technique (En4D-Var) is developed and applied to the Weather Research and

Forecasting (WRF) model. En4D-Var is a new and innovative design for data

assimilation. It uses the flow-dependent background error covariance matrix

from ensemble forecasts and performs 4D-Var optimization. It adopts the

incremental approach and preconditioning idea in variational algorithm while

avoiding tangent linear and adjoint models in its formulation and implementation.

This advantage will enable us to utilize the model as a constraint like 4D-Var

but can include any physics as needed in assimilation. The WRF En4D-Var system

is tested using Observing System Simulation Experiment (OSSE), and a real case

study. It shows robust assimilation capability.