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The studyshowed that the initial conditions are improved by using the satellite
data assimilation and result in a reduced forecast error for heavy rainfall location.
Therefore, it is not surprising that considerable effort has focused on improving the
estimates of the model initial states through data assimilation.
This paper is organized as follows. Section
25.2
describes the real-time configu-
ration of WRF-ARW and data assimilation system. The observational datasets used
in the verification are given in Sect.
25.3
. Section
25.4
explains the methodology
used in the evaluation. The results of the forecast error for the May 2006 case are
presented in Sect.
25.5
. Section
25.6
investigates the impact of data assimilation on
the forecasts. Finally, a summary and discussion are given in Sect.
25.7
.
25.2
Model and Data Assimilation System
25.2.1
ARW WRF Regional Model
The numerical weather prediction model used in this study is the WRF model
(
Michalakes et al. 2001
;
Skamarock et al. 2005
), which is a nonhydrostatic, fully
compressible, primitive equation model. Lead institutions involved in the effort to
develop this model include the National Center for Atmospheric Research (NCAR),
Air Force Weather Agency (AFWA), National Centers for Environmental Prediction
(NCEP), National Oceanic and Atmospheric Administration (NOAA), and other
government agencies and universities. WRF is built around a software architectural
framework in which different dynamical cores and model physics packages are
presented within the same code. With the WRF model, it is possible to mix and
match the dynamical cores and physics packages of different models to optimize
performance since each model has strengths and weaknesses in different areas and
weather events. It uses a terrain-following hydrostatic pressure coordinate and the
Arakawa C grid staggering.
25.2.2
GSI 3DVAR Data Assimilation System for ARW WRF
Regional Model
The Gridpoint Statistical Interpolation (GSI) analysis system (
Kleist et al. 2009a
,
b
)
is developed based on NCEP's current three-dimensional variational analysis
(3DVAR) system known as the Spectral Statistical Interpolation (SSI) (
Parrish and
Derber 1992
;
Derber et al. 1991
). The SSI has the advantage that the statistics of
the background error, both structure and amplitude, can be easily obtained and
applied in the analysis procedure. It is simpler to apply a diagonal background
error covariance in spectral space than to convolve the corresponding smoothing
kernel with the innovations in physical space. However, with only a diagonal
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