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Reanalysis combines observations made in the past with the current IFS model to provide a complete, consistent and model-compatible numerical representation of past weather and climate.  

Reanalyses contain estimates of atmospheric parameters:

• atmospheric parameters (e.g. air temperature, pressure, and wind at different altitudes)

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• surface parameters (e.g. rainfall, soil moisture content, and sea-surface temperature).

The   The estimates are produced as grid-box averages for all locations on earth. The reanalyses can made from data several decades old even when data was less available than currently.  

A reanalysis is necessary for initiation of each re-forecast.  Spacial resolution is are generally different between reanalyses and the model version used to create the re-forecasts.   In particular, spatial resolutions are generally different.

ERA5

ERA5 is an improved and more comprehensive ECMWF climate reanalysis that and is the fundamental initialising analysis for re-forecasts.

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ERA-Interim is ECMWF's previous atmospheric reanalysis, based on a 2006 version of the IFS.  ERA-Interim data are available for dates from 1979 until 31st August 2019 and but are no longer updated.  ERA5 replaced ERA-Interim in 2019.

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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 ENS 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 as for to the operational ENSensemble, but do does not involve any data assimilation.  These The perturbations derive from singular vectors (SVs) plus geographical averages of Ensemble ensemble of Data Assimilations 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 ENS ensemble runs.  

The set of re-forecast ensembles are 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 section for medium range M-climate, extended range ER-M-climate, and seasonal S-M-climate.

The procedures adopted for using re-forecasts allow for seasonal variations and model changes to be taken into account.  But it should be noted that note the model climates (M-climate, ER-M-climate, or S-M-climate) can nevertheless be different from the observed climate.

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Forecasters should consider possible deficiencies in model climates when considering EFI data.  For an example of the effect, see Fig5.3.1 and Fig5.3.2.  Such effects have also appeared in the Extended Range ENS and Seasonal extended range ensemble and seasonal forecasts.

Fig5.3.1(left): Extreme Forecast Index (EFI) for 2m temperature for Days10-15, ENS ensemble forecast run data time DT 00UTC 20 June 2017. 

Fig5.3.2(right): Cumulative Distribution Function(CDF) for 2 m 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 corrwspomdimg 2 m CDF temperature curve (black).   This affects subsequent EFI and SOT fields.   Here the realistic real-time forecast of 2 m temperature CDF (red) over Lake Superior is thus incorrectly flagged as having a strongly negative EFI value in Fig5.3.1.  

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