Ensemble forecasting
Ensemble forecasting is a method used in or within numerical weather prediction. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere. Ensemble forecasting is a form of Monte Carlo analysis. 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 atmosphere, which is often referred to as sensitive dependence on 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 atmospheric state should fall within the predicted ensemble spread, and the amount of spread should be related to the uncertainty (error) of the forecast. In general, this approach can be used to make probabilistic forecasts of any dynamical system, and not just for weather prediction. 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) Ministry of Earth Sciences (IMD, IITM & NCMRWF) (India) 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.