Abstract:
A method called Perturbation vs. Error Correlation Analysis (PECA),
which evaluates the ensemble perturbations instead of the forecasts
themselves by measuring their ability to explain forecast error
variance,
is used to evaluate ensemble forecasts from NCEP and ECMWF. Ensemble
perturbations from NCEP and ECMWF were found to perform similarly.
The error variance explained by either ensemble increases with the
number
of members and the lead time. The dynamically conditioned NCEP and ECMWF
perturbations outperform both randomly chosen perturbations and
differences between lagged forecasts ("NMC" method). Thus ensemble
forecasts potentially could be used to construct flow dependent
short-range
forecast error covariance matrices for use in data assimilation schemes.