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The Next Generation of Multi-Sensor Merged Sea Surface Temperature Data Sets for Europe

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Remote Sensing of the European Seas

Sea Surface Temperature (SST) measured from satellites in considerable spatial detail and at high frequency is required for operational monitoring and forecasting of the ocean, assimilation into coupled oceanatmosphere models, numerical weather prediction, seasonal forecasting and climate change applications. Currently, many different SST data sets derived from satellite systems are available, and European scientists and agencies alike are presented with a bewildering set of options in terms of SST product content, coverage, spatial resolution, timeliness, format and accuracy. In response, a new generation of integrated SST data products and services are being provided by the Global Ocean Data Assimilation Experiment (GODAE) High resolution SST Pilot Project (GHRSST-PP). An international distributed framework, called the Regional/Global Task Sharing Framework (R/GTS), has been implemented, in which L2 satellite SST data products are processed, following a common Detailed Processing Specification agreed by the GHRSST-PP International Science Team. Output products contain dynamic flags and uncertainty estimates for each SST measurement, for quality control of SST data prior to direct use or to analysis/assimilation. Gap-free L4 analysis systems have also been developed, to combine complementary satellite and in situ SST observations in real time, to improve spatial coverage, temporal resolution, cross-sensor calibration stability and SST product accuracy. European systems provide daily 2-km Mediterranean Sea maps and 1/20 degree (~6 km) global maps. Using GHRSST-PP data, SST anomalies for the North Sea and U waters are presented for 2006, clearly showing strong (>3°C) anomalies for July.

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References

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Donlon, C. (2008). The Next Generation of Multi-Sensor Merged Sea Surface Temperature Data Sets for Europe. In: Barale, V., Gade, M. (eds) Remote Sensing of the European Seas. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6772-3_14

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