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A four-dimensional ensemble variational (4D-En-Var) data assimilation has beendeveloped for a limited area model. The integration of tangent linear andadjoint models, as applied in standard 4D-Var, is replaced with the use of anensemble of non-linear model states to estimate four-dimensional backgrounderror covariances over the assimilation time window. The computational costsfor 4D-En-Var are therefore significantly reduced in comparison with standard4D-Var and the scalability of the algorithm is improved.  The flow dependency of 4D-En-Var assimilation increments is demonstrated insingle simulated observation experiments and compared with correspondingincrements from standard 4D-Var and Hybrid 4D-Var ensemble assimilationexperiments. Real observation data assimilation experiments carried out overa 6-week period show that 4D-En-Var outperforms standard 4D-Var as well asHybrid 4D-Var ensemble data assimilation with regard to forecast qualitymeasured by forecast verification scores

Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)