Data Assimilation in the NCEP GFS
Ensemble data assimilation (EDA) has shown great promise in perfect
model studies. The flow-dependent background error covariances which
EDA provides have been shown to be quite benefical, especially when
observations are sparse and/or infrequent. When applied to real
forecast systems, with imperfect models, results to date have been
mixed. The need to parameterize model error not accounted for in the
ensemble itself is thought to be the primary factor limiting the
performance of first-generation EDA systems. We have implemented a
prototype EDA system for the NCEP Global Forecast System (GFS).
Analyses obtained with a 100 member T62L28 ensemble, using real
observations for January and February 2004, will be shown and compared
with analyses obtained with the NCEP operational analysis system.
Sensitivity to the scheme used for parameterizing model error will be
Jeff Whitaker, Tom
Hamill and Xue Wei