Assimilation of Accumulated Precipitation Data

Reference: Precipitation Data Assimilation in the WRFDA Users' Guide

Source code

Get the pre-compiled code, if you have not done so.

WRFDA/var/build/da_wrfvar.exe is the executable that will be used in this practice session.

Working directory

We recommend you create a new working directory for this exercise:

mkdir /home/ec2-user/workdir/rainfall
cd /home/ec2-user/workdir/rainfall

Input data

The test data for this rainfall tutorial case can be found in /home/ec2-user/test_data/Rainfall This test case is the same domain as the 3dvar test case (continental US, February 5, 2008), but at a lower resolution due to the constraints of the classroom computers ln -fs /home/ec2-user/test_data/Rainfall/ob/ob.rain.2008020518.06h ob07.rain ln -fs /home/ec2-user/test_data/Rainfall/rc/wrfinput_d01 ./fg ln -fs /home/ec2-user/test_data/Rainfall/rc/wrfinput_d01 . ln -fs /home/ec2-user/test_data/Rainfall/rc/wrfbdy_d01 . ln -fs /home/ec2-user/test_data/Rainfall/be/be.dat . These are the observation, first guess, boundary, and background error files, respectively. It is necessary to link a few more files for a 4D-VAR precipitation run

ln -fs /home/ec2-user/compiled_code/WRFDA-v4.1.2/run/RRTM_DATA_DBL ./RRTM_DATA
ln -fs /home/ec2-user/compiled_code/WRFDA-v4.1.2/run/*.TBL .

Edit namelists

A very basic namelist.input file for running this tutorial case is available at /home/ec2-user/test_data/Rainfall/namelist.input.

cp /home/ec2-user/test_data/Rainfall/namelist.input .
vi namelist.input

Pay special attention to the following namelist variables, making sure they are set to these values:

&wrfvar1
var4d=true, Remember: precipitation DA only works with 4DVAR.
var4d_lbc=false,
var4d_bin=3600,
var4d_bin_rain=21600,
...
/
...
&wrfvar4
use_rainobs=true,
thin_rainobs=true,
thin_mesh_conv=20.,
/

Run 4D-VAR with Accumulated Rainfall Observations

mpirun -np 6 /home/ec2-user/compiled_code/WRFDA-v4.1.2/var/build/da_wrfvar.exe

Note! In the provided namelist, we have set a small limit 5 for ntmax (maximum number of iterations of the cost function minimization) like we did for some other exercises. Therefore, on the AWS node this case will take ~3 minutes.