Open AccessPredictability of the Performance of an Ensemble Forecast System: Predictability of the Space of Uncertainties
Author(s) -
Elizabeth Satterfield,
Istvan Szunyogh
Publication year - 2010
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/2009mwr3049.1
Subject(s) - predictability , data assimilation , ensemble forecasting , covariance , computer science , kalman filter , ensemble kalman filter , range (aeronautics) , dimension (graph theory) , meteorology , global forecast system , nonlinear system , forecast skill , numerical weather prediction , mathematics , statistics , extended kalman filter , artificial intelligence , physics , aerospace engineering , engineering , quantum mechanics , pure mathematics
The performance of an ensemble prediction system is inherently flow dependent. This paper investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model gridpoint location ℓ. A local error covariance matrix ℓ is defined for each local region, and the diagnostics are applied to the linear space defined by the range of the ensemble-based estimate of ℓ. The particular diagnostics are chosen to help investigate the efficiency of in capturing the space of analysis and forecast uncertainties. Numerical experiments are carried out with an implementation of the local ensemble transform Kalman filter (LETKF) data assimilation system on a reduced-resolution [T62 and 28 vertical levels (T62L28)] version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere in a realistic setting are used in these experiments. It is found that (i) paradoxically, the linear space provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time; (ii) provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth (i.e., for cases of lower predictability); and (iii) the ensemble dimension (E dimension) is a reliable predictor of the performance of in predicting the space of forecast uncertainties.