The focus of our investigation team is the utilization of Jason-1 altimeter
data in support of monitoring, prediction, and process studies of interannual
variability in the tropical Pacific Ocean with extensions to the Indian Ocean.
Our team draws on individual, collective, and institutional strengths in
altimeter data processing and analysis, tropical ocean modeling, ocean data
assimilation, equatorial ocean theory, and basin-scale in situ observations.
Specific research topics covered by our team include altimetry-based process
studies of seasonal to interannual (i.e., El Niño) variability in the tropical
Pacific Ocean, the variability of the Indo-Pacific warm pool system, assimilation
and impact assessment of altimetry data and TAO/TRITON sea surface dynamic height
topography into ocean model and coupled ocean-atmosphere prediction models, and
analyses of equatorial wave dynamics via the joint analysis of Jason-1 and
TAO/TRITON moored observations. Several of these research topics represent a
broadening of our use of altimeter data in the tropical Pacific Ocean beyond
our accomplishments during the pre-launch phase, three-year Prime, and Extended
Mission of TOPEX/POSEIDON, and are in direct support of the objectives of the
CLIVAR (Climate Variability and Predictability) program.
For example, in Ballabrera et al.  a reduced order Kalman filter is used
to assimilate observed fields of the surface wind stress, sea surface temperature
and sea level into the coupled ocean-atmosphere model of Zebiak and Cane.
The method projects the Kalman filter equations onto a subspace defined by the
eigenvalue decomposition of the error forecast matrix, allowing its application
to high-dimensional systems. The Zebiak and Cane model couples a linear,
reduced-gravity ocean model with a single, vertical-mode atmospheric model.
The compatibility between the simplified physics of the model and each observed
variable is studied separately and together. The results show the ability of the
empirical orthogonal functions (EOFs) of the model to represent the simultaneous
value of the wind stress, sea surface temperature (SST), and sea level, when the
fields are limited to the latitude band 10°S – 10°N, and when the number of EOFs
is greater than the number of statistical significant modes. Figure 1 shows an
example of our research that isolates the individual and combined influences of
SST, surface wind, and altimetric observations on El Niño forecast skill as
measured by the Niño 3 SST index of the eastern equatorial Pacific.
In this first application of the Kalman filter to a coupled ocean-atmosphere
prediction model, the sea level fields are assimilated in terms of the Kelvin
and Rossby modes of the thermocline depth anomaly. An estimation of the error
of these modes is derived from the projection of an estimation of the sea level
error over such modes. The ability of the method to reconstruct the state of the
equatorial Pacific and to predict its time evolution is shown. The method is
quite robust for predictions up to six months, and was able to predict the onset
of the 1997 El Niño event fifteen months before its occurrence.
Ballabrera J., A.J. Busalacchi, R. Murtugudde, 2001: Application of a reduced order Kalman filter to assimilate sea level, sea surface temperature, and wind stress into a coupled atmosphere-ocean model: Impact on the prediction of El Niño. J. Clim. (in press).