MERCATOR OCEAN JOURNA: 
SEPTEMBER 2021 
first guess SST field correlate spatially, and consequently, 
how far information from observations should be spread). 
A further change occurred in 2019, when the background 
error covariance length scales were adjusted to improve 
the feature resolution of the analyses (Fiedler et al., 2019). 
The impact of this change is illustrated in Figure 1. 
Simultaneously, SLSTR data from the Sentinel-3A satellite 
were included in the analysis. However, it had a limited 
impact because its data were bias corrected toward a 
11 March 2019 
8 
reference satellite and in situ data. SLSTR on Sentinel-3B 
was also added on 2019, along with NOAA20 VIIRS. Finally, 
following assessment of the impact, in 2020, SLSTR 
sensors were added to the reference dataset used to 
correct other satellites’ biases. This reduced the cold bias 
of the analyses compared to independent near surface 
Argo data. For example, in June 2020 tests, the global bias 
was reduced from -0.08°C to -0.04°C (see CMEMS Quality 
Information Document [QUID] for more details). 
L2 March 2019 
45 
SST (°C) 
Va 
‚Figure 1: The gulf stream region as represented in the global L4 foundation SST analyses on 11 and 12 March, respectively before and after 
the background error covariance length scales adjustment. The visible sharper transitions between warmer and colder water were the result 
of an upgrade to improve the feature resolution of the produc: 
To complement the updated NRT foundation SST production 
system, a new reprocessed foundation SST dataset was 
generated to supersede an existing dataset based on 
an old version of the OSTIA system (Donlon et al., 2012). 
The new dataset was generated using a configuration of 
the NRT OSTIA system described in Good et al., (2020), 
and input data included the climate data records from 
ESA CCI and interim climate data records from C3S. The 
resulting dataset covers 1981 to 2020, but routine updates 
were planned to extend the dataset forward in time. It was 
designed for users of the NRT product who need reliable 
historical data. The reprocessed foundation SST product 
was complemented by the ESA CCl and C3S product, which 
served climate users. Within CMEMS, this has been used 
to generate ocean monitoring indicators of global average 
SST, trends and annual anomalies. 
To complement global L4 products, SST-TAC provided a 
multi-sensor level 3 product covering the global ocean at 
1/10° horizontal resolution. This daily product resultec 
from the merging of several satellite SST level 2 data. 
Beforehand, data passed a significant number of quality 
zontrols and were inter-calibrated through an inter-sensor 
bias correction procedure. The procedure exploited a 
median field generated from a set of “best quality” sensors, 
to provide an estimate of the night time SST based on 
ariginal SST observations. New sources of data were 
included in the L3S global product. In 2019, observations 
from Suomi National Polar-orbiting Partnership (SNPP) 
Visible/Infrared Imager Radiometer Suite (VIIRS) and GM 
were included. In 2020, the coverage was significantly 
ıncreased with the integration of sea surface temperature 
data from N20, Sentinel-3A and S3B, GOES16, GOES17, 
Himawari, MSG Indian Ocean (Figure 2).