Abstract
Sea surface temperature (SST) is an essential climate variable used for ocean and weather monitoring and forecasting. The NOAA’s next generation geostationary satellite GOES-16 was declared operational at the east position (75°W) in December 2017, carrying onboard an Advanced Baseline Imager (ABI). The hyperspectral ABI sensor now allows SST estimates every 10–15 min at both day and nighttime, with advanced options for cloud screening and water vapor correction. In the present work, we compare the first operational ABI SST product (OSI SAF, 2018) with an in situ match-up database (MDB) across the Tropical and Southwestern Atlantic Ocean, off the Brazilian coast, throughout the year of 2018. The MDB was obtained from two long-term programs, i.e., PIRATA moored buoys (FOLTZ et al., 2016) and PNBoia moored and drifting buoys (MARINHA DO BRASIL, 2017). Separate comparisons were made for each data set, analyzing the uncertainties according to the program (i.e., buoy type and region), satellite SST quality level and influence of diurnal heating. We also compare the ABI product with the OSTIA analysis L4 SST (DONLON et al., 2012) to increment our analyses on the spatio-temporal biases within the study region. The results show that the OSI SAF ABI SST L3C has a mean bias (0.1 °C) and error (RMSE, 0.5 °C) within the GHRSST standards, with an exception being coastal waters off the southeast Brazilian coast (RMSE, 0.65 °C), which are subjected to sharp thermal fronts. The highest biases are for regions/seasons subjected to persistent cloud coverage and high water-vapor content, i.e., the Intertropical and South Atlantic Convergence Zones, as well as highly dynamic frontal zones, i.e., the Brazil Malvinas Confluence Zone, the Subtropical Front and coastal waters. The ABI SST product is suitable for operational use, and applications should explore more deeply the new set of information provided.
Highlights
Published: 8 January 2021The sea surface temperature (SST) is an important regulator of several ocean–atmosphere interaction processes, such as the exchange of heat, water, gas and momentum between the atmosphere and the ocean [1]
This work is divided into two main parts: (i) the first is the comparison of the SSTsat (ABI-GOES-16) with the Match-up Data Base (MDB) (SSTdepth) obtained from moored and drifting buoys to verify the quality of the satellite product in the study region; and the second (ii) is the comparison of the daily average SSTsat (ABI-GOES-16) with the global SSTfnd analysis (OSTIA)
The best performance was with the daily average SSTdepth of the PIRATA moored buoys with lowest root mean squared error (RMSE) (
Summary
The sea surface temperature (SST) is an important regulator of several ocean–atmosphere interaction processes, such as the exchange of heat, water, gas and momentum between the atmosphere and the ocean [1]. Precise and accurate SST measurements, both in long time series and in Near Real Time (NRT) products are essential for environmental studies and the operational monitoring and forecast of the ocean, weather and climate [2,3]. With the evolution of orbital remote sensing, radiometric instruments onboard geostationary and polar satellites have become essential to monitor and collect information from areas that are not covered by in situ data collection programs, especially in the open ocean [4]. SST satellite products are generated from the brightness temperature (BT) measured by thermal infrared or microwave sensors, at atmospheric window channels (with minimum atmospheric gas absorption), and converted into SST by means of algorithms that combine external sources of information, using radiative transfer equations [5,6], empirical
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