Source of information: http://en.wikipedia.org/wiki/Ensemble_forecasting
Ensemble forecasting is a numerical 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 sources of uncertainty in weather forecast models: (1) the errors introduced by chaos or sensitive dependence on the initial conditions; and (2) errors introduced because of imperfections in the model, such as the finite grid spacings. Ideally, the verified weather pattern should fall within past ensemble spreads, and the amount of spread should be related to the probability of certain weather events occurring.
Considering the problem of numerical weather prediction, ensemble predictions are now commonly made at most of the major operational weather prediction facilities worldwide, including the National Centers for Environmental Prediction (US), the European Centre for Medium-Range Weather Forecasts (ECMWF), the United Kingdom Met Office, Meteo France, Environment Canada, the Japanese Meteorological Agency, the Bureau of Meteorology (Australia), the China Meteorological Administration, the Korea Meteorological Administration, and 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.
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.
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.[4] 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, 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.
When many different forecast models are used to try to generate a forecast, the approach is termed multi-model ensemble forecasting. This method of forecasting has been shown to improve forecasts when compared to a single model-based approach. When the models within a multi-model ensemble are adjusted for their various biases, this process is known as super ensemble forecasting. This type of a forecast significantly reduces errors in model output.
Stochastic or "ensemble" forecasting is used to account for uncertainty. It involves multiple forecasts created with an individual forecast model by using different physical parametrizations or varying initial conditions. The ensemble forecast is usually evaluated in terms of an average of the individual forecasts concerning one forecast variable, as well as the degree of agreement between various forecasts within the ensemble system, as represented by their overall spread. Ensemble spread is diagnosed through tools such as spaghetti diagrams, which show the dispersion of one quantity on prognostic charts for specific time steps in the future. Another tool where ensemble spread is used is a meteogram, which shows the dispersion in the forecast of one quantity for one specific location. It is common for the ensemble spread to be too small to incorporate the solution which verifies, which can lead to a misdiagnosis of model uncertainty; this problem becomes particularly severe for forecasts of the weather about 10 days in advance.
When ensemble spread is small and the forecast solutions are consistent within multiple model runs, forecasters perceive more confidence in the ensemble mean, and the forecast in general. A spread-skill relationship sometimes exists, as spread-error correlations are normally less than 0.6. The relationship between ensemble spread and skill varies substantially depending on such factors as the forecast model and the region for which the forecast is made.
Ideally, the relative frequency of events from the ensemble could be used directly to estimate the probability of a given weather event. For example, if 30 of 50 members indicated greater than 1 cm rainfall during the next 24 h, the probability of exceeding 1 cm could be estimated to be 60 percent. The forecast would be considered reliable if, considering all the situations in the past when a 60 percent probability was forecast, on 60 percent of those occasions did the rainfall actually exceed 1 cm. This is known as reliability or calibration. In practice, the probabilities generated from operational weather ensemble forecasts are not highly reliable, though with a set of past forecasts (reforecasts or hindcasts) and observations, the probability estimates from the ensemble can be adjusted to ensure greater reliability. Another desirable property of ensemble forecasts is sharpness. Provided that the ensemble is reliable, the more an ensemble forecast deviates from the climatological event frequency and issues 0 percent or 100 percent forecasts of an event, the more useful the forecast will be. However, sharp forecasts that are unaccompanied by high reliability will generally not be useful. Forecasts at long leads will inevitably not be particularly sharp, for the inevitable (albeit usually small) errors in the initial condition will grow with increasing forecast lead until the expected difference between two model states is as large as the difference between two random states from the forecast model's climatology.
The Observing System Research and Predictability Experiment (THORPEX) is a 10-year international research and development programme to accelerate improvements in the accuracy of one-day to two-week high impact weather forecasts for the benefit of society, the economy and the environment.
THORPEX establishes an organizational framework that addresses weather research and forecast problems whose solutions will be accelerated through international collaboration among academic institutions, operational forecast centres and users of forecast products.
TIGGE, the THORPEX Interactive Grand Global Ensemble, is a key component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2 week high-impact weather forecasts for the benefit of humanity. Centralized archives of ensemble model forecast data, from many international centers, are used to enable extensive data sharing and research. The designated TIGGE archive centers include the Chinese Meteorological Administration (CMA), The European Center for Medium-Range Weather Forecasts (ECMWF), and The National Center for Atmospheric Research (NCAR). Scientific data requirements and archive planning solidified in late 2005, and archive collection began in October 2006.
The Unidata LDM software package is used to transport the ensemble model data from the providers to the archive centers. Currently, the output from the ECMWF, UK Met Office (UKMO), CMA, Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP-USA), Meteorological Service of Canada (CMC), Bureau of Meteorology Australia (BOM), Centro de Previsao Tempo e Estudos Climaticos Brazil (CPTEC), Korea Meteorological Administration (KMA), and MeteoFrance (MF) global models, totaling 440 GB/day, is moved at up to 30 GB/hour to NCAR (Realtime Statistics). By requirement the parameter fields, atmospheric levels, and physical units are consistent across all data from the providers and encoded in WMO GRIB-2 format. In contrast, each provider may submit their model output in a resolution they choose.
TIGGE data are available to the public for non-commercial research, with a 48-hour delay after forecast initialization time. At NCAR, users can discover data through the TIGGE portal and select parameters, grid resolution, and spatial subsets for the most current two-week period. The most current two-week period of TIGGE data are also available for direct download in the form of forecast files through the RDA near realtime 2-week TIGGE archive. Long term TIGGE data archives are available through the RDA full TIGGE archive. Forecast files are organized by level type (single level, pressure level, potential vorticity level, and potential temperature level), and forecast time-step for a specified model. All ensemble members are included in each forecast file. At ECMWF, users can discover and download data through a web interface linked to the Meteorological Archival and Retrieval System (MARS). CMA offers an additional option for CMA TIGGE data access. Each center will offer fast access to terabytes of data kept online and delayed access to the long term archives preserved in their archive systems.
The key objectives of TIGGE