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

• Ueno Genta

  The Institute of Statistical Mathematics / JST CREST

• Higuchi Tomoyuki

  The Institute of Statistical Mathematics / JST CREST

• Kagimoto Takashi

  Frontier Research Center for Global Change / JAMSTEC

• Hirose Naoki

  Kyushu University

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

Keywords

• ensemble Kalman filter
• data assimilation
• ocean and atmosphere

Details

• CRID: 1390001205590374912
• NII Article ID: 130004672453
• DOI: 10.14864/softscis.2006.0.1884.0
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed