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The typical predictability is currently approximately twice the timescale, but might ultimately be three times the timescale.  Small baroclinic systems or fronts are currently well forecast to around Day2, cyclonic systems to around Day4 and the long planetary waves defining weather regimes to around Day8.  As models improve over time these limits are expected to advance further ahead of the data time.  Features that are coupled to the orography (e.g. lee-troughs), or to the underlying surface (e.g. heat lows), are rather less consistently well forecast.  The predictable scales also show the largest consistency from one run to the next.  Fig4.1.3 shows 1000hPa forecasts from six sequential runs of HRESverifying at   (identical to CONTROL-10) verifying at the same time. 

Fig4.1.3: A sequence of Mean Sea Level Pressure forecast charts ranging from T+156h 156 to T+96h96, all verify at 00UTC 24 October 2022.  The forecast details differ between the forecasts but large-scale systems (a low near Ireland, a high over central Europe, a trough towards the southern Baltic) are common features.  The T+156hr 156 predicted gales  over over southern  and and northwest France.  It would have been unwise to make such a detailed interpretation of the forecast, considering the typical skill at that range.  Only a statement of windy, unsettled and cyclonic conditions would have been justified.  Such a cautious interpretation would have avoided any embarrassing forecast “jump”, when the subsequent T+144hr 144 and T+132hr 132 runs showed a weaker circulation.  The same cautious approach would have minimized the forecast “jump” with the arrival of the T+108hr 108 forecast.

 A synoptic example of combining EM and probabilities

However, spectral filtering does not take into account how the predictability varies due its flow dependency; a .  A small-scale feature near Portugal might be less predictable than an equally sized feature over Finland.  To avoid over

Over-interpreting the EM, in particular underestimating ensemble forecast or underestimation of the risk of extreme weather events , it should preferably be should be avoided.  This can be helped if ensemble forecasts are 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 .   As an example, gales are put into a synoptic context if ensemble forecasts of MSLP (or 1000hPa) are presented together with gale probabilities will put the latter into a synoptic context that will facilitate interpretation (Fig4.1.5). 

Fig4.1.4: A sequence of Mean Sea Level Pressure forecast charts overlaid by the probabilities of wind speeds >10 m s^-1 ranging from T+156h 156 to T+96h96, all verify at 00UTC 24 October 2022.  Probabilities are coloured according to the scale. Compare with Fig4.1.3.

The ensemble assesses the forecasts in a consistent and optimal way as its flow dependency serves as a superior dynamic filter.   It gives the probability of an outcome (in this case strength of wind) rather than relying on an individual solution.

 The T+12 h ensemble forecast is used here as an analysis proxy for the verification of the above forecasts (see Fig4.1.6).

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It can be seen from the above that some of the HRES medium-range (identical to CONTROL-10) forecasts in Fig4.1.3 (T+96hr96, T+108hr 108 and perhaps T+144hr144) were quite good with respect to strong winds over Britain and Ireland but at the time the ENS ensemble indicated that gale force winds were not certain. 

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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 ENSmedium range, ER-M-climate for Extended Range ENSextended range, S-M-climate for Seasonal forecasting) for the current forecast date.  This is derived using the same model construction as the ENS ensemble from a number of perturbed forecasts based on calendar dates surrounding the date of the current ENS 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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