This talk presented recent results from NGGPS-funded work at the Physical Sciences Laboratory focused on improving the use of ensembles in the UFS for data assimilation and prediction. In order to provide an accurate estimate of the background-error covariance used in data assimilation, and to provide reliable probabilistic predictions, the ensemble prediction system must account for both errors in the initial state and the prediction system itself. PSL has been working on improvements to the stochastic parameterization suite for UFS weather application to better capture uncertainty in the land states, and to improve the consistency of the representation of uncertainty in atmospheric physics tendencies and fluxes across model component interfaces (land/atmosphere and ocean/atmosphere). PSL has also been working to improve the Ensemble Kalman Filter (EnKF) data assimilation system used to initialize the NOAA GFS – including updating the system to work with the a much higher model top (80km instead of 50km), improving assimilation of satellite radiances, and reducing the differences between solutions provided by the variational and EnKF algorithms. Recent progress in both of these areas was be presented.
