Evaluation of the Ensemble Transform Analysis Perturbation Scheme at NRL

Abstract The ensemble transform (ET) scheme changes forecast perturbations into analysis perturbations whose amplitudes and directions are consistent with a user-provided estimate of analysis error covariance. A practical demonstration of the ET scheme was undertaken using Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) analysis error variance estimates and the Navy Operational Global Atmospheric Prediction System (NOGAPS) numerical weather prediction (NWP) model. It was found that the ET scheme produced forecast ensembles that were comparable to or better in a variety of measures than those produced by the Fleet Numerical and Oceanography Center (FNMOC) bred-growing modes (BGM) scheme. Also, the demonstration showed that the introduction of stochastic perturbations into the ET forecast ensembles led to a substantial improvement in the agreement between the ET and NAVDAS analysis error variances. This finding is strong evidence that even a small-sized ET ensemble ...

[1]  Kerry Emanuel,et al.  Development and Evaluation of a Convection Scheme for Use in Climate Models , 1999 .

[2]  P. L. Houtekamer,et al.  Methods for Ensemble Prediction , 1995 .

[3]  T. Hogan,et al.  The Description of the Navy Operational Global Atmospheric Prediction System's Spectral Forecast Model , 1991 .

[4]  Ronald Gelaro,et al.  Singular Vector Calculations with an Analysis Error Variance Metric , 2002 .

[5]  Timothy F. Hogan,et al.  Boundary layer clouds in a Global Atmospheric model: Simple cloud cover parameterizations , 2002 .

[6]  Xuguang Wang,et al.  A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes , 2003 .

[7]  Michael Ghil,et al.  Statistical significance test for transition matrices of atmospheric Markov chains , 1990 .

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

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

[10]  Craig H. Bishop,et al.  Ensemble Transformation and Adaptive Observations , 1999 .

[11]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

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

[13]  S. Julier,et al.  Which Is Better, an Ensemble of Positive–Negative Pairs or a Centered Spherical Simplex Ensemble? , 2004 .

[14]  Roger Davies,et al.  A fast radiation parameterization for atmospheric circulation models , 1987 .

[15]  R. Hodur.  The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) , 1997 .

[16]  Yuejian Zhu,et al.  Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system , 2008 .

[17]  R. Gelaro,et al.  A comparison of variance and total‐energy singular‐vectors , 2005 .

[18]  C. Leith.  Theoretical Skill of Monte Carlo Forecasts , 1974 .

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

[20]  C. Bishop,et al.  The ensemble‐transform scheme adapted for the generation of stochastic forecast perturbations , 2007 .

[21]  Jeffrey L. Anderson,et al.  The Impact of Dynamical Constraints on the Selection of Initial Conditions for Ensemble Predictions: Low-Order Perfect Model Results , 1997 .