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Initial perturbations based on the Ensemble Transform (ET) technique in the NCEP Global Ensemble Forecast System
  • Published Date:
    2006
Filetype[PDF - 522.58 KB]


Details:
  • Corporate Authors:
    National Centers for Environmental Prediction (U.S.)
  • Series:
    Office note (National Centers for Environmental Prediction (U.S.)) ; 453
  • Document Type:
  • Description:
    Limitations on the initial perturbations used in the global operational ensemble forecast system at the National Centers for Environmental Prediction (NCEP) include the use of a climatologically fixed estimate of the analysis error variance and nonorthogonal paired bred vectors. In order to address these shortcomings, we introduced initial perturbations generated by the Ensemble Transform (ET) and ET with rescaling methods and compared them with the breeding ensemble in the NCEP operational environment. Both ET and ET with rescaling are second generation methods and generate initial perturbations that are consistent with the operational data assimilation (DA) system. The best possible initial analysis error variance from DA is used to restrain the initial perturbations that are orthogonal with respect to an inverse analysis error variance norm. In addition, a simplex transformation (ST) is imposed to ensure that the initial perturbations are centered and span a subspace with the maximum number of degrees of freedom. The variance is maintained in as many directions as possible within the ensemble subspace. It is shown that the perturbations are uniformly centered and distributed in different directions. The more ensemble members we have, the more orthogonal the perturbations will become. In the limit of infinite number of ensemble members, the perturbations will be orthogonal. Results show that the overall difference is not large although ET with rescaling performs best in almost all probabilistic scores and in terms of the forecast error explained by the perturbations. The forecast error variance can be explained best by pure ET with ST, which also has the highest time consistency between the analysis and forecast perturbations. The anomaly correlation of the ensemble mean from the breeding ensemble is slightly higher than that of the others for longer forecast lead times. A new one-sided breeding, which is centered by removing the mean of all perturbations, is tested for the same experimental period. It shows higher probabilistic scores than the paired ensemble. It is also found that the onesided bred perturbations span a high number of degrees of freedom and show a strong high time consistency.

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