ARW SINGLE OBSERVATION TEST W/ GLOBAL BE

Setting up the Run Script

For this exercise, make a copy of the prepared basic run script from the section Prepare run script for basic cases: 

cp run_gsi_regional.ksh run_gsi_regional.ksh_psot. 

Make the following additional modifications to the script run_gsi_regional.ksh_psot:

• Set the name/path for the analysis run directory
• Set up the one observation test option to true
• if_oneob=Yes
• Select the background field format
• bk_core=ARW
• Select the global background error covariance
• bkcv_option=GLOBAL

An example of this run script is available from the link run_gsi_regional.ksh

Check the GSI namelist

Users can check the namelist to see how to set up a single observation test. The related namelist options include :

• oneobtest=.true.
• Set the location of the pseudo observation: &SINGLEOB_TEST
maginnov=1.0,magoberr=0.8,oneob_type='t',
oblat=38.,oblon=279.,obpres=500.,obdattim=${ANAL_TIME},
obhourset=0.,
/

An example namelist is available here.

ARW SINGLE OBSERVATION TEST W/ GLOBAL BE

Running the Script

In this example, GSI is run as a 4-core MPI job. If you named your run script run_gsi_regional.ksh_psot and run on PBS system (Cheyenne), type: 

qsub run_gsi_regional.ksh_psot 

to launch the job. The progress of the job can be monitored by examining the tail of the standard out file in the run directory as set in the variable WORK_ROOT: 

tail stdout

When completed,the contents of this run directory are provided in the following list .

ARW SINGLE OBSERVATION TEST W/ NAM BE

Setting up the Run Script

For this exercise, make a copy of the prepared basic run script: 
cp run_gsi_regional.ksh_basic run_gsi_regional.ksh_psot. 

Make the following additional modifications to the script run_gsi_regional.ksh_psot:

• Set the name/path for the analysis run directory
• Set up the one observation test option to true
• if_oneob=Yes
• Select the background field format
• bk_core=ARW
• Select the NAM background error covariance
• bkcv_option=NAM

An example of this run script is available from the link run_gsi_regional.ksh

Check the GSI namelist

Users can check the namelist to see how to set up a single observation test. The related namelist options include :

• oneobtest=.true.
• Set the location of the pseudo observation: &SINGLEOB_TEST
maginnov=1.0,magoberr=0.8,oneob_type='t',
oblat=38.,oblon=279.,obpres=500.,obdattim=${ANAL_TIME},
obhourset=0.,
/

An example namelist is available here.

ARW SINGLE OBSERVATION TEST W/ NAM BE

Running the Script

In this example, GSI is run as a 4-core MPI job. If you named your run script run_gsi_regional.ksh and run on PBS system (Cheyenne), type: 

qsub run_gsi_regional.ksh 

to launch the job. 

The progress of the job can be monitored by examining the tail of the standard out file in the run directory as specified in the variable WORK_ROOT: 

tail stdout

When completed, the contents of this run directory are provided in the following list .

ARW SINGLE OBSERVATION TEST W/ NAM BE

Results

The standard output file stdout contains the run diagnostics, such as convergence information and observation distribution from the GSI run. Details of the standard output file are available in section 4.1 of the GSI User's Guide.

Information about the use of observations by the analysis, and the corresponding statistics are available from the fit files (namedfort.2*). The fit files located in the run directory should agree with the following fit files for temperature (fit_t1); wind (fit_w1);moisture (fit_q1); surface pressure (fit_p1).

Visualizing the Analysis

The model analysis may be visualized through use of the ncl script GSI_singleobs_arw.ncl provided with the community GSI under ./util/Analysis_Utilities/plots_ncl. It plots the XY (left column) and XZ (right column) cross sections of the analysis increment fields through the grid point that has the maximum temperature increment.

To visualize your output, copy the ncl script to run directory and change lines:

• change cdf_analysis = addfile("wrf_inout.cdf","r") to point to analysis results.
• change cdf_bk = addfile("${DATA_ROOT}/wrfout_d01_2018-08-12_12:00:00.cdf","r") to point to background.

