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Ensemble hindcasts of SST anomalies in the tropical Pacific using an intermediate coupled model
Author(s) -
Zheng Fei,
Zhu Jiang,
Zhang RongHua,
Zhou GuangQing
Publication year - 2006
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2006gl026994
Subject(s) - ensemble kalman filter , anomaly (physics) , data assimilation , ensemble forecasting , probabilistic logic , climatology , sea surface temperature , ensemble average , ensemble learning , kalman filter , meteorology , environmental science , computer science , statistics , mathematics , geology , extended kalman filter , geography , physics , artificial intelligence , condensed matter physics
Ensemble hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific are studied using an intermediate coupled model (ICM), in which an ensemble Kalman filter (EnKF) data assimilation system is implemented to provide the initial ensemble. A linear, first‐order Markov stochastic model is adopted to represent model errors. Parameters in the stochastic model are estimated by comparing observation‐minus‐forecast values over 30 years. Twelve‐month, 120 ensemble hindcasts are performed over the period 1995–2004, each with 100 ensemble members. This ensemble technique provides a simple method of extending the standard ICM forecasts to the probabilistic domain. The results show that the prediction skill of the ensemble mean is better than that of one single deterministic forecast using the same ICM. For the probabilistic perspective, those ensemble forecasts have their ensembles following observed SST anomaly variations well.