Applications of Conditional Nonlinear Optimal Perturbations to Ensemble Prediction and Adaptive Observation

  • Zhina Jiang
  • Hongli Wang
  • Feifan Zhou
  • Mu Mu


Conditional nonlinear optimal perturbation (CNOP), which is a natural extension of the singular vector into the nonlinear regime, is applied to ensemble prediction study and the determination of sensitive area in adaptive observations. The purpose of this paper is to summarize the recent progresses of the authors in these fields.

For the ensemble prediction part, a quasi-geostrophic model is used under the perfect model assumption. A series of singular vectors (SVs) and CNOPs have been utilized to generate the initial ensemble perturbations. The results are compared for forecast lengths of up to 14 days. It is found that the forecast skill of samples, in which the first singular vector (FSV) is replaced by CNOP, is comparatively higher than that of samples composed of only SVs in the medium range (day 6 $∼$ day 14). This conclusion is valid under the condition that analysis error is a kind of fast-growing ones regardless of its magnitude, whose nonlinear growth is faster than that of FSV in the later part of the forecast.

The potential application of CNOP to identify the data-sensitive region in targeting strategy is explored by using the 5th generation Pen-State University/National Center for Atmosphere Research mesoscale Model (MM5) and its adjoint system. The differences between FSVs and CNOPs and their evolutions are studied in two precipitation cases in July 2003 and in August 1996 respectively. It is found that the structures of CNOPs differ much from those of FSVs as well as the developments of their total energies. The results of sensitivity experiments indicate that the forecast results are more sensitive to the CNOP-type initial errors than to FSV-type in terms of total energy. These results suggest that it is feasible to use CNOP to identify the sensitive region in adaptive observations.


Singular vector Conditional nonlinear optimal perturbation Ensemble prediction Adaptive observation Weather 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zhina Jiang
  • Hongli Wang
  • Feifan Zhou
  • Mu Mu
    • 1
  1. 1.State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG)Institute of Atmospheric Physics Chinese Academy of SciencesChina

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