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Ensemble forecasting is a numerical weather prediction method that is used to attempt to generate a representative sample of the possible future states of a dynamical system. Ensemble forecasting is a form of Monte Carlo analysis: multiple numerical predictions are conducted using slightly different initial conditions that are all plausible given the past and current set of observations, or measurements. Sometimes the ensemble of forecasts may use different forecast models for different members, or different formulations of a forecast model. The multiple simulations are conducted to account for the two usual sources of uncertainty in forecast models: (1) the errors introduced by the use of imperfect initial conditions, amplified by the chaotic nature of the evolution equations of the dynamical system, which is often referred to as ''sensitive dependence on the initial conditions;'' and (2) errors introduced because of imperfections in the model formulation, such as the approximate mathematical methods to solve the equations. Ideally, the verified future dynamical system state should fall within the predicted ensemble spread, and the amount of spread should be related to the uncertainty (error) of the forecast. Consider the problem of numerical weather prediction. In this case, the dynamic system is the atmosphere, the model is a numerical weather prediction model and the initial condition is represented by an objective analysis of an atmospheric state. Today ensemble predictions are commonly made at most of the major operational weather prediction facilities worldwide, including: * National Centers for Environmental Prediction (NCEP of the US) * European Centre for Medium-Range Weather Forecasts (ECMWF) * United Kingdom Met Office * Météo-France * Environment Canada * Japan Meteorological Agency * Bureau of Meteorology (Australia) * China Meteorological Administration (CMA) * Korea Meteorological Administration * CPTEC (Brazil) Experimental ensemble forecasts are made at a number of universities, such as the University of Washington, and ensemble forecasts in the US are also generated by the US Navy and Air Force. There are various ways of viewing the data such as spaghetti plots, ''ensemble means'' or ''Postage Stamps'' where a number of different results from the models run can be compared. ==History== As proposed by Edward Lorenz in 1963, it is impossible for long-range forecasts—those made more than two weeks in advance—to predict the state of the atmosphere with any degree of skill, owing to the chaotic nature of the fluid dynamics equations involved. Furthermore, existing observation networks have limited spatial and temporal resolution (for example, over large bodies of water such as the Pacific Ocean), which introduces uncertainty into the true initial state of the atmosphere. While a set of equations, known as the Liouville equations, exists to determine the initial uncertainty in the model initialization, the equations are too complex to run in real-time, even with the use of supercomputers.〔 These uncertainties limit forecast model accuracy to about six days into the future.〔Weickmann, Klaus, Jeff Whitaker, Andres Roubicek and Catherine Smith (2001-12-01). (The Use of Ensemble Forecasts to Produce Improved Medium Range (3–15 days) Weather Forecasts. ) Climate Diagnostics Center. Retrieved 2007-02-16.〕 Edward Epstein recognized in 1969 that the atmosphere could not be completely described with a single forecast run due to inherent uncertainty, and proposed a stochastic dynamic model that produced means and variances for the state of the atmosphere. Although these Monte Carlo simulations showed skill, in 1974 Cecil Leith revealed that they produced adequate forecasts only when the ensemble probability distribution was a representative sample of the probability distribution in the atmosphere. It was not until 1992 that ensemble forecasts began being prepared by the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction. The ECMWF model, the Ensemble Prediction System,〔(【引用サイトリンク】 title=The Ensemble Prediction System (EPS) )〕 uses singular vectors to simulate the initial probability density, while the NCEP ensemble, the Global Ensemble Forecasting System, uses a technique known as vector breeding. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Ensemble forecasting」の詳細全文を読む スポンサード リンク
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