WRF ARW User's Guide - MMM - UCAR

WRF-VAR
> cp –p $DAT_DIR/rc/2008020512/wrfbdy_d01 ./wrfbdy_d01 (IMPORTANT:
make a copy of wrfbdy_d01 as the wrf_bdy_file will be overwritten
by da_update_bc.exe)
> vi parame.in
&control_param
wrfvar_output_file = './wrfvar_output'
wrf_bdy_file = './wrfbdy_d01'
wrf_input = '$DAT_DIR/rc/2008020512/wrfinput_d01'
cycling = .false. (set to .true. if WRF-Var first guess comes
from a previous WRF forecast.)
debug = .true.
low_bdy_only = .false.
update_lsm = .false.
/
> ln –sf WRFDA/var/da/da_update_bc.exe ./da_update_bc.exe
> ./da_updatebc.exe
At this stage, you should have the files wrfvar_output and wrfbdy_d01 in your WRF-
Var working directory. They are the WRF-Var updated initial condition and boundary
condition for any subsequent WRF model runs. To use, just link a copy of
wrfvar_output and wrfbdy_d01 to wrfinput_d01 and wrfbdy_d01, respectively, in
your WRF working directory.
Running gen_be
Starting with WRFDA version 3.1, the users have two choices to define the background
error covariance (BE). We call them CV3 and CV5 respectively. Both are applied the
same set of the control variables, stream function, unbalanced potential velocity, unbalanced
temperature, unbalanced surface pressure, and pseudo relative humidity. With
CV3, the control variables are in physical space while with CV5 the control variables are
in eigenvector space. So the major differences between these two kinds of BE are the vertical
covariance. CV3 used the vertical recursive filter to model the vertical covariance
but CV5 used the empirical orthogonal function (EOF) to represent the vertical covariance.
The recursive filters to model the horizontal covariance are also different in these
two BEs. We have not conducted the systematic comparison of the analyses based on
these two BEs. However, CV3 (a BE file provided with our WRF-Var system) is a global
BE and can be used for any regional domains while CV5 is a domain-dependent BE,
which should be generated based in the forecasts data from the same domain. At this
moment, it is hard to tell which BE is better; the impact on analysis may be varying case
by case.
CV3 is the NCEP background error covariance, it is estimated in grid space by what has
become known as the NMC method (Parrish and Derber 1992) . The statistics are estimated
with the differences of 24 and 48-hour GFS forecasts with T170 resolution valid at
the same time for 357 cases distributed over a period of one year. Both the amplitudes
and the scales of the background error have to be tuned to represent the forecast error in
the guess fields. The statistics that project multivariate relations among variables are also
derived from the NMC method.
WRF-ARW V3: User’s Guide 6-34