3D‐Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system

Singular vectors are computed which are consistent with 3D-Var (three-dimensional variational) estimates of analysis error statistics. This is achieved by defining the norm at initial time in terms of the full Hessian of the 3D-Var cost function. At final time the total energy norm is used. the properties of these Hessian singular vectors (HSVs) differ considerably from total energy singular vectors (TESVs) in such aspects as energy spectrum and growth rate. Despite these differences, the leading 25 TESVs and HSVs explain nearly the same part of the 2-day forecast error. Two experimental ensemble configurations are studied. One configuration uses perturbations based on HSVs in the computation of initial perturbation, the other uses TESVs and 2-day linearly evolved singular vectors (ESVs) of two days before. the latter approach provides a way to include more stable and large-scale structures in the perturbations. Ten pairs of ensembles are compared to the operational European Centre for Medium-Range Weather Forecasts Ensemble Prediction System. the ensembles using ESVs perform slightly better. the ensembles based on HSVs show a slightly worse performance and are lacking some spread in the medium range. Possible directions to improve the computation of HSVs are discussed.

[1]  P. Courtier,et al.  The ECMWF implementation of three‐dimensional variational assimilation (3D‐Var). I: Formulation , 1998 .

[2]  Gerard L. G. Sleijpen,et al.  A Jacobi-Davidson Iteration Method for Linear Eigenvalue Problems , 1996, SIAM J. Matrix Anal. Appl..

[3]  R. Buizza Impact of horizontal diffusion on T21, T42, and T63 singular vectors , 1998 .

[4]  G. Brier VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[5]  P. Courtier,et al.  The ECMWF implementation of three‐dimensional variational assimilation (3D‐Var). II: Structure functions , 1998 .

[6]  E. Epstein,et al.  Stochastic dynamic prediction1 , 1969 .

[7]  Roberto Buizza,et al.  The impact of increased resolution on predictability studies with singular vectors , 1997 .

[8]  M. Ehrendorfer The Liouville Equation and Its Potential Usefulness for the Prediction of Forecast Skill. Part I: Theory , 1994 .

[9]  Philippe Courtier,et al.  Dynamical structure functions in a four‐dimensional variational assimilation: A case study , 1996 .

[10]  E. Epstein,et al.  Stochastic dynamic prediction , 1969 .

[11]  T. Palmer,et al.  Singular Vectors, Metrics, and Adaptive Observations. , 1998 .

[12]  E. Davidson The iterative calculation of a few of the lowest eigenvalues and corresponding eigenvectors of large real-symmetric matrices , 1975 .

[13]  Philippe Courtier,et al.  Four‐Dimensional Assimilation In the Presence of Baroclinic Instability , 1992 .

[14]  E. Kalnay,et al.  Ensemble Forecasting at NCEP and the Breeding Method , 1997 .

[15]  Martin Ehrendorfer,et al.  Optimal Prediction of Forecast Error Covariances through Singular Vectors , 1997 .

[16]  J. Barkmeijer,et al.  Singular vectors and estimates of the analysis‐error covariance metric , 1998 .

[17]  Jan Barkmeijer,et al.  Perturbations that optimally trigger weather regimes , 1995 .

[18]  Roberto Buizza,et al.  The Singular-Vector Structure of the Atmospheric Global Circulation , 1995 .

[19]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[20]  Mats Hamrud,et al.  Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System , 1998 .

[21]  P. L. Houtekamer,et al.  A System Simulation Approach to Ensemble Prediction , 1996 .

[22]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .