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Forecast error growth is, on average, largest at the beginning of the forecast.  At longer forecast ranges it levels off asymptotically towards the error level of persistence forecasts, pure guesses or the difference between two randomly chosen atmospheric states (see Fig4.1.1).  This error level is significantly higher than the average error level for a simple climatological average if used as a forecast.  Forecast verification is discussed in the annexe.

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In order to estimate and compensate for any model drift the model output is compared with the corresponding model climates (M-climate for medium range, ER-M-climate for extended range, S-M-climate for seasonal forecasting) for the current forecast date.  This is derived using the same model construction as the ensemble from a number of perturbed forecasts based on calendar dates surrounding the date of the current ensemble run using historical data from several years.  Systematic errors are then corrected during post-processing after the forecast is run. 

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