Ensemble Construction and Verification of the Probabilistic ENSO Prediction in the LDEO5 Model

Cheng, Yang; Tang, Youmin; Jackson, Peter L.; Chen, Dake; Deng, Ziwang

doi:10.1175/2010jcli3453.1

Cited by 23 publications

(26 citation statements)

References 75 publications

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“…The spreads of Exp3–6 retain relatively large values and are closer to the RMSE (Figure 12), compared with those of E1∼2. A good ensemble system should show an ensemble spread close to the RMSE [e.g., Cheng et al , 2010]. Thus, Exp3–6 is better than E1∼2, showing the advantage of the inflation method.…”

Section: Validation Of Assimilation Experimentsmentioning

confidence: 99%

Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

Deng¹,

Tang²,

Freeland³

2011

J. Geophys. Res.

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[1] The efficacy of several model error schemes in the Ensemble Kalman Filter (EnKF) data assimilation is investigated through a series of sensitivity experiments, in which the Argo and other in situ temperature and salinity profiles are assimilated into an ocean general circulation model (OGCM) for the Pacific Ocean. Different schemes for combining the additive inflation, multiplicative inflation, one-step bias correction and two-stage bias correction are evaluated in the framework of the EnKF. Experimental results indicate that the additive inflation is the key technique that can maintain ensemble spread in an adequate range. When sufficient observations are available, the assimilation system with additive inflation scheme can efficiently reduce both model bias and random errors. The combination of additive inflation and multiplicative inflation can further improve the performance of the assimilation system, in particular when the additive inflation underestimates model error. The bias correction schemes, the one-step method and the persistent bias method are effective in reducing the model bias only within a relatively short initial assimilation period and in some regions. Further improvement from the bias correction schemes is not evident as the assimilation period increases.

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Section: Validation Of Assimilation Experimentsmentioning

confidence: 99%

Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

Deng¹,

Tang²,

Freeland³

2011

J. Geophys. Res.

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“…4 Dependence of parameter optimization on the parameter inflation factor. Y-axis represents the time averaged ensemble mean RMSE (unit: °C) of prior SST anomaly synoptic-scale atmospheric processes, westerly wind bursts and the Madden-Julian oscillation) of the atmosphere also influences ENSO predictability (e.g., Karspeck et al 2006;Cheng et al 2010), here we mainly focus on the model error. Thus, no stochastic atmospheric forcing (wind) is imposed in ENSO prediction in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, no stochastic atmospheric forcing (wind) is imposed in ENSO prediction in this study. The other is that bias correction of SST anomaly is not performed, which may benefit PO (Cheng et al 2010). …”

Section: Methodsmentioning

confidence: 99%

A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model

Han

Zhang

et al. 2015

Clim Dyn

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“…Ensemble generation methods are typically implemented with the awareness that ocean perturbations are important, though the effect has mostly been studied for the surface ocean. For example, singular vectors in combination with stochastic optimal wind perturbation (Cheng et al, 2010), perturbing the model coupling physics (Luo et al, 2005) and also the implementation of bred vectors (Cai et al, 2003), have been studied. Beyond two months most techniques showed too little ensemble spread as compared to the forecast error (Vialard et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Ensemble initialization of the oceanic component of a coupled model through bred vectors at seasonal-to-interannual timescales

Baehr¹,

Piontek²

2014

Geosci. Model Dev.

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Abstract. We evaluate the ensemble spread at seasonal-tointerannual timescales for two perturbation techniques implemented in the ocean component of a coupled model: (1) lagged initial conditions as commonly used for decadal predictions; (2) bred vectors as commonly used for weather and seasonal forecasting. We show that relative to an uninitialized reference simulation the implementation for bred vectors can improve the ensemble spread compared to lagged initialization at timescales from one month up to three years.As bred vectors have so far mostly been used at short timescales, we initially focus on the implementation of the bred vectors in the ocean component. We introduce a depthdependent vertical rescaling norm, accounting for the vertical dependence of the variability, and extending the commonly used upper-ocean rescaling norm to the full water column. We further show that it is sufficient for the (sub-surface) ocean to breed temperature and salinity (i.e., scalar quantities), and rely on the governing physics to carry the temperature and salinity perturbations to the flow field.Using these bred vectors with a rescaling interval of 12 months, we initialize hindcast simulations and compare them to hindcast simulations initialized with lagged initial conditions. We quantify the ensemble spread by analyzing Talagrand diagrams and spread-error ratios. For both temperature and salinity, the lagged initialized ensemble is particularly under-dispersive for the first few months of predictable lead time. The ensemble initialized with bred vectors improves the spread for temperature and salinity for the 0-700 m and 1000-3500 m means, compared to the lagged ensemble at lead times of several months to one year. As the lead time increases to years, the differences between the two ensemble initialization techniques become more difficult to discern. While the results need to be confirmed in an initialized framework, the present analysis represents a first step towards improved ensemble generation at the transition from seasonal to interannual timescales, in particular at lead times up to one year.

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Ensemble Construction and Verification of the Probabilistic ENSO Prediction in the LDEO5 Model

Cited by 23 publications

References 75 publications

Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean

A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model

Ensemble initialization of the oceanic component of a coupled model through bred vectors at seasonal-to-interannual timescales

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