Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction

By C. Keppenne, M. Rienecker, N. Kurkowski, D. Adamec. 2005

73 citations recorded by the source corpus.

To compensate for a poorly known geoid, satel- lite altimeter data is usually analyzed in terms of anomalies from the time mean record. When such anomalies are as- similated into an ocean model, the bias between the clima- tologies of the model and data is problematic. An ensemble Kalman filter (EnKF) is modified to account for the presence of a forecast-model bias and applied to the assimilation of TOPEX/Poseidon (T/P) altimeter data. The online bias cor- rection (OBC) algorithm uses the same ensemble of model state vectors to estimate biased-error and unbiased-error co- variance matrices. Covariance localization is used but the bias covariances have different localization scales from the unbiased-error covariances, thereby accounting for the fact that the bias in a global ocean model could have much larger spatial scales than the random error. The method is applied to a 27-layer version of the Posei- don global ocean general circulation model with about 30- million state variables. Experiments in which T/P altime- ter anomalies are assimilated show that the OBC reduces the RMS observation minus forecast difference for sea-surface height (SSH) over a similar EnKF run in which OBC is not used. Independent in situ temperature observations show that the temperature field is also improved. When the T/P data and in situ temperature data are assimilated in the same run and the configuration of the ensemble at the end of the run is used to initialize the ocean component of the GMAO cou- pled forecast model, seasonal SSH hindcasts made with the coupled model are generally better than those initialized with optimal interpolation of temperature observations without al- timeter data. The analysis of the corresponding sea-surface temperature hindcasts is not as conclusive.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/03bb59e5161182085c8b439ad9b01b8945e05124

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Citations in the corpus

Accounting for Model Errors in Ensemble Data Assimilation (2009, methodology): “…method (DdSM)] has been successfully tested, for example, by Dee and Todling (2000), Carton et al. (2000), Chepurin et al. (2005) in three-dimensional variational data assimilation (3DVAR) and optimal interpolation (OI) data assimilation systems, and by Keppenne et al. (2005) in an EnKF system.”
Obstacles to High-Dimensional Particle Filtering (2008, background): “Since it cannot be determined exactly given imperfect observations, we consider x to be a random variable with pdf p(x).”
State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency (2007, methodology): “This parameter could be tuned to optimize the filtering system [Keppenne et al., 2005].”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, methodology): “Schemes to correct for systematic model errors in EDA have been proposed (Baek et al. 2006; Keppenne et al. 2005), but in the experiments shown here we have not accounted for biases in the ensemble mean first guess.”
Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter (2007, methodology): “: CORRECTING FOR FORECAST BIAS IN SOIL MOISTURE ASSIMILATION W09410 Silva, 1998; Keppenne et al., 2005].”