Forecast variability

The ability of a NWP model to forecast extremes with the same frequency as they occur in the atmosphere is crucial for any ensemble approach, either lagged, multi-model or EPS. If the model has a tendency to over- or under-forecast certain weather elements, their probabilities will, of course, also be biased.

More generally, the forecast variability over time and space should be equal to at least the analysed, ideally the observed variability. There are different variance measures to monitor this variability:

·         Variability around the climatological average, which measures the model’s ability to span the full climatological range

·         The averaged analysed and forecast spatial variance over a specified area at a specific time e.g. a day; it may be presented as a time series

·         The averaged temporal variability over a specified area, calculated over a sufficiently long time period. The varia­nce can be computed for every grid point or as the change over 12 or 24 hours. It may be presented as geographical distributions (see Figure 61).

For all three methods the level of variability averaged over many forecasts in the medium range should be the same as for the initial analysis or a short-range forecast.

Figure 61: 500 hPa geopotential variability, October 2010 - March 2011. The standard deviation over the period is calculated for every grid point. The analysis (left) shows maximum variability between Greenland and Canada and in the North Pacific. This is well captured by the D+5 forecast (centre) and D+10 forecast (right) but with slightly decreasing values.

If a perfect model has, by definition, no systematic errors, then a stable model might have systematic errors which do not change their characteristics during the forecast range. Most state-of-the-art NWP models are fairly stable in the medium range but start to display some model drift, such as gradual cooling or warming, moistening or drying, in the extended ranges.