The Copernicus Surface Velocity Platform drifter with Barometer and Reference Sensor for Temperature (SVP-BRST): genesis, design, and initial results - Paul Poli; Marc Lucas; Anne O'Carroll; Marc Le Menn; Arnaud David; Gary K. Corlett; Pierre Blouch; David Meldrum; Christopher J. Merchant; Mathieu Belbeoch; Kai Herklotz (Ocean Science, 2019)
To support calibration and validation of satellite sea surface temperature (SST) retrievals, over 60 high-resolution SST (HRSST) drifting buoys were deployed at sea between 2012 and 2017. Their data record is reviewed here. It is confirmed that sea state and immersion depth play an important role in understanding the data collected by such buoys and that the SST sensors need adequate insulation. In addition, calibration verification of three recovered drifters suggests that the sensor drift is low, albeit negative at around −0.01 K year−1. However, the statistical significance of these results is limited, and the calibration procedure could not be exactly reproduced, introducing additional uncertainties into this drift assessment. Based on lessons learnt from these initial buoys, a new sensor package for the Surface Velocity Platform with Barometer (SVP-B) was designed to serve calibration of SST retrievals by European Union's Copernicus satellites. The novel sensor package includes an HRSST sensor calibrated by a metrology laboratory. The sensor includes a pressure probe to monitor immersion depth in calm water and acquires SST data at 1 Hz over a 5 min interval every hour. This enables the derivation of mean SST as well as several percentiles of the SST distribution. The HRSST sensor is calibrated with an uncertainty better than 0.01 K. Analysis of the data collected by two prototypes deployed in the Mediterranean Sea shows that the buoys are able to capture small-scale SST variations. These variations are found to be smaller when the sea state is well mixed and when the buoys are located within eddy cores. This affects the drifter SST data representativeness, which is an aspect of importance for optimal use of these data.