An ensemble runs multiple forecasts with perturbed initial states, model parameters or physical formulations to represent uncertainty. Its distribution can indicate probabilities and sensitivity, but finite sampling or shared model errors can make the ensemble underdispersive. Growing initial perturbations can be selected using right singular vectors of a finite-time propagator.
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Ensemble forecasting is a technique used in meteorology and other fields, such as finance and climate modeling, that leverages multiple simulations or models to improve the accuracy and reliability of predictions. The main idea behind ensemble forecasting is to account for uncertainty in the initial conditions and model formulations by creating a range of forecasts rather than a single deterministic forecast.