STOFFELEN, A.', GIESEN, R.', BENTAMY, A.?, 
"Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands 
Institut Francais de Recherche pour l’Exploitation de la Mer (IFREMER), Plouzane, France 
DVE 
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Wind stress on the ocean surface forces ocean dynamics 
and plays an essential role in the heat, momentum and 
gases exchange at the air-sea interface. Winds are highly 
variable at all temporal and spatial scales and not well 
captured on ocean eddy scales. Even with the growing 
constellation of scatterometers and other satellite wind 
instruments, sampling remains incomplete. On the other 
hand, users need wind forcing products at kilometric scale 
(ocean eddy scale) with global coverage and high temporal 
frequency. 
The Wind Thematic Assembly Centre (TAC) developed 
a unique repository of L3 and L4 surface wind and wind 
stress vector observation products of unmatched quality 
for both operational and climate purposes. The Wind: 
TAC product evolution relies on the evolving satellite 
constellation delivering basic data exploited for CMEMS 
products, mainly through the EUMETSAT Ocean and 
Sea Ice Satellite Application Facility (0SI SAF). Wind-TAC 
products contain wind Information from scatterometers, 
radiometers and the European Centre for Medium-Range 
Weather Forecasts (ECMWF) model. All L3 wind products 
contain ECMWF model winds that are collocated in space 
and time with scatterometer observations at L2. The L2 
scatterometer and ECMWF model winds are subsequently 
sampled and processed to L3 wind products in the same 
way. Therefore, they are subject to identical spatial and 
temporal sampling errors, which are evaluated against 
nominally gridded ECMWF products. 
Over the period 2015-2021, the L3 NRT (near-real time) 
wind product in the CMEMS catalogue has been updated 
with three newly available scatterometer datasets. 
Reprocessed (REP) L3 and L4 products have been 
introduced to complement NRT products, extending 
the time coverage back to 1992. The collocated stress- 
equivalent scatterometer and ECWMF model winds have 
been leveraged to identify biases between observed anc 
modelled wind fields and develop a locally bias-correctec 
ECMWF ocean forecing product. 
1. MAIN ACHIEVEMENTS 
FROM 2015 TO 2021 
1.1 Wind product evolutions 
L3 wind products are constrained by upstream satellite data 
availlability and associated L2 input products from OS! SAF 
New datasets are added only when data quality and tempora 
coverage are stable. Metop-A and Metop-B ASCAT datasets 
in the L3 NRT product were complemented by three new 
scatterometer datasets over the period 2015-2021, ScatSat-1 
ISCAT in 2018, Metop-C ASCAT in 2019 and HY-2B HSCAT 
in 2020. The total number of available daily scatterometer 
>bservations over the global ocean has thereby increased from 
around 4 million in 2015 to around 8 million in 2021 (Figure 1). 
Next to the sea surface vector winds and their latitudinaı 
and meridional components, L3 and L4 products contain 
several derived variables that are dedicated to downstream 
users. All products contain surface wind stress and its 
ıatitudinal and meridional components. In addition, L3 daily 
and L4 6-hourly products include: 
- the divergence and rotation of the wind speed vector 
field, 
the divergence and rotation of the wind stress vector 
field. 
ALULL3 wind datasets include ECMWF model winds collocated 
in space and time with each scatterometer observation. Since 
scatterometers sense the ocean roughness, measurements 
zontain no atmospheric information. To achieve the closest 
similarity to scatterometer winds, L2 ECMWF model winds 
are converted to 10 m stress-equivalent winds (U10S) by 
taking out effects of atmospheric stability and air mass 
density (de Kloe et al., 2017).