the dependence of short-range ocean forecasts on satellite ...godae-data/govst-iv/... · the...

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)

http://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%.



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



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







Aviso Ocean Current

Bluelink Assim Bluelink Model

Comparison of

SLA analyses &

model

Standard

deviation of

model-obs

misfits using

along-track SLA

from RADS



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);



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




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.

≠ ≈

≈ ≠ ≠




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




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.



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.





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.



Differences between model runs



% 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



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



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%



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..




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 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.





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.



Differences between model runs



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


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