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ERA5 is an improved and more comprehensive ECMWF climate reanalysis that is the fundamental initialising analysis for re-forecasts.    It replaced ERA-Interim in 2019.

ERA products are normally updated once per month and within three months of real-time.  Quality assurance processing is applied to ensure consistency by removal of biases in models and observations.   Preliminary daily updates of the dataset can be available to users within seven days of real time.   ERA5 is available for dates from 1979 and is being extended forward in near real time.

The main differences of ERA5 from ERA-interim are

• higher spacial resolution (137 levels, 31km; 62km for EDA).
• higher output frequency of analysis fields (hourly, 3-hourly for EDA).
• introduction of uncertainty estimates.
• additional input observation types.
• many more output parameters.
• use of a much longer period of historical data (back to 1950).

ERA5 also provides:

• a much improved representation of the troposphere.
• an improved representation of tropical cyclones.
• better global balance of precipitation and evaporation.
• better precipitation over land in the deep tropics.
• better soil moisture.
• more consistent sea surface temperatures and sea ice.

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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  ERA-Interim data are available for dates from 1979 until 31st August 2019 and are no longer updated.  ERA5 replaced ERA-Interim in 2019.

The main differences of ERA5 from ERA-interim are

• • higher spacial resolution (137 levels, 31km; 62km for EDA).
  • higher output frequency of analysis fields (hourly, 3-hourly for EDA).
  • introduction of uncertainty estimates.
  • additional input observation types.
  • many more output parameters.
  • use of a much longer period of historical data (back to 1950).

Re-forecasts

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 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 similar as for the operational ENS, but do not involve any data assimilation.  These perturbations derive from singular vectors 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 ENS runs.  

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