Medium Range Forecasting - ENS alone
Although the ENS offers the most consistent method of achieving good and consistent forecasts it is usefully augmented by HRES to give information on possible finer scale detail. Some information is lost if the HRES output is not used and using the ENS alone is not recommended although rarely this may be necessary. Nevertheless, the information in this section regarding use and interpretaion of ENS output can and should be usefully employed whether HRES is available or not. Note also that ECMWF strategy 2015-2025 centres on ENS development, rather than HRES, so it is vital that users adopt ways of working successfully with the ensemble.
Use of the ensemble mean (EM)
Generally, whether the ensemble spread is small or large, the EM (or median if applicable) will, beyond the short range, exhibit higher accuracy than the CTRL or HRES forecast. This is particularly true for parameters, such as MSLP and temperature. However, with increasing spread, the forecast information will depend more heavily on the probabilities; this is particularly true for parameters, such as precipitation and cloud amounts. The EM also displays a higher degree of day-to-day consistency. Jumpiness in the EM is also markedly less, on average, than seen in HRES or CTRL, particularly when examining forecasts beyond about Day3.
Criticism of the ensemble mean
EM forecasts and, similarly averages of forecasts from the same or different NWP models, provide more accurate and considerably less “jumpy” solutions, but meteorologists are somewhat apprehensive about using them. This reluctance derives mainly from three reasons:
- Ensemble averages do not constitute genuine, dynamically consistent, three-dimensional representations of the atmosphere.
- Ensemble averages are less able to represent extreme or anomalous weather events. Event probabilities or the EFI should be used instead.
- Ensemble averages might lead to inconsistencies between different parameters. For example, the ensemble cloud average (or median) might not be consistent with the average (or median) of the precipitation.
- On average, gradients in EM fields (e.g. mean sea level pressure) systematically reduce with lead time, which can give misleading guidance on other parameters (e.g. wind strength, which is commonly inferred from the gradient).
A synoptic example of combining ensemble mean and probabilities
To avoid over-interpreting the EM, in particular underestimating the risk of extreme weather events, it should preferably be presented together with a measure of the ensemble spread or event probabilities; these will convey an impression complementary to the EM. Since the EM and the probabilities relate naturally to each other, they should be presented together. So, for example, the EM of the MSLP (or 1000hPa) presented together with gale probabilities will put the latter into a synoptic context that will facilitate interpretation (see Fig6.1.1).
Fig6.1.1 (same as Fig4.1.5): 1000hPa forecast from 12UTC 13 August 2010 T+156hr to 00UTC 16 August 00 UTC T+96 h, all valid at 00UTC 20 August 2010. Full lines are the 1000 hPa geopotential EM overlaid by the probabilities of wind speeds >10m/s. Probabilities are coloured in 20% intervals starting from 20%. Compare with Fig4.1.3 and Fig4.1.4.