ENSEMBLE FORECASTING AT NCEP AND THE BREEDING METHOD

The breeding method has been used to generate perturbations for ensemb le forecasting at the National Centers for Environmental Prediction (f ormerly known as the National Meteorological Center) since December 19 92. At that time a single breeding cycle with a pair of bred forecasts was implemented. In March 1994, the ensemble was expanded to seven in dependent breeding cycles on the Gray C90 supercomputer, and the forec asts were extended to 16 days. This provides 17 independent global for ecasts valid for two weeks every day. For efficient ensemble forecasti ng, the initial perturbations to the control analysis should adequatel y sample the space of possible analysis errors. It is shown that the a nalysis cycle is like a breeding cycle: it acts as a nonlinear perturb ation model upon the evolution of the real atmosphere. The perturbatio n (i.e., the analysis error), carried forward in the first-guess forec asts, is ''scaled down'' at regular intervals by the use of observatio ns. Because of this, growing errors associated with the evolving state of the atmosphere develop within the analysis cycle and dominate subs equent forecast error growth. The breeding method simulates the develo pment of growing errors in the analysis cycle. A difference field betw een two nonlinear forecasts is carried forward (and scaled down at reg ular intervals) upon the evolving atmospheric analysis fields. By cons truction, the bred vectors are superpositions of the leading local (ti me-dependent) Lyapunov vectors (LLVs) of the atmosphere. An important property is that all random perturbations assume the structure of the leading LLVs after a transient period, which for large-scale atmospher ic processes is about 3 days. When several independent breeding cycles are performed, the phases and amplitudes of individual (and regional) leading LLVs are random, which ensures quasi-orthogonality among the global bred vectors from independent breeding cycles. Experimental run s with a 10-member ensemble (five independent breeding cycles) show th at the ensemble mean is superior to an optimally smoothed control and to randomly generated ensemble forecasts, and compares favorably with the medium-range double horizontal resolution control. Moreover, a pot entially useful relationship between ensemble spread and forecast erro r is also found both in the spatial and time domain. The improvement i n skill of 0.04-0.11 in pattern anomaly correlation for forecasts at a nd beyond 7 days, together with the potential for estimation of the sk ill, indicate that this system is a useful operational forecast tool. The two methods used so far to produce operational ensemble forecasts- that is, breeding and the adjoint (or ''optimal perturbations'') techn ique applied at the European Centre for Medium-Range Weather Forecasts -have several significant differences, but they both attempt to estima te the subspace of fast growing perturbations. The bred vectors provid e estimates of fastest sustainable growth and thus represent probable growing analysis errors. The optimal perturbations, on the other hand, estimate vectors with fastest transient growth in the future. A pract ical difference between the two methods for ensemble forecasting is th at breeding is simpler and less expensive than the adjoint technique.