QUO VA DIS? THE MERCATOR OCEAN QUARTERLY VALIDATION BULLETIN
Charles Desportes (1), Marie Drévillon (1), Charly Régnier (1), Bruno Levier (1)
(1)
Mercator Ocean, 8-10 rue Hermès, 31520 Ramonville Saint-Agne, France, Email: qualif@mercator-ocean.fr
ABSTRACT
The Quovadis bulletin gives an estimate of the accuracy
of Mercator Ocean’s analyses and forecasts for the last
season. It also provides a summary of useful
information on the context of the production for this
period. Its contents are in constant evolution in order to
reach a useful and synthetic format. It contains at least:
a summary of the quality control of the input data, a
summary of data assimilation performance, information
on the climatic conditions (large scale atmospheric
forcing or coupling), forecast error statistics,
comparisons with independent data, intercomparisons of
systems with common metrics.
1. THE NEED OF A REGULAR QUALITY
REPORT
Following the spirit of the numerical weather forecast
centres quality reports, Mercator Ocean publishes a
quarterly bulletin: the Quarterly Ocean VAlidation
DISplay (QUOVADIS). In the framework of the
European project GMES/MyOcean, it measures and
keeps track of the performance of the monitoring and
forecasting operational systems in order to identify
possible improvements. This includes measuring the
impact of changes in the real time observation network
and giving useful information for the improvement of
this network. A second aim is to be a basis for regular
interactions with the scientific community and other
users so that they can derive the level of confidence for
the use of the products.
2. MAIN CONTENTS
2.1.
Status and evolutions of the systems
A short description and current status of the systems are
given. Main incidents that could have impacted the
quality of the products are described.
2.2. Summary of the availability and quality
control of the input data
A quarterly report is already produced for the input data
of the Mercator Ocean systems (SLA and in-situ
temperature and salinity profiles for the moment). The
Quovadis displays a synthesis of this document,
including the main technical information from the data
centres. In addition, it provides a monitoring coming
from the data assimilation system.
2.3. Information on the large scale climatic
conditions
Mercator Ocean is involved in the monthly seasonal
forecast expertise at Météo France. This chapter
summarizes the state of the ocean and atmosphere
during the season, as discussed in the “Bulletin
Climatique Global” of Météo France.
2.4. Accuracy of the products
In this section (the major section of the report), a great
amount of diagnostics are displayed for the global 1/12°
(PSY4), global ¼° (PSY3), the Atlantic and
Mediterranean zoom at 1/12° (PSY2) monitoring and
forecasting systems currently producing daily 3D
temperature, salinity and current and ice products. Two
new systems are operational since July 2011: IBI on the
North East Atlantic at 1/36° horizontal resolution that
includes the modelling of tides, and BIOMER, a global
biogeochemical model at 1° horizontal resolution forced
with PSY3.
Synthesis tables of the classical data assimilation
statistics (average and RMS of innovation) are
displayed for predefined spatial regions and for each
type of assimilated data (satellite altimetry, in situ
temperature and salinity profiles, and SST). The
different running systems are intercompared. An
example is shown in Fig. 1, where the first three
systems listed above are compared in Tropical and
North Atlantic regions.
Figure 2. Profiles of AMJ 2012 innovations of
temperature (°C), mean (solid line) and RMS (dotted
line) for PSY4 in blue, PSY3 in red, and PSY2 in yellow
in Dakar region.
Figure 1. Comparison of SLA data assimilation scores
(average misfit in cm) in AMJ 2012 and between all
available Mercator Ocean systems in the Tropical and
North Atlantic. The scores are averaged for all
available satellite along track data (Jason 1, Jason 2
and Envisat). For each region the bars refer
respectively to PSY2 (cyan), PSY3 (green), PSY4
(orange).
An other example is given in Fig. 2 where profiles of
temperature for different systems are compared to insitu data from Coriolis database.
Daily analyses are collocated with available
observations. This allows us producing, for instance,
potential temperature vs. salinity diagrams in
dynamically consistent regions. These graphs offer a
different view on the models’ representation of water
masses and oceanic processes (for instance
Mediterranean outflow in the Atlantic in Figure 3).
Comparisons are also made with observations that are
not yet assimilated in the system, like:
• drifting buoys,
• high resolution SST (OSTIA, ODYSSEA),
• tide gauges for the IBI system,
• Cersat dataset for the sea ice concentration,
• Ocean colour maps for the biochemistry
validation.
Figure3. Water masses (Theta, S) diagrams in the Bay
of Biscay for PSY3 in AMJ 2012. PSY3: yellow dots;
Levitus WOA09 climatology: red dots; in situ
observations: blue dots.
2.5. Forecast error statistics
In this section, the accuracy of the forecast is assessed.
Statistics are performed in layers over geographical
regions (ocean basins or smaller) and synthesized on bar
plots, or binned into 2°x2° boxes. Forecast errors are
also illustrated by maps of the RMS difference between
the forecast and the hindcast for all given dates of the
season.
From colocations, skill scores are computed, as can be
seen in Fig. 4. The Murphy Skill Score (see [1] for
instance) is described by Equation 1. This score is close
to 0 if the forecast is equivalent to the reference. It is
positive and aims towards 1 if the forecast is more
accurate than the reference.
2
− Obs m ) 

SS = 1 − k =1 n m =1 M
1
2
∑
 ∑ (Ref m − Obs m ) 
k =1  M m =1

n
1
M
∑  M ∑ (Forecast
m
(1)
Figure 4. Salinity skill scores for PSY3 (global ¼°) in
the 0-500m layer. Here the reference is the WOA 2005
climatology.
2.6. Monitoring of ocean and sea ice physics
In this section, some variables are monitored as time
series along a one year period, like the ice area, or the
surface temperature in Fig. 5.
Figure 5. Daily SST (°C) spatial mean for a one year
period ending in AMJ 2012, for PSY3 (in black) and
RTG-SST observations (in red).
2.7. R&D study or regional focus
In a last section, the Quovadis includes process studies,
assessment of the impact of altimetric data,
intercomparisons of systems based on current R&D
work at Mercator Ocean.
3. CONCLUSION
The Quovadis is still under construction but already
offers a general view of Mercator Ocean products
quality, using CORIOLIS observations as a reference. It
can be obtained at the following address:
www.mercatorocean.fr/eng/science/qualification.
REFERENCES
1. Wilks (2006), Statistical Methods in the
Atmospheric Sciences, Academic Press.