Mark Buehner
Data Assimilation and Satellite Meteorology Division
Several important NWP applications require the accurate prediction of
forecast uncertainty or its pre-cursor including: ensemble prediction,
observation targeting, data assimilation and data quality control. Under
certain conditions, the optimal estimate of forecast uncertainty is
provided by singular vectors when the initial-time norm is defined by the
inverse analysis error covariances. However, until now only the use of
analysis error covariance estimates without correlations or lacking
flow-dependence have been evaluated in an operational setting. In this
talk, norms based on analysis error covariances from the ensemble Kalman
filter and a simpler approach applied to an existing forecast-analysis
system are evaluated in a quasi-operational setting. These approaches
attempt to more realistically sample the probability distribution of
analysis error by simulating, via a Monte Carlo approach, the error
generated at each stage of the forecast-analysis process. Results from
using these stationary or flow-dependent norms are compared with more
common norms, including total energy. In the context of ensemble
forecasting, the forecast error distributions derived obtained from both
the singular vectors and the original EnKF analysis ensembles are
compared.