions, with MBE between 12.7 and 23.5 W m−2 (5.9% and 11.7%) and RMSE between 27 and 33.4 W m−2 (12.7% and 16.7%). These errors are most likely due to the simplified assumptions adopted by the GL1.2 algorithm for clear sky reflectance (Rmin) and aerosols as well as the uncertainty of the water vapor data. Further improvements in determining these parameters are needed. Additionally, the results also indicate that the GL1.2 operational product can help to improve the quality control of radiometric data from a large network, such as INMET's. Overall, the GL1.2 data are suitable for use in various regional applications.

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Potential evapotranspiration estimates for northeast Brazil using GOES-8 data

Cited by 2 publications

References 16 publications

Evapotranspiration estimation in the Brazil using NDVI data

Evapotranspiration estimation in the Brazil using NDVI data

Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network

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