Development of the FOAM ocean data assimilation system
Matthew Martin1, Adrian Hines, John Stark, Rosa Barciela and John Hemmings
1Met
Office, FitzRoy Road, Exeter, Devon, EX1 3PB, UK. matthew.martin@metoffice.gov.uk
Abstract: The FOAM ocean forecasting system assimilates in situ temperature and salinity profile data, in
situ and satellite SST data and satellite altimeter SSH data. Recent improvements to the data assimilation
system include the implementation of a scheme which assimilates sea-ice concentration and velocity data
and a scheme which enables the assimilation of high resolution SST data. A coupled physical-biological
model has also been set up which is being used to develop a scheme to assimilate satellite ocean colour
data.
1. Introduction
The Forecasting Ocean Assimilation Model
(FOAM) is an operational deep-ocean forecasting
system which produces analyses and 5-day
forecasts of the ocean currents, temperature,
salinity and sea-ice. It assimilates in situ
temperature and salinity profile data, in situ and
satellite sea surface temperature (SST) data, and
satellite altimeter sea surface height (SSH) data.
The assimilation method (Bell et al. 2000) has
been developed from the Analysis Correction
scheme, an iterative approximation to Optimal
Interpolation. Recent improvements to the data
assimilation system include the implementation of
new data sources for assimilation. An overview of
the methods implemented for the assimilation of
each of these data types is given in the next
sections, followed by a summary.
The distribution of the ice concentration increments
onto the ice thickness categories could be done in
a number ways. The optimal solution would
depend on an accurate knowledge of the model
thickness error covariance, which we do not know
a priori. Two main sources of model error exist:
errors due to ice motion and errors due to thermal
forcing. Whereas thermal forcing has a greater
impact on thinner ice categories, errors in the
divergence of the flow affect all categories equally.
The solution we adopted was to add or remove
thin ice, giving the smallest thermodynamic impact.
Where the analysis increment is growing new ice
on open water, the new ice is added with a
thickness of 0.1m.
2. Sea-ice concentration and velocity
assimilation
The sea-ice model used in the development
version of the FOAM system has recently been
upgraded to include an elastic-viscous-plastic
(EVP) rheology as described by Hunke and
Dukowicz (1997), and a representation of the ice
thickness distribution (ITD) which is evolved
according to Thorndike et al. (1975). A project has
been undertaken to improve both the sea-ice
analysis and forecast through the assimilation of
sea-ice concentration and velocity data into the
model.
The assimilation is performed within the standard
Analysis Correction framework (Bell et al. 2000).
An analysis field for the sea-ice concentration is
produced using an optimal interpolation approach
similar to that used for SST. The analysis
increments are nudged in evenly over the
assimilation period so that the total increment is
applied by the end of the assimilation period.
Figure 1: Time series of sea-ice concentration RMS
errors for (a) Arctic and (b) Antarctic. Control (dark blue),
assimilation of concentration data only (pink),
assimilation of velocity data only (red) and assimilation
of concentration and velocity data (light blue).
Passive microwave satellite derived sea-ice
concentration data have been assimilated for a
one year integration. The resulting sea-ice
concentration errors in the Arctic are presented in
Figure 1. These show that the assimilation has
some success in bringing the modelled sea-ice
closer to the observations (as represented by the
HadISST analysis). The assimilation revealed
some significant biases between the model and
observations in some regions.
accuracy but affected by cloud) satellite
measurements
which
are
processed
by
Medspiration. The level 2 processed observations
are assimilated by the FOAM system. Figure 3
shows examples of AMSR-E and SEVIRI
observations for one day. Other sensors include
AVHRR, TMI and AATSR.
Sea-ice velocity data has also been assimilated
using an algorithm which alters the effective wind
stress on the ice. Using the velocity components
as control variables, the ice velocity observations
are assimilated using a multivariate method
described by Daley (1991). By introducing
correlations between the velocity components the
amount of divergence can be controlled. The
velocity increments calculated in this way are then
used to produce a change to the stresses which
are applied to the ice. Overall, the assimilation
improves the representation of the ice motion, both
in the analysis, as shown in Figure 2, and in the
forecast.
0.14
Ice Velocity Comparison (North)
RMS Error (m/s)
0.12
0.1
0.08
0.06
0.04
0.02
0
Nov-99 Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00
Figure 2: RMS differences between the FOAM test runs
and IFREMER ice observations. There are no IFREMER
observations from May to September due to the melt
season.
