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, con-

The breeding method has been used to generate perturbations for ensemble forecasting at the National Centers for Environmental Prediction (formerly known as the National Meteorological Center) since December 1992. At that time a single breeding cycle with a pair of bred forecasts was implemented. In March 1994, the ensemble was expanded to seven independent breeding cycles on the Cray C90 supercomputer, and the forecasts were extended to 16 days. This provides 17 independent global forecasts valid for two weeks every day. For efficient ensemble forecasting, the initial perturbations to the control analysis should adequately sample the space of possible analysis errors. It is shown that the analysis cycle is like a breeding cycle: it acts as a nonlinear perturbation model upon the evolution of the real atmosphere. The perturbation (i.e., the analysis error), carried forward in the first-guess forecasts, is ''scaled down'' at regular intervals by the use of observations. Because of this, growing errors associated with the evolving state of the atmosphere develop within the analysis cycle and dominate subsequent forecast error growth. The breeding method simulates the development of growing errors in the analysis cycle. A difference field between two nonlinear forecasts is carried forward (and scaled down at regular intervals) upon the evolving atmospheric analysis fields. By construction, the bred vectors are superpositions of the leading local (timedependent) 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 atmospheric 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 runs with a 10-member ensemble (five independent breeding cycles) show that 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 potentially useful relationship between ensemble spread and forecast error is also found both in the spatial and time domain. The improvement in skill of 0.04-0.11 in pattern anomaly correlation for forecasts at and beyond 7 days, together with the potential for estimation of the skill, 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'') technique applied at the European Centre for Medium-Range Weather Forecasts-have several significant differences, but they both attempt to estimate the subspace of fast growing perturbations. The bred vectors provide estimates of fastest sustainable growth and thus represent probable growing analysis errors. The optimal perturbations, on the ...

The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following:the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts;a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; andfor all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources of forecast uncertainty.The relative strengths and weaknesses of the three systems identified in this study can offer guidelines for the future development of ensemble forecasting techniques.

Real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January-10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution (triangular truncation at wavenumber 62 with 28 levels) than the GDAS real-time NCEP operational runs (triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reduced-resolution version of the NCEP three-dimensional variational data assimilation system (3DVAR), with the biggest improvement in data-sparse regions. Ensemble data assimilation analyses yielded a 24-h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system (the 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system). Improvements in the data-rich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system (including model error) were tested. Adding scaled random differences between adjacent 6-hourly analyses from the NCEP-NCAR reanalysis to each ensemble member (additive inflation) performed slightly better than the other two methods (multiplicative inflation and relaxation-to-prior).

Leading NWP centers have agreed to create a database of their operational ensemble forecasts and open access to researchers to accelerate the development of probabilistic forecasting of high-impact weather.Objectives and cOncept. During the past decade, ensemble forecasting has undergone rapid development in all parts of the world. Ensembles are now generally accepted as a reliable approach to forecast confidence estimation, especially in the case of high-impact weather. Their application to quantitative probabilistic forecasting is also increasing rapidly. In addition, there has been a strong interest in the development of multimodel ensembles, whether based on a set of single (deterministic) forecasts from different systems, or on a set of ensemble forecasts from different systems (the so-called superensemble). The hope is that multimodel ensembles will provide an affordable approach to the classical goal of increasing the hit rate for prediction of high-impact weather without increasing the false-alarm rate. This is being taken further within The Observing System Research and Predictability Experiment (THORPEX), a major component of the World Weather Research Programme (WWRP) under the World Meteorological Organization (WMO). A key goal of THORPEX is to accelerate improvements in

Quantifying forecast uncertainty with an ensemble approach can improve the users' bottom line D uring the past decade, due to increased computer resources, the development of more realistic atmospheric models, and the recognition of the importance of atmospheric predictability in general, ensemble forecasting became a major component of Numerical Weather Prediction (NWP). NWP centers around the globe [European Centre for Medium-Range Weather Forecasts (ECMWF),

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