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Forecast Error Growth

Relationship between Forecast Range and Forecast Error

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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Fig4.1.1:  A schematic illustration of the forecast error development of a state-of-the-art NWP (full curve), persistence and guesses (dotted curve), whose errors converge to a higher error saturation level than modified forecasts, which converge at a lower RMSE level (dashed curve). 

Relationship between Scale and Predictive Skill

It is known from theory and synoptic experience that the larger the scale of an atmospheric system, the longer is its timescale and the more predictable it normally is (see Fig4.1.2).

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It can be seen from the above that some of the HRES (identical to CTRL) forecasts in Fig4.1.3 (T+96, T+108 and perhaps T+144) were quite good with respect to strong winds over Britain and Ireland but at the time the ensemble indicated that gale force winds were not certain. 

Model drift

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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