Assimilation of satellite altimetry and temperature observations in a 1/3-degree global ocean model with an ensemble Kalman filter

A parallel Ensemble Kalman Filter (EnKF) is used to assimilate sea surface height (SSH) and temperature observations into the Global Modeling and Assimilation Office's (GMAO) global quasi-isopycnal OGCM. The EnKF relies on multivariate forecast-error covariances between the observables and the model prognostic variables to compute the Kalman gain matrix and analysis increments. The forecast-error covariances are estimated adaptively from an ensemble integration of the OGCM and are used to update each ensemble member. This is in contrast with more conventional assimilation algorithms such as optimal interpolation (OI), 3DVAR or 4DVAR, in which the error covariances do not vary with time.

In the first part of this talk, results from a 10-year-long integration of the OGCM while assimilating SSH observations from TOPEX/Poseidon and in situ temprature observations from TAO and XBT are presented. The model temperature, salinity and current fields from the EnKF run are compared to the corresponding fields of (a) a control integration with no assimilation, (b) of a run where an optimal interpolation (OI) algorithm is used to assimilate only the temperature data, and (c) of available observations of the ocean subsurface. Special attention is given to the impact of the satellite altimetry data on the model states produced by the assimilation.

One of the GMAO's objectives is to use the EnKF in its routine seasonal-to-interannual (SI) ensemble forecasting procedure. An ensemble of coupled-model forecasts can be initialized with the final state of each ensemble member at the end of the EnKF run. The procedure is discussed in the second part of this talk and Nino-3 SST hindcasts initialized with the EnKF are compared to OI-initialized hindcasts made with the GMAO's production SI prediction system.