Assessing the Performance of an Ensemble Forecast System in Predicting the Magnitude and the Spectrum of Analysis and Forecast Uncertainties

The ability of an ensemble to capture the magnitude and spectrum of uncertainty in a local linear space spanned by the ensemble perturbations is assessed. Numerical experiments are carried out with a reduced resolution 2004 version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The Local Ensemble Transform Kalman Filter (LETKF) data assimilation system is used to assimilate observations in three steps, gradually adding more realistic features to the observing network. In the first experiment, randomly placed, noisy, simulated vertical soundings, which provide 10% coverage of horizontal model grid points, are assimilated. Next, the impact of an inhomogeneous observing system is introduced by assimilating simulated observations in the locations of real observations of the atmosphere. Finally, observations of the real atmosphere are assimilated.

The most important findings of this study are the following: predicting the magnitude of the forecast uncertainty and the relative importance of the different patterns of uncertainty is, in general, a more difficult task than predicting the patterns of uncertainty; the ensemble, which is tuned to provide near optimal performance at analysis time underestimates not only the total magnitude of the uncertainty, but also the magnitude of the uncertainty that projects onto the space spanned by the ensemble perturbations; finally, a strong predictive linear relationship is found between the local ensemble spread and the upper bound of the local forecast uncertainty.