Note: HRES and Ensemble Control (ex-HRES) are scientifically, structurally and computationally identical.  With effect from Cy49r1, Ensemble Control (ex-HRES) output is equivalent to HRES output shown in the diagrams.   At the time of the diagrams, HRES had resolution of 9km and ensemble members had a resolution of 18km.

Medium Range Forecasting - using ensemble forecasts

Ensemble forecasts (ENS) offer the most consistent method of achieving good and consistent forecasts.  Interpretation of the output gives the ability to assess most likely outcomes and the probability of extreme weather and even an assessment of how extreme that weather might be.  It is vital that users adopt ways of working successfully with the ensemble.

Note:

  • ECMWF strategy 2015-2025 is centred on ENS development
  • HRES and Ensemble Control Forecast (ex-HRES) are scientifically, structurally and computationally identical.  The Ensemble Control Forecast (ex-HRES) output runs on the schedule of HRES in Cy48 and earlier.   It runs before the medium range ensemble starts.  It is labelled HRES for the convenience of users but the name will be withdrawn in a future update cycle.

Use of the ensemble control member

Ensemble Control Forecast (ex-HRES) are scientifically, structurally and computationally identical.  It might be thought of as something of a substitute for HRES (Cy48 and earlier) working as a deterministic model yielding deterministic results.  But the remember unperturbed ensemble control is only one member of the ensemble.

There are 50 other (perturbed) members of the ensemble.  All are equally valid.  There is no reason to select the results from the unperturbed control member rather than any of the others.  Also any member viewed in isolation cannot provide any estimate of forecast uncertainty or confidence.   

The high resolution (currently 9km) brings advantages and disadvantages - smaller scale atmospheric features are modelled and forecast, and look beguilingly realistic.  Development of these atmospheric systems often is in response to inherent numerical instability (which affects all numerical models) and reliance on detail is inappropriate.  

The main strategy to adopt is to avoid over-interpreting non-predictable features.  Therefore the detail of the most recent ensemble members should not be used in isolation.  Run-to-run jumpiness can on the one hand be tackled as something negative that has to be dampened, but on the other hand as something positive which can enrich the forecast information by giving alternative scenarios.  Ensemble members can give an indication of the probability and the consistency of features of the forecast.

Use of the ensemble mean (EM)

Generally beyond the short range, the ensemble mean (or median if applicable) has higher accuracy than the ensemble control (CTRL) no matter the ensemble spread.  This is particularly true for mean sea level pressure and temperature.

With larger ensemble spread it is better to use probabilities rather than ensemble mean predicted values for forecasts.  This is particularly true for parameters such as precipitation and cloud amounts. 

The ensemble mean also shows greater day-to-day consistency.  On average, jumpiness is much less than in the ensemble control, particularly with forecasts beyond about Day3. 

Criticism of the ensemble mean (EM)

Averages of forecasts from the same or different NWP models are similarly more accurate.  However, 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 extreme forecast index (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 ensemble mean fields (e.g. ensemble average 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 isobaric gradient).

A synoptic example of combining ensemble mean and probabilities

It is important to avoid over-interpretation of the ensemble mean, in particular underestimation of the risk of extreme weather events.  To aid visual interpretation by the user, ensemble mean output should be presented together with a measure of the ensemble spread.  The ensemble mean and the probabilities relate naturally to each other and can be most effective when shown together.  So, for example, the ensemble mean of the MSLP (or 1000hPa) presented together with gale probabilities will put the latter into a synoptic context that will help interpretation (see Fig6.1-1).

Fig6.1-1: 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 1000hPa geopotential EM overlaid by the probabilities of wind speeds >10m/s.  Probabilities are coloured in 20% intervals starting from 20%.



(FUG Associated with Cy49r1)