Convergence of Singular Vectors toward Lyapunov Vectors

The rate at which the leading singular vectors converge toward a single pattern for increasing optimization times is examined within the context of a T21 L3 quasigeostrophic model. As expected, the final-time backward singular vectors converge toward the backward Lyapunov vector, while the initial-time forward singular vectors converge toward the forward Lyapunov vector. Although there is significant case-to-case variability, in general this convergence does not occur over timescales for which the tangent approximation is valid (i.e., less than 5 days). However, a significant portion of the leading Lyapunov vector is contained within the subspace spanned by an ensemble composed of the first 30 singular vectors optimized over 2 or 3 days. Also as expected, the final-time leading singular vectors become independent of metric as optimization time is increased. Given an initial perturbation that has a white spectrum with respect to the initial-time singular vectors, the percent of the final-time perturbation explained by the leading singular vector is significant and increases as optimization time increases. However, even for 10-day optimization times, the leading singular vector accounts for, on average, only 23% to 28% of the total evolved global perturbation variance depending on the metric and trajectory.

[1]  Eugenia Kalnay,et al.  Ensemble Forecasting at NMC: The Generation of Perturbations , 1993 .

[2]  Istvan Szunyogh,et al.  A comparison of Lyapunov and optimal vectors in a low-resolution GCM , 1997 .

[3]  C. Nicolis,et al.  Lyapunov Vectors and Error Growth Patterns in a T21L3 Quasigeostrophic Model , 1997 .

[4]  Roberto Buizza,et al.  Sensitivity Analysis of Forecast Errors and the Construction of Optimal Perturbations Using Singular Vectors , 1998 .

[5]  Franco Molteni,et al.  Ensemble prediction using dynamically conditioned perturbations , 1993 .

[6]  Ronald M. Errico,et al.  Mesoscale Predictability and the Spectrum of Optimal Perturbations , 1995 .

[7]  Tim N. Palmer,et al.  Decaying Singular Vectors and Their Impact on Analysis and Forecast Correction , 1998 .

[8]  R. Buizza Localization of optimal perturbations using a projection operator , 1994 .

[9]  E. Lorenz A study of the predictability of a 28-variable atmospheric model , 1965 .

[10]  P. L. Houtekamer,et al.  Prediction Experiments with Two-Member Ensembles , 1994 .

[11]  Local error growth in a barotropic model , 1992 .

[12]  Brian F. Farrell,et al.  Small Error Dynamics and the Predictability of Atmospheric Flows. , 1990 .

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

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

[15]  Franco Molteni,et al.  Toward a dynamical understanding of planetary-scale flow regimes. , 1993 .

[16]  Franco Molteni,et al.  Predictability and finite‐time instability of the northern winter circulation , 1993 .