Prediction of ocean state by data assimilation with the ensemble Kalman filter

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Abstract

We report the first application of the ensemble Kalman filter (EnKF) to an intermediate coupled atmosphere-ocean model by Zebiak and Cane [1987] into which the sea surface height (SSH) anomaly observations by TOPEX/POSEIDON (T/P) altimetry are assimilated. Smoothed estimates of the 54,403 dimensional state are obtained from 1981 observational points with 2048 ensemble members. While data assimilated are SSH anomalies alone, an ensemble experiment of 2002/03 El Nino event based on the EnKF can predict consistent Nino 3 sea surface temperature (SST) anomalies about 5 months in advance.

Journal

  • SCIS & ISIS

    SCIS & ISIS 2006(0), 1884-1889, 2006

    Japan Society for Fuzzy Theory and Intelligent Informatics

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