A sample script can be found at GSI_singleobs_arw.ncl

 

Once you have customized the script, run the script with the command:
ncl GSI_singleobs_arw.ncl 

The script will generate a file: GSI_singleObse_T_arw.pdf. Use display GSI_singleObse_T_arw.pdf to show the image. Compare this image with the reference solution [PDF] for this configuration.

3DVAR GSI USING ARW BACKGROUND (PREPBUFR)

Setting up the Run Script

For this exercise, make a copy of the previously prepared run script: 
cp run_gsi_regional.ksh_basic run_gsi_regional.ksh. 

Make the following additional modifications to the script run_gsi_regional.ksh:

• Set the name/path for the analysis run directory to
• WORK_ROOT=${run directory}
• Select the background field format bk_core=ARW
• Select the NAM regional background error covariance bkcv_option=NAM
• Comment out the links to the radiance data, gpsro and radar data:
• # ln -s ${srcobsfile[$ii]} ${gsiobsfile[$ii]} 

 

An example of this run script is available from the link run_gsi_regional.ksh

3DVAR GSI USING ARW BACKGROUND (PREPBUFR)

Running the Script

For this example, GSI is run as a 4-core MPI job. If you run on PBS system (Cheyenne), type: 

qsub run_gsi_regional.ksh 

to launch the job.

The progress of the job can be monitored by examining the tail of the standard out file in the run directory as specfied in the variable WORK_ROOT: 

tail stdout

The contents of this run directory are provided in the following list.

3DVAR GSI USING ARW BACKGROUND (PREPBUFR)

Results

The standard output file stdout contains the run diagnostics, such as convergence information, and observation distribution from the GSI run. Details of the standard output file are available in section 4.1 of the GSI User's Guide.

Information about the use of observations by the analysis, and the corresponding innovations are available from the fit files (named fort.2*). The fit files located in the run directory should agree with the following fit files for temperature (fit_t1); wind(fit_w1); moisture (fit_q1); surface pressure (fit_p1); and radiance (fit_rad1); and GPS (fort.212); and radar radial velocity(fort.209).

Convergence information is available in the file: fort.220

Visualizing the Analysis

The model analysis may be visualized through modifying the ncl script Analysis_increment.ncl provided with the community GSI under ./util/Analysis_Utilities/plots_ncl. This script plots the analysis increments from conventional observations at at level 1 and 20.

To visualize your output, copy the ncl script to run directory and make the following changes:

• Set: cdf_analysis = addfile("wrf_inout.cdf","r") to point to analysis results
• Set: cdf_bk = addfile("${PATH}/wrf_inout.cdf","r") to point to background file.
• Set: kmax=1 for plot at level 2 or 20 for plot at level 21

The sample scripts for these plots can be found at GSI_Analysis_increment.ncl

 

Once you have customized the script for your output directory, run the script with the command:
ncl GSI_Analysis_increment.ncl 
Once done a pdf file GSI_Analysis_increment_20.pdf will be generated for 21 level analysis increment in the run directory. Compare these images with the reference solution [PDF]. Here is reference for the 2nd level [PDF].

3DVAR GSI USING ARW BACKGROUND (PREPBUFR AND OTHER OBS)

Setting up the Run Script

For this exercise, make a copy of the previously prepared run script: 
cp run_gsi_regional.ksh_basic run_gsi_regional.ksh. 

Make the following additional modifications to the script run_gsi_regional.ksh:

• Set the name/path for the analysis run directory to
• WORK_ROOT=${run directory}
• Select the background field format bk_core=ARW
• Select the NAM regional background error covariance bkcv_option=NAM
• Open the links to the radiance data, gpsro and radar data by un-commenting the following line:
• ln -s ${srcobsfile[$ii]} ${gsiobsfile[$ii]} 

An example of this run script is available from the link run_gsi_regional.ksh

3DVAR GSI USING ARW BACKGROUND (PREPBUFR AND OHTER OBS)

Running the Script

For this example, GSI is run as a 4-core MPI job. If you run on PBS system (Cheyenne), type: 

qsub run_gsi_regional.ksh 

to launch the job.

The progress of the job can be monitored by examining the tail of the standard out file in the run directory as specfied in the variable WORK_ROOT: 

tail stdout

The contents of this run directory are provided in the following list.

