To test how these small differences in the initial conditions may affect the outcome of the forecast, an ensemble system can be used to produce many forecasts.
How we do ensemble forecastsInstead of running just a single forecast, the computer model is run a number of times from slightly different starting conditions. The complete set of forecasts is referred to as the ensemble, and individual forecasts within it as ensemble members. We design the ensemble forecast system so that each member should be equally likely. The initial differences between the ensemble members are small, and consistent with uncertainties in the observations. But when we look several days ahead the forecasts can be quite different.
Fig. 1: Schematic of how the ensemble samples the uncertainty in the forecast
Fig. 1 illustrates how an ensemble samples the uncertainty of the forecast, assuming that the forecast model is perfect. If the starting conditions were known accurately, and the model was perfect, then an accurate forecast could, in theory, be produced (shown in red). However, because it is not possible to know the exact starting conditions we use our best guess and generate a forecast which can sometimes be inaccurate (shown in blue). By sampling the uncertainty in the starting conditions, and running several ensemble members forward with the model (shown in black), we produce an estimate of the forecast uncertainty and an indication of which weather events may occur.
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