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www.cmar.csiro.au/staff/oke/
The dependence of short-range ocean forecasts
on satellite altimetry
Peter Oke, Madeleine Cahill, David Griffin
CSIRO Marine and Atmospheric Research
September 2012
“The most exciting phrase to hear in science, the one that heralds the most discoveries, is
not ‘Eureka!’, but ‘That’s funny…’ ”, Isaac Asimov (1920-1992)
www.cmar.csiro.au/staff/oke/
Talk Outline
Bluelink model and data assimilation
Observing System Experiments (OSEs):
0 ALTIM, 1 ALTIM, 2 ALTIM, 3 ALTIM
Evaluation
Conclusions:
1. Without altimetry an eddy-resolving model generates
many fictitious eddies.
2. A single altimeter can constrain the strong mesoscale
signals, but often generates fictitious eddies.
3. Multiple altimeters are needed to constrain the
mesoscale signals.
4. A 3rd altimeter adds constraint over 2 altimeters.
5. Assimilation of 3 altimeters reduces the SLA-error in
an eddy-resolving model by about 30%.
www.cmar.csiro.au/staff/oke/
Bluelink model and data assimilation
Ocean Forecasting Australia Model (OFAM)
1/10o resolution around Australia (90-180E; 76S-20N)
ERA-Interim forcing Schiller, A., P. R. Oke, G. B. Brassington, M. Entel, R. Fiedler, D. A. Griffin, J. Mansbridge, K. Ridgway 2008:
Eddy-resolving ocean circulation in the Asian-Australian region inferred from an ocean reanalysis
effort. Progress in Oceanography, 76, 334-365.
Bluelink Ocean Data Assimilation System
Ensemble Optimal Interpolation (EnOI)
Assimilated in situ T/S, SST, along-track SLA Oke, P. R., G. B. Brassington, D. A. Griffin, A. Schiller 2008: The Bluelink Ocean Data Assimilation System
(BODAS). Ocean Modelling, 20, 46-70.
Data assimilation concept: project model-obs misfits onto an
ensemble fields using least-squares
e.g., SLA is projected onto SLA, T, S, U, V
www.cmar.csiro.au/staff/oke/
Evaluation of the Bluelink system
Assimilation involves 2 steps:
Step 1: Combine model background field with observations
Step 2: Initialise the model
www.cmar.csiro.au/staff/oke/
Evaluation of the Bluelink system
Assimilation involves 2 steps:
Step 1: Combine model background field with observations
Step 2: Initialise the model
Comparison of
SLA analyses &
model
Standard
deviation of
model-obs
misfits using
along-track SLA
from RADS
Aviso Aust. IMOS GSLA
BL 3d-EnOI Analysis Bluelink Model
www.cmar.csiro.au/staff/oke/
Evaluation of the Bluelink system
Assimilation involves 2 steps:
Step 1: Combine model background field with observations
Step 2: Initialise the model
Aviso Ocean Current
Bluelink Assim Bluelink Model
Comparison of
SLA analyses &
model
Standard
deviation of
model-obs
misfits using
along-track SLA
from RADS
www.cmar.csiro.au/staff/oke/
Observing System Experiments (OSEs)
Configuration
Identical initial conditions from the end of a ~20-year run
2/2008 – 3/2009 (i.e., interleaving Jason-1 and -2 orbits)
Analyse only months 2-13
All experiments assimilate data from in situ T/S and satellite
SST, and:
Zero altimeters;
One altimeter (Jason 2);
Two altimeters (Jason 1+Jason 2);
Three altimeters (Jason 1+Jason 2+Envisat);
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Comparisons with drifters
In the WBC region, where the eddy signal is strong …
OSE without altimetry gets some of the large eddies
OSE with 1 or 3 altimeters show consistent agreement
www.cmar.csiro.au/staff/oke/
Comparisons with drifters
In the WBC region,
where the eddy
signal is strong …
OSE without
altimetry gets
some of the large
eddies
OSE with 1 or 3
altimeters show
consistent
agreement
Demonstrates
that altimetry is
critical for
constraining
eddy-resolving
models.