Control
(dark
blue),
assimilation
of
concentration data only (pink), assimilation of velocity
data only (red) and assimilation of concentration and
velocity data (light blue).
3. High resolution satellite SST data
assimilation
Medspiration is an ESA funded project
(www.noc.soton.ac.uk/lso/medspiration)
to
produce SST products at high resolution and
accuracy for the Atlantic as part of the GODAE
High Resolution SST (GHRSST) Pilot Project. As
part of the testing phase of this project, the FOAM
system was set up to assimilate the data from
Medspiration on a daily basis. The data includes
microwave (low resolution and accuracy, good
coverage) and infrared (high resolution and
Figure 3: Data coverage for 1st Feb 2005 for (a) AMSR-E
(1306768 observations) and (b) SEVIRI (1712698
observations).
A parallel version of the operational 1/9˚ north
Atlantic FOAM system was run which assimilated
the Medspiration SST data in place of the satellite
data used in the operational system (2.5˚ AVHRR
data). Figure 4 shows a comparison of the current
system and the parallel system against in situ SST
data. Both the mean and RMS errors are improved
using the new data. The assimilation scheme
requires further tuning before the maximum
benefits of these data are realised.
Figure 4: (a) Mean and (b) RMS errors of 24 hour
forecast vs in situ SST data in the north Atlantic for a run
assimilating low resolution data (red) and high resolution
data (blue).
4. Assimilating satellite ocean colour data
As a part of the Centre for observation of Air-Sea
Interactions
and
fluXes
(CASIX)
project
(www.pml.ac.uk/casix), work is underway to
improve estimates of air-sea fluxes of CO2 by
embedding the HadOCC ecosystem model
(Palmer and Totterdell, 2001) into the FOAM
system and by assimilating satellite ocean colour
data.
A global version of FOAM-HadOCC has completed
an annual cycle starting February 2000 without
assimilation of ocean colour data. Figure 5 shows
some examples of the surface chlorophyll from the
model run (top), together with data from SeaWifs in
the north east Atlantic (bottom) for 3 different
weeks in March, which coincide with the onset of
the spring phytoplankton bloom.
As part of the development of an ecosystem data
assimilation scheme, a one-dimensional test bed
version of HadOCC forced by FOAM data has
been
set
up.
This
assimilates
surface
measurements of log(Chlorophyll) as part of an
identical-twin experiment.
Figure 5: (a) Modelled and (b) observed chlorophyll data,
March 2000.
Balancing increments are applied to the
unobserved variables in the ecosystem model in
such a way that carbon and nitrogen are
conserved locally. In this way, the scheme aims to
correct errors associated with incorrect partitioning
of material between carbon and nitrogen pools.
The increments are determined from a tunable
error covariance model with temporal variations
dependent on the biological model dynamics.
concentration and velocity data from satellites has
been shown to produce much improved analyses
and forecasts. Assimilation of high resolution
satellite SST data also has a positive impact over
assimilating only low resolution data, although
more work is needed to realize the full benefits of
this data source. An improved description of the
ocean ecosystem is also being sought via
assimilation of ocean colour data into a coupled
physical-biological model.
Acknowledgements:
This work was partially supported by funding from the
European Space Agency (ESA) under contract number
17334/03/NL/FF.
References:
Figure 6: Results from a 1D test bed assimilating
log(chlorophyll) data.
5. Summary
A number of recent developments to the FOAM
ocean data assimilation system have been
described.
The
assimilation
of
sea-ice
Bell, M.J., Forbes, R.M. and A. Hines. Assessment of
the FOAM global data assimilation system for real-time
operational ocean forecasting. J. Mar. Syst., 25, pp. 1-22,
2000.
Hunke, E. and J. Dukowicz. An elastic-viscous-plastic
model for sea ice dynamics. J. Phys. Oceanogr., 27,
1849-1867, 1997.
Palmer, J.R. and I.J. Totterdell. Production and export in
a global ocean ecosystem model. Deep-Sea Research I,
48, 1169-1198, 2001.
Thorndike, A., D. Rothrock,G. Maykut and R. Colony.
The thickness distribution of sea ice. J. Geophys. Res.,
80, 4501-4513, 1975.