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P. Poli et al.: SVP-BRST: genesis, design, and initial results
Ocean Sci., 15,199-214, 2019
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temperature (SST) data. Over short timescales, this essential
ocean state variable provides important information on
the spatial distribution and intensity of dynamic structures,
such as eddies, coastal currents and upwelling regions, in
near-real time (within a few hours after acquisition). Over the
long term (multi-decade), it describes the distribution of heat
within the Earth system. Long time series of SST datasets
(e.g., Merchant et ah, 2014) are crucial to provide information
on global and regional sea surface temperature trends.
These can be used directly to monitor the evolution of the
surface ocean on decadal timescales and help quantify the
intensity of events such as El Nino/La Nina, as well as being
useful to constrain climate reanalyses (e.g., Dee et al.,
2014). For these reasons, the importance of monitoring SST
was recognized as a priority by the Copernicus program, and
a sensor aimed at observing SST was included on Sentinel-3
satellites, the Sea and Land Surface Temperature Radiometer
(SLSTR; Coppo et ah, 2013). To deliver the SST data product
service (Bonekamp et ah, 2016), the dual-view capability and
onboard calibration of SLSTR give it comparable accuracy to
similar sensors, such as the Advanced Along-Track Scanning
Radiometer (AATSR; Llewellyn-Jones et al., 2001).
Satellite sensors measure top-of-atmosphere radiance,
which has some relation to but is not identical to the physical
temperature of Earth’s emitting surface. The inverse process
of inference of the surface state tends to amplify uncertainty.
Achieving the desired quality of Earth observation
measurements from SLSTR places stringent requirements
on the SLSTR sensor calibration (Donlon, 2011). This
drives a requirement for higher accuracy and better knowledge
of uncertainties of the surface measurements used for
validating the satellite products. This process requires the
highest-possible quality in situ measurements, with wellcharacterized
uncertainties, so that the error budget of SST
products can be investigated (e.g., Corlett et ah, 2014). Such
investigation requires covering the various regimes of satellite
SST retrievals, mandating in turn that the high-quality
in situ data be geographically well distributed.
As a result, concomitantly to the SLSTR development, the
Copernicus program aims to develop fiducial reference measurement
(FRM) initiatives. Among them is the deployment
of an array of temperature-measuring surface drifters, covering
several SST regimes. The operational nature and climate
quality of Sentinel-3 datasets are expected to deliver
long-term data records (Donlon, 2011). For consistency, this
implies that the surface references used for calibration and
validation must also be homogeneous over time. This FRM
initiative complements others started lately, such as under
the European Space Agency (ESA) project Fiducial Reference
Measurements for validation of Surface Temperature
from Satellites (FRM4STS), which has conducted in particular
a comparison of infrared radiometers with radiation thermometers
in a laboratory setting (Theocharous et ah, 2019).
Beyond comparisons, the goal is to establish the traceability
of the various sensing techniques to the Système International
(SI) unit, as it then guarantees anchoring to international
physical standards. In such attempt, the importance of
metadata to define exactly the sensor and its environment is
essential. For drifters measuring SST, this means knowing in
particular the SST sensor depth and type, its calibration process,
and other aspects influencing the buoy behavior (such
as drogue loss).
Based on lessons learnt from previous similar initiatives, a
new type of drifter has had to be developed and submitted to
a rigorous calibration procedure to meet this goal. In short,
this new type of drifter must carry a state-of-the-art digital
temperature sensor coupled to a hydrostatic water pressure
sensor, allowing for a measurement frequency of up to 1 Hz.
The value of this new drifter for calibration and validation
(cal/val) of SST satellite retrievals is expected to be assessed
through international collaboration.
The outline of this paper is the following. Section 2 revisits
the past high-resolution SST drifting buoy initiatives, including
error budget analysis. Based on the lessons learnt, Sect. 3
presents the design adopted for a new generation of drifter,
called the Surface Velocity Platform drifter with Barometer
and Reference Sensor for Temperature (SVP-BRST).
Section 4 shows preliminary measurement results from two
SVP-BRST prototypes deployed in the Mediterranean Sea.
Finally, Sect. 5 gives conclusions and prospects for future
work.
2 Genesis: lessons learnt from past HRSST drifting
buoy initiatives
2.1 Background: the HRSST-1 and -2 requirements
O’Carroll et al. (2008) compared SST retrievals from
AATSR with SST retrievals from a microwave sensor and
with in situ SST from drifters. The drifters were found to
have a standard deviation of error smaller than the microwave
SSTs and larger than those from the AATSR. This highlighted
the need for improved in situ calibrated reference
temperature data for satellite SST cal/val, particularly in reference
to the validation of high-quality dual-view satellite
SSTs, and the satellite and in situ communities started a
dialogue on collaboration and improvements. In 2009, the
Group for High-Resolution SST (GHRSST) called on the
Data Buoy Cooperation Panel (DBCP) HRSST Pilot Project
(HRSST-PP) to implement a number of key requirements
for buoys to be eligible to support HRSST work (Donlon,
2009). The buoys would have to provide hourly measurements,
nominal or design depth in calm water of the drifting
buoy SST to an absolute accuracy of 5 cm, location accuracy
of 500 m, SST with a nominal resolution of 0.01 K or less
and a total uncertainty of 0.05 K, and measurement time to
within 5 min.
