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An Ensemble of Data Assimilations(EDA) is an ensemble of independent 4D-Var data assimilations where the main analysis error sources (observation errors, model errors, and boundary condition errors) are represented by perturbing the related quantities - respectively observations, forecast model, and soil moisture + sea-surface temperature + sea ice, according to their estimated accuracy.  which aims to:

  • give estimates of analysis and short-range forecast uncertainty.
  • provide flow-dependent background error estimates for the deterministic 4D-Var system.

The EDA analyses are generated by randomly perturbing the observations and, across appropriate length scales, the sea surface temperature, sea ice and soil moisture fields.  The main analysis error sources according to their estimated accuracy:

  • observation errors are represented by perturbing observations.  The observations are assumed unbiased (once any dynamic bias correction has been applied)

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  • and observation errors are assumed to have a normal distribution.

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  • model errors are represented by perturbing the forecast model.  Model error is simulated using the Stochastically Perturbed Parameterisation Tendencies scheme (SPPT).  The same SPPT configuration is used in EDA as in the ensemble.
  • boundary condition errors are represented by perturbing soil moisture, sea-surface temperature, and sea ice across appropriate length scales.

ENS.  Differences between pairs of analyses (and forecast) fields have the statistical characteristics of analysis (and forecast) error. 


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Fig5.1.7: An idealized schematic showing how the 12h 12 hour assimilation window used by 4D-Var (left part of the diagram) modifies the initial trajectories of the EDA members (in blue) to reflect the information from the assimilated observations (black dots with error bars).  The analysis trajectories (in green) have taken into account the new observations and thus are confined within a narrower ensemble.  Assimilating the new observations reduces the spread.  Also a bias has been corrected by reducing the magnitude of some of the largest values in the original ensemble.

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