Journal of University of Chinese Academy of Sciences >
Developments and verifications of a new large size ENSO ensemble prediction system
Received date: 2009-12-08
Online published: 2010-05-15
Supported by
Supported by the Natural Science Foundation of China (40805033), the Chinese Academy of Science (KZCX2-YW-202), the Chinese COPES Project (GYHY-200706005), and the National Basic Research Program of China (2006CB403600)
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
ZHENG Fei , ZHU Jiang . Developments and verifications of a new large size ENSO ensemble prediction system[J]. Journal of University of Chinese Academy of Sciences, 2010 , 27(3) : 420 -431 . DOI: 10.7523/j.issn.2095-6134.2010.3.017
[1] Wang C, Picaut J. Understanding ENSO physics—A review. Earth Climate: the ocean-atmosphere interaction
[J]. Geophys Monogr Amer Geophys Union, 2004, 147: 21- 48.
[2] Jin E K, James L K, Wang B, et al. Current status of ENSO prediction skill in coupled ocean-atmosphere models
[J]. Climate Dyn, 2008, 31: 647-664.
[3] Kirtman P B. The COLA anomaly coupled model: ensemble ENSO prediction
[J]. Mon Wea Rev, 2003, 131: 2324-2341.
[4] Chen D, Cane M A. El Nino prediction and predictability
[J]. J of Computational Physics, 2008, 227: 3625-3640.
[5] Palmer T N. Predicting uncertainty in forecasts of weather and climate
[J]. Rep Prog Phys, 2000, 63: 71-116.
[6] Moore A M, Kleeman R. Skill assessment for ENSO using ensemble prediction
[J]. Quart J Roy Meteor Soc, 1998, 124: 557-584.
[7] Kleeman R, Moore A M. A new method for determining the reliability of dynamical ENSO predictions
[J]. Mon Wea Rev, 1999, 127: 694-705.
[8] DeWitt D G. Retrospective forecasts of interannual sea surface temperature anomalies from 1982 to present using a directly coupled atmosphere-ocean general circulation model
[J]. Mon Wea Rev, 2005, 133: 2972-2995.
[9] Mitchell H L, Houtekamer P L, Pellerin G. Ensemble size, balance, and model-error representation in an ensemble Kalman filter
[J]. Mon Wea Rev, 2002, 130: 2791-2808.
[10] Zheng F, Zhu J, Zhang R H, et al. Ensemble hindcasts of SST anomalies in the tropical Pacific using an intermediate coupled model
[J]. Geophys Res Lett, 2006, 33: L19604. doi: 10.1029/2006GL026994.
[11] Zheng F, Zhu J, Zhang R H. Impact of altimetry data on ENSO ensemble initializations and predictions
[J]. Geophys Res Lett, 2007, 34: L13611. doi: 10.1029/2007GL030451.
[12] Zheng F. Researches on ENSO ensemble predictions . Beijing: Institute of Atmospheric Physics, Chinese Academy of Sciences, 2007.
[13] Evensen G. The ensemble Kalman filter: theoretical formulation and practical implementation
[J]. Ocean Dyn, 2003, 53: 343-367.
[14] Evensen G. Sampling strategies and square root analysis schemes for the EnKF
[J]. Ocean Dyn, 2004, 54: 539-560.
[15] Zheng F, Zhu J. Balanced multivariate model errors of an intermediate coupled model for ensemble Kalman filter data assimilation
[J]. J Geophys Res, 2008, 113: C07002. doi:10.1029/2007JC004621.
[16] Zheng F, Zhu J, Wang H, et al. Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles
[J]. Adv Atmos Sci, 2009, 26: 359-372.
[17] Zheng F, Wang H, Zhu J. ENSO ensemble prediction: initial condition perturbations vs. model perturbations
[J]. Chin Sci Bull, 2009, 54: 2516-2523.
[18] Keenlyside N, Kleeman R. On the annual cycle of the zonal currents in the equatorial Pacific
[J]. J Geophys Res, 2002, 107: doi:10.1029/2000JC0007111.
[19] Zhang R H, Zebiak S E, Kleeman R, et al. A new intermediate coupled model for El Ni o simulation and prediction
[J]. Geophys Res Lett, 2003, 30: 2012. doi:10.1029/2003GL018010.
[20] McCreary J P. A linear stratified ocean model of the equatorial undercurrent
[J]. Philosophical Transactions of the Royal Society (London), 1981, 298: 603-635.
[21] Zhang R H, Zebiak S E, Kleeman R, et al. Retrospective El Ni o forecast using an improved intermediate coupled model
[J]. Mon Wea Rev, 2005, 133: 2777-2802.
[22] Smith T M, Reynolds R W, Peterson T C, et al. Improvements to NOAAs historical merged land-ocean surface temperature analysis (1880-2006) . J Climate, 2008, 21: 2283-2296.
[23] Lagerloef G S E, Mitchum G, Lukas R, et al. Tropical Pacific near-surface currents estimated from altimeter, wind, and drifter data
[J]. J Geophys Res, 1999, 104: 23313-23326.
[24] Leith C E. Theoretical skill of Monte Carlo forecasts
[J]. Mon Wea Rev, 1974, 102: 409-418.
[25] Toth Z, Kalnay E. Ensemble forecasting at NCEP and the breeding method
[J]. Mon Wea Rev, 1997, 125: 3297-3319.
[26] Talagrand O, Vautard R, Strauss B. Evaluation of probabilistic prediction systems //Proceedings, ECMWF Workshop on Predictability, 1997.
[27] Hamill T M. Interpretation of rank histograms for verifying ensemble forecasts
[J]. Mon Wea Rev, 2001, 129: 550-560.
[28] Mason S J, Graham N E. Conditional probabilities, relative operating characteristics, and relative operating levels
[J]. Wea Forecasting, 1999, 14: 713-725.
[29] Balmaseda M A, Davey M K, Anderson D L T. Decadal and seasonal dependence of ENSO prediction skill
[J]. J Climate, 1995, 8: 2705-2715.
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