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The system uses historical re-forecast runs on dates in past years relating to the date (i.e. month and day) of the current ensemble run.   Re-forecasts are based on an ensemble of forecast members ideally using the same model techniques and physics as the current model.   The re-forecast ensemble uses the appropriate reanalysis field for initialisation.   Perturbations are applied to all but the control.  This is similar to the operational ensemble, but does not involve any data assimilation.  The perturbations derive from singular vectors (SVs) plus geographical averages of ensemble of data assimilations (EDA).  The EDAs are perturbations that have been computed operationally over the most recent 12 months.  This approach means that the flow-dependence inherent in operational EDA perturbations is missing in the re-forecasts.  Stochastic physics are also used during the re-forecast runs, as in operational ensemble runs.  

The set of re-forecast ensembles is based on previous dates which can stretch back several decades.   They differ in number and detail according to the IFS model configuration and are the basis for deriving the corresponding model climates.  These are described in the relevant section for medium range  range M-climate, extended sub-seasonal range ER-M-climate , and seasonal S-M-climate .

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Fig5.3.4-2: Cumulative Distribution Function(CDF) for 2m temperature for Days10-15 in the middle of Lake Superior (red), with M-climate (black).  The initialisation techniques are different for real-time forecasts (using lake surface temperature observed by satellites), and for the re-forecasts (for which this information is not available).  This can lead to the model climate developing anomalously warm or cold lake surfaces and corresponding 2 m CDF temperature curve (black).   This affects subsequent extreme forecast index (EFI) and shift of tails (SOT) fields.   Here the realistic real-time forecast of 2m temperature CDF (red) over Lake Superior is thus incorrectly flagged as having a strongly negative EFI value in Fig5.3.4-1(left).  

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