Sea surface temperature GOES-8 estimation approach for the Brazilian coast

Abstract

This work presents a quite consistent procedure for estimation of sea surface temperature (SST) using data from the new generation of the Geostationary Operational Environmental Satellite (GOES). The SST methodology is based on the classical split-window equation. The regional split-window coefficients (A ₀, A ₁, A ₂ and A ₃) are estimated by an algorithm regression taking as dependent variable three datasets, i.e. the SST derived from National Oceanic and Atmospheric Administration (NOAA)-14 polar-orbiting satellite and from buoys of Pilot Research Moored Array in Tropical Atlantic (PIRATA) and National Programme of Buoys (PNBOIA). This work shows that the main advantage of the GOES-8 SST algorithm, in comparison with the multi-channel sea surface temperature (MCSST) procedure using Advanced Very High Resolution Radiometer (AVHRR) data, is the high frequency sampling imagery (each half-hour) which permits a daily image with much less quantity of cloud contamination. The algorithm results using AVHRR/NOAA-14 as input dataset for the regression show that the accuracy of the GOES-8 SST algorithm is better than 1.0°C for all Brazilian coast. For regional estimation, the accuracy has been improved to around 0.5°C. Also, the accuracy of GOES-8 SST is better than 0.7°C using in situ SST collected from moored and drifting buoys.

Acknowledgments

The authors would like to thank the Centro de Previsa˜o do Tempo Estudos Climáticos (CPTEC/INPE) for supplying the data and computer facilities used in this work. This work was undertaken as a project under CPTEC and Universidade Federal do Rio de Janeiro (UFRJ—Meteorology Department) agreement and supported by the Fundaça˜o de Amparo à Pesquisa do Estado de Sa˜o Paulo (FAPESP)—grant number 98/06415-6, Fundaça˜o José Bonifácio (FUJB), Conselho Nacional de Pesquisa (CNPq).