Predictability of the Magnitude and the Spectrum of the Uncertainties in an Ensemble Forecast System

In this paper, the ability of the ensemble to capture the magnitude and spectrum of uncertainty is assessed in a reduced resolution 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 conventional observations. Finally, observations of the real atmosphere are assimilated. The goal of this study is to investigate the accuracy of the prediction of the expected value of the magnitude of the uncertainty and the accuracy of the predicted spectrum of uncertainties within the linear space spanned by ensemble perturbations. Additionally, we aim to better understand the roots of the underestimation of the magnitude of uncertainty by the ensemble at longer forecast lead times.