Assimilation of along-track altimeter data in the Tropical Pacific region of a global OGCM ensemble

Identical-twin experiments are performed with an ocean general
circulation model ensemble to investigate the potential for correction
of subsurface ocean model states through assimilation of altimetric
sea level observations with the Ensemble Kalman Filter (EnKF). The
EnKF provides a convenient extension to existing ensemble prediction
systems . Observations are simulated for the Topcial Pacific by
sampling a truth run at 10-day intervals at the TOPEX/POSEIDON
along-track measurement points and adding realistic instrument and
orbit errors. Ensemble spread is generated by perturbing the
best-guess forcing fields. The perturbations are based on a
multivariate EOF decomposition of differences between two reanalysis
products. The effectiveness of the assimilation is investigated by
comparison of the forecasts and analyses with a control run and with
the truth. Time series of subsurface state variables along the
equator show that the analyses are closer to the truth than the
control in all cases, indicating a significant potential for improved
ENSO forecast initialisation. A second assimilation run with an
Ensemble Square-Root Filter (ESRF) shows that the analyses are very
similar to those from the EnKF. However, ensemble spread in the
subsurface state variables is found to be a poor proxy for the true
analysis error in this experiment, in particular in the case of the
ESRF. While the sea level analyses remain close to the truth,
persistent offsets are introduced in the subsurface state, suggesting
a role for bias correction schemes in ensemble methods.