This dataset provide a times series of daily mean fields of Sea Surface Temperature (SST) foundation at ultra-high resolution (UHR) on a 0.02 x 0.02 degree grid (approximately 2 x 2 km) for the Mediterranean Sea, every 24 hours. An Optimal interpolation (OI) technique is used to combine coincident swath measures of SST from different types satellite sensors and to fill gaps where no observations are available or obstructed by clouds. This multi-sensor compositing and interpolation process categorizes this dataset as a Level 4 product. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST-PP as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with eachother before merging. The processing is the same as for the Atlantic Near Real Time (NRT) L4 dataset available on Copernicus Marine Service [SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025 dataset] and users can refer to the user manual and quality documents available there for more details. This dataset was developed in the frame of European Space Agency (ESA)'s Medspiration project.

This dataset provide a times series of daily multi-sensor composite fields of Sea Surface Temperature (SST) foundation at ultra high resolution (UHR) on a 0.02 x 0.02 degree grid (approximately 2 x 2 km) over Mediterranean Sea, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L3S SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. The processing is the same (minus the optimal interpolation step) as for the Atlantic Near Real Time (NRT) L3S dataset available on Copernicus Marine Service [SST_ATL_PHY_L3S_NRT_010_037 dataset] and users can refer to the user manual and quality information documents available there for more details. This dataset is generated daily within a 24 delay and is therefore suitable for assimilation into operational models.