On 7 December 1992, The National Meteorological Center (NMC) started operational ensemble forecasting. The ensemble forecast configuration implemented provides 14 independent forecasts every day verifying on days 1–10. In this paper we briefly review existing methods for creating perturbations for ensemble forecasting. We point out that a regular analysis cycle is a “breeding ground” for fast-growing modes. Based on this observation, we devise a simple and inexpensive method to generate growing modes of the atmosphere.

The new method, “breeding of growing modes,” or BGM, consists of one additional, perturbed short-range forecast, introduced on top of the regular analysis in an analysis cycle. The difference between the control and perturbed six-hour (first guess) forecast is scaled back to the size of the initial perturbation and then reintroduced onto the new atmospheric analysis. Thus, the perturbation evolves along with the time-dependent analysis fields, ensuring that after a few days of cycling the perturbation field consists of a superposition of fast-growing modes corresponding to the contemporaneous atmosphere, akin to local Lyapunov vectors.

The breeding cycle has been designed to model how the growing errors are “bred” and maintained in a conventional analysis cycle through the successive use of short-range forecasts. The bred modes should thus offer a good estimate of possible growing error fields in the analysis. Results from extensive experiments indicate that ensembles of just two BGM forecasts achieve better results than much larger random Monte Carlo or lagged average forecast (LAF) ensembles. Therefore, the operational ensemble configuration at NMC is based on the BGM method to generate efficient initial perturbations.

The only two methods explicitly designed to generate perturbations that contain fast-growing modes corresponding to the evolving atmosphere are the BGM and the method of Lorenz, which is based on the singular modes of the linear tangent model. This method has been adopted operationally at The European Centre for Medium-Range Forecasts (ECMWF) for ensemble forecasting. Both the BGM and the ECMWF methods seem promising, but since it has not yet been possible to compare in detail their operational performance we limit ourselves to pointing out some of their similarities and differences.

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Footnotes

*General Sciences Corporation, Laurel, Maryland

+National Meteorological Center, Washington, D.C.