P. Poli et al.: SVP-BRST: genesis, design, and initial results 
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Ocean Sci., 15,199-214, 2019 
Figure 7. Trajectories of the two SVP-BRST prototypes after de 
ployment on 26 April 2018. The two buoys separated on 22 May 
2018. Map data: SIO, NOAA, US Navy, NGA, GEBCO; map im 
age: Landsat/Copernicus. 
mean temperature over 5 min reported by the HRSST sensor, 
5 percentiles of the SST distribution within that time interval 
(10 %, 30 %, 50 % or median, 70 %, and 90 %), and the mean 
and the standard deviation of the hydrostatic water pressure 
during 5 min. 
These parameters are shown in Fig. 8, where atmospheric 
pressure, SST, and significant wave height from the ECMWF 
operational analyses have been added. This information was 
co-located to the buoy dates, times, and locations using the 
same procedure as described in Sect. 2.2 (albeit at different 
horizontal and temporal resolutions). For the sake of compar 
ing results, the time series are only for as long as both buoys 
were freely drifting (until 11 June). 
The information from ECMWF analyses, although at a 
horizontal resolution of around 10 km, is independent from 
the buoys. It hence provides interesting information to con 
sider when assessing the buoy data. For air pressure (Fig. 8a), 
both buoys agree with the ECMWF analyses to within 
0.8 hPa rms. This is comparable to the state-of-the-art SVP-B 
deployed in this region. 
For SST (Fig. 8b), the comparison to ECMWF analyses 
only suggests that the latter are typically lagging behind the 
buoy evolution by 24 h, until 5 June 2018. It must be remem 
bered that the SST is not currently analyzed in the ECMWF 
prediction system, but this system was upgraded on 6 June, 
including a component to include atmosphere-ocean cou 
pling (Buizza et ah, 2018). 
The depth inferred from the HRSST hydrostatic pressure 
sensor (Fig. 8c) shows values around 15 to 18 cm (which is 
the design location of the HRSST sensor). The spread be 
tween the two estimates is stable in time, around 4 cm. The 
calibration procedure of the pressure sensors may explain 
this difference. This remains however close to the design 
depth of 18 cm below the flotation line of the buoy. 
The spread in the SST percentiles, shown in Fig. 8d, is usu 
ally within 0.1 K but sometimes exceed 0.3 K. In such situa 
tions, the calibration accuracy of the sensor is not of much 
help to help exploit the data for precise comparison with 
other sources. However, the availability of five estimates of 
SST, instead of just the mean, should help users move their 
applications to a small (five-member) ensemble and better 
understand how the spread in input in situ SST impacts their 
products. 
Figure 8e shows the standard deviation of depth (inferred 
assuming hydrostatic equilibrium). This estimate varies be 
tween 1.5 and 3.5 cm. It is largest when the significant wave 
height (estimated by the ECMWF analyses) is largest, in line 
with stronger winds at the same times (Fig. 8f). This is ex 
pected from the buoy dynamics (as the pressure measured 
will be affected by positive and negative accelerations), and 
confirms that the ECMWF wind and wave height analysis 
appears to be correct. Given this result, the larger spread in 
SST percentiles appears to be well correlated with situations 
where the wave heights and wind speeds are smaller. This 
would seem to validate the conjectures formed earlier by re 
visiting the HRSST-2 SVP-BS data record, namely that the 
sea state is an important parameter to consider when exploit 
ing the in situ SST data. 
Regarding the SST data, we see that both buoys cap 
ture fairly well the diurnal warming/cooling cycle, a feature 
that is generally clearly missing from the ECMWF analyses. 
What is more, the amplitude of the daily cycle is variable, 
suggesting that the local ocean and atmospheric dynamics 
impacts the SST measured by the buoys. This is indeed the 
case for the period from 29 April to 5 May (time period A 
in Fig. 9): the observed SST is slightly cooler and, crucially, 
is missing the diurnal cycle found in the rest of the time se 
ries. Looking at co-located wind data (not shown), we do not 
find any clear modification, suggesting that the reason for 
this behavior in the SST data is principally oceanic and not 
atmospheric. Indeed, if we look at the buoys’ location dur 
ing that time period, we see that they are trapped within an 
eddy core (Fig. 10), and, significantly, it is a cold eddy. It 
is known that these eddies generate an upwelling within their 
core, leading to colder and vertically more homogeneous sur 
face and near surface waters. The buoy data suggest that this 
upwelling more than compensates the diurnal warming and 
eliminates the near surface stratification. During time period 
A, the average diurnal cycle measured by the two buoys is 
rather weak (Fig. 11a and b). 
Once the buoys move out of the eddy core (Fig. 12), the 
diurnal cycle is once again found in the data. This is visible 
in Fig. 9 during time period B, and in Fig. 1 lc and d, where 
the daily amplitude in SST exceeds 0.5 K (when it was less 
than 0.2 K in time period A). 
Looking at the evolution of SST 5 min percentiles enables 
to gauge the small-scale variations in temperature near the