3DVAR GSI USING ARW BACKGROUND (PREPBUFR AND OTHER OBS)

Results

The standard output file stdout contains the run diagnostics, such as convergence information, and observation distribution from the GSI run. Details of the standard output file are available in section 4.1 of the GSI User's Guide.

Information about the use of observations by the analysis, and the corresponding innovations are available from the fit files (named fort.2*). The fit files located in the run directory should agree with the following fit files for temperature (fit_t1); wind(fit_w1); moisture (fit_q1); surface pressure (fit_p1); and radiance (fit_rad1); and GPS (fort.212); and radar radial velocity(fort.209).

Convergence information is available in the file: fort.220

Visualizing the Analysis

The model analysis may be visualized through modifying the ncl script Analysis_increment.ncl provided with the community GSI under ./util/Analysis_Utilities/plots_ncl. This script plots the data impact from additional satellite radiance, gpsro and radar data (analysis with conventional, satellite radiance, gpsro and radar data minus analysis with conventional data only) at level 31.

To visualize your output, copy the ncl script to run directory and make the following changes:

• Set: cdf_analysis = addfile("wrf_inout.cdf","r") to point to analysis results
• Set: cdf_bk = addfile("${Path to case 3 result}/wrf_inout.cdf","r") point to case 3 analysis file.
• Set: kmax=20 for plot at level 21

The sample scripts for these plots can be found at GSI_Analysis_increment.ncl

 

Once you have customized the script for your output directory, run the script with the command:
ncl Analysis_increment.ncl 
Once done a pdf file GSI_Analysis_increment_20.pdf will be generated in the run directory. Compare these images with the reference solution [PDF].

GSI 3D HYBRID FOR ARW USING GLOBAL ENSEMBLE FORECAST

Setting up the Run Script for GSI hybrid analysis

Copy the sample run script run_gsi_regional.ksh from the practical case 3 (ARW 3DVAR with PrepBUFR) to a working directory and make the following modifications to run GSI hybrid analysis:

• Set the name/path for the analysis run directory to
• WORK_ROOT=${run directory}
• Set the location of the ensemble files in the variable ENS_ROOT=...
• Set to run GSI hybrid analysis: if_hybrid=Yes
• Set not to run GSI 4D hybrid analysis: if_4DEnVar=No

Please note that the sample script provides links to the GFS ensemble data for this tutorial case only. If you are running your own GSI hybrid case with a different date, please make any necessary modifications to specify the variable ENS_ROOT and ENSEMBLE_FILE_mem in the run script.

SETUP GSI 3D HYBRID FOR ARW USING GLOBAL ENSEMBLE FORECAST

Running the Script

If you run on PBS system (Cheyenne), type: 

qsub run_gsi_regional.ksh 

to launch the job. 

The progress of the job can be monitored by examining the tail of the standard out file in the run directory as specified in the variable WORK_ROOT: 

tail stdout

When completed, the contents of this run directory are provided in the following list .

SETUP GSI 3D HYBRID FOR ARW USING GLOBAL ENSEMBLE FORECAST

Results

The standard output file stdout contains the run diagnostics, such as convergence information, and observation distribution from the GSI run. Details of the standard output file are available in section 4.1 of the GSI User's Guide.

Information about the use of observations by the analysis, and the corresponding innovations are available from the fit files (named fort.2*). The fit files located in the run directory should agree with the following fit files for temperature (fit_t1); wind(fit_w1); moisture (fit_q1); surface pressure (fit_p1); and radiance (fit_rad1); and GPS (fort.212); and radar radial velocity(fort.209).

Convergence information is available in the file: fort.220

Visualizing the Analysis

Use the same method as the practical case 3 (ARW 3DVAR) to make plots of the analysis increments. This time, plots will be made for the 2nd level (kmax=1) and level 21 (kmax=20). Once done pdf files GSI_Analysis_increment_1.pdf and GSI_Analysis_increment_20.pdf will be generated in the run directory. Compare these images with the reference solution [level 2 ] and [level 21].

GSI CYCLING RUN ARW BACKGROUND

GSI analysis at 12Z

For this step of GSI analysis at 12Z of August 12, 2018, we will use the GSI analysis output wrf_inout from the online exercise 03 ARW 3DVAR with conventional data (PrepBUFR) .

