Extended Abstract (352K)9A.3
En4D-Var: An Innovative Data Assimilation Technique Combining Ensemble Forecast with 4D-Var
Qingnong Xiao, NCAR, Boulder, CO; and C. Liu
In this study, an ensemble based 4-dimensional variational data assimilation
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.
Session 9A, Advanced Methods for Data Assimilation—I
Wednesday, 14 January 2009, 10:30 AM-12:00 PM, Room 130
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