≠ ≈
≈ ≠ ≠
www.cmar.csiro.au/staff/oke/
Comparisons with drifters
In the EBC region, where the eddy signal is weaker …
OSE without altimetry generates fictitious eddies
Poor agreement between the OSE with 1 and 3 altimeters
www.cmar.csiro.au/staff/oke/
Comparisons with drifters
In the EBC region, where
the eddy signal is
weaker …
OSE without altimetry
generates fictitious
eddies
Poor agreement
between the OSE with
1 and 3 altimeters
≠ ≈
≈ ≠ Demonstrates that
a single altimeter is
insufficient to
constrain an eddy-
resolving models.
www.cmar.csiro.au/staff/oke/
Area-averaged RMS differences between observed and modelled
SLA in each OSE in the whole Australian domain (pink), and in high-
variability regions (yellow).
Comparisons with observations:
In high-variability regions assimilating data
from 3 altimeters reduces model-obs mis-fits
from 12 cm to 8.6 cm (~28%);
… and improves correlations from 0.52 to 0.75.
www.cmar.csiro.au/staff/oke/
Comparisons with observations:
Area-averaged RMS differences between observed and modelled
SLA in each OSE in the whole Australian domain (pink), and in high-
variability regions (yellow).
In high-variability regions, assimilation of the 1st altimeter has
the biggest impact, reducing model-obs misfits by ~ 21%;
… and assimilation of the 2nd and 3rd altimeter has less impact,
further reducing model-obs misfits by ~5% and ~2.5% each.
www.cmar.csiro.au/staff/oke/
% Change = RMS (Run A – Run B) / RMS (obs signal) x 100
% Error = RMS Error / RMS (obs signal) x 10un
1st altimiter
constrains the
seasonal Rossby
waves
2nd & 3rd altimeter
constrains the
eddy field
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Impact on sub-surface T &S
The 1st altimeter has the greatest impact on T/S, but the 2nd and
3rd eddy also have a significant impact
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Impact on sub-surface T &S
The 1st altimeter has the greatest impact on T/S, but the 2nd and
3rd eddy also have a significant impact
www.cmar.csiro.au/staff/oke/
Conclusions
Altimetry is critical for constraining an eddy resolving model:
1. Without altimetry an eddy-resolving model generates many
fictitious eddies
2. A single altimeter can constrain the strong mesoscale signals
3. Multiple altimeters are needed to constrain the mesoscale
signals properly
4. A 3rd altimeter adds constrain over 2 altimeter
5. Assimilation of 3 altimeters reduces the error of an eddy-
resolving model by about 30%
www.cmar.csiro.au/staff/oke/
Abstract
Short-range ocean forecasting and reanalyses routinely combine
observations from satellite altimetry, satellite sea surface temperature,
and in situ temperature and salinity from Argo, XBT, and moorings, to
initialise global and regional ocean models. Each data type provides
independent information that helps represent different aspects of the
ocean circulation. The most critical observation type for eddy-resolving
applications is satellite altimetry – quantifying the variability of the
mesoscale ocean circulation in time and space. Results from recently
completed ocean reanalyses will be presented, showing that the accuracy
of ocean reanalyses is directly related to the number and quality of
satellite altimeters..
www.cmar.csiro.au/staff/oke/
Area-averaged RMS differences between observed and modelled
SLA in each OSE in the whole Australian domain (pink), in high-
variability regions (yellow), and in low-variability regions (green).
Comparisons with observations:
In high-variability regions assimilating data from 3 altimeters
reduces model-obs mis-fits from 12 cm to 8.6 cm;
… and improves correlations from 0.52 to 0.75.
www.cmar.csiro.au/staff/oke/
Comparisons with observations:
Area-averaged RMS differences between observed and modelled
SLA in each OSE in the whole Australian domain (pink), in high-
variability regions (yellow), and in low-variability regions (green).
In high-variability regions, assimilation of the 1st altimeter has
the biggest impact, reducing model-obs misfits by ~ 21%;
… and assimilation of the 2nd and 3rd altimeter has less impact,
further reducing model-obs misfits by ~5% and ~2.5% each.
www.cmar.csiro.au/staff/oke/
Evaluation – some subtleties
SLA errors of assimilating eddy-resolving models are 5-10 cm;
SLA errors in altimetry are 3-5 cm;
Changes in model-obs mis-fits when an altimeter is with-held
are ~1-2 cm
… with blunt tools, it’s hard to
fine tune a sculpture