Lei, Lili, National Center for Atmospheric Research,
David R. Stauffer and Aijun Deng, The Pennsylvania State University
A hybrid
nudging-ensemble Kalman filter (HNEnKF), previously tested in the Lorenz
three-variable model and a two-dimensional shallow-water model, is now explored
in WRF/DART with real observations using a Cross Appalachian Tracer Experiment
case from September 1983 (CAPTEX-83).
The HNEnKF, effectively combining observation nudging and the ensemble
Kalman filter (EnKF), achieves a more gradual data assimilation and greatly
reduces the insertion noise compared to the EnKF.
Three-hourly surface
observations and twelve-hourly rawinsonde observations from the World
Meteorological Organization (WMO) are assimilated. These assimilated meteorological observations are used to
verify the priors of the experiments.
It is found that the HNEnKF generally obtains better priors than the
EnKF. To independently verify the
HNEnKF approach, the hourly WRF experiment results are also used to drive the
Second-Order Closure Integrated Puff (SCIPUFF) model to predict surface tracer
concentrations that are verified against the observed surface concentration
data. The HNEnKF analyses driving
SCIPUFF produce better statistics of the independent tracer data than the EnKF. Thus there appears to be some
advantages in the hourly dynamic analyses produced by the continuous HNEnKF
compared to the intermittent EnKF. The analyses of surface pressure tendency
demonstrate that the HNEnKF is able to provide better temporal smoothness and
dynamic consistency in the hourly analyses than the EnKF.