Developments and verifications of a new large size ENSO ensemble prediction system

doi:10.7523/j.issn.2095-6134.2010.3.017

Abstract

Abstract:

A new large size ensemble prediction system (EPS) is introduced in this paper. To minimize the forecast uncertainties for ENSO (El Ni o-Southern Oscillation) predictions, the EPS is firstly based on an intermediated coupled model (ICM), and then the ensemble Kalman filter (EnKF) data assimilation method is adopted to generate the initial ensemble conditions for the EPS through assimilating available oceanic observations. Meanwhile, a developed linear, first-order Markov stochastic model-error model is embedded in the EPS to represent the model uncertainties during the twelve-month ensemble forecast process. The prediction skill of the EPS is verified based on the (100-members) retrospective ensemble forecast experiment covering the period between November, 1992 and October, 2008 in both deterministic and probabilistic senses. This ensemble technique provides a successful method of extending the standard deterministic forecasts to the probabilistic domain. The verification 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 observational variations well, and provide additional information that could not be gleaned from a purely deterministic approach.

Key words: ENSO, ensemble prediction, ICM, EnKF, Markov stochastic model

CLC Number:

P456

ZHENG Fei, ZHU Jiang. Developments and verifications of a new large size ENSO ensemble prediction system[J]. , 2010, 27(3): 420-431.

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