If you haven't practiced case 03, simply follow the steps in the above link to perform the GSI data assimilation and get the analysis.

GSI CYCLING RUN ARW BACKGROUND

Set up WRF-ARW run at 12Z

For this step of WRF-ARW run at 12Z of August 12, 2018, we will use the GSI analysis output wrf_inout from the online exercise 05 as the initial fields to launch 6-hour WRF forecast.

First, make sure you have a compiled code of the latest WRF-ARW code (V4.0). You can download the boundary condition for WRF from link . 

Please follow the WRF tutorial and documents for the details of the WRF system application. 

Here we provide the namelist file for reference of WRF run: 
namelist.input

Running the ARW and checking the forecast results

The ARW can be run by creating a run script run_wrf.ksh and submitting it in the run directory: 

bsub < run_wrf.ksh

It will take a few minutes to finish. Once done, users should see the forecast files in each forecast hour from 12z to 20z like 
wrfout_d01_2018-08-12_HH_00:00:00.

The contents of this run directory are provided in the following list .

The ARW standard output file rsl.out.0000 is povided for reference.

GSI CYCLING RUN ARW BACKGROUND

GSI analysis at 18Z

For this step of GSI analysis at 18Z of August 12, 2018, we will use the 6-hour forecast output from the WRF run at 12Z as the background and conventional observations at 18Z. The steps to set up the GSI analysis is very similar to case 03, except for the background field and observations. The example run script can be found here.

Running the GSI run Script and checking the results

After GSI runs, a run directory will be created according to the path set in the variable WORK_ROOT. The contents of this run directory are provided in the following list .

The standard output file stdout and the fit files for this GSI run: temperature (fit_t1); wind (fit_w1); moisture (fit_q1).

Convergence information (section 4.6 of the GSI User's Guide) is available in the file: fort.220

WRF-ARW run at 18Z

As a continued data assimilation run, the 18Z GSI analysis from the above step is then used as the initial field, together with the WRF background conditions at 18Z, to launch the WRF forecast at 18Z. The steps are very similar to the WRF run at 12Z and therefore not repeated here.

GSI 4D HYBRID FOR ARW USING GLOBAL ENSEMBLE FORECAST

Setting up the Run Script for GSI 4D hybrid analysis

Copy the sample run script run_gsi_regional.ksh from the practical case 3 (ARW 3DVAR with PrepBUFR) to a working directory and make the following modifications to run GSI 4D hybrid analysis:

• set the analysis time to 
  ANAL_TIME=2017051318
• Set the name/path for the analysis run directory to
• WORK_ROOT=${run directory}
• Set the location of the ensemble files in the variable ENS_ROOT=...
• set the BK_ROOT to where you store the background files, WRF forecast at 3 time levels
• set the path to the background file at the analysis time 
  BK_FILE=${BK_ROOT}/wrfout_d01_2018-08-12_18:00:00
• set to run 4D hybrid analysis 
  if_hybrid=Yes 
if_4DEnVar=Yes

As can be seen in the sample run script, for ${if_4DEnVar} = Yes, there are two additional background files at 1 hour before and after the analysis time, as specified in the variable BK_FILE_M1 and BK_FILE_P1; there are also two additional sets of ensemble files before and after the analysis time, as specified in the variable ENSEMBLE_FILE_mem_m1 and ENSEMBLE_FILE_mem_p1. Please make sure those variables setup right.

An example of this run script is available from the link run_gsi_regional.ksh

GSI 4D HYBRID FOR ARW USING GLOBAL ENSEMBLE FORECAST

Running the Script

If you run on PBS system (Cheyenne), type: 

qsub run_gsi_regional.ksh 

to launch the job. 

The progress of the job can be monitored by examining the tail of the standard out file in the run directory as specified in the variable WORK_ROOT: 

tail stdout

When completed, the contents of this run directory are provided in the following list .

GSI 4D HYBRID FOR ARW USING GLOBAL ENSEMBLE FORECAST

Results

The standard output file stdout contains the run diagnostics, such as convergence information, and observation distribution from the GSI run. Details of the standard output file are available in section 4.1 of the GSI User's Guide.

Information about the use of observations by the analysis, and the corresponding innovations are available from the fit files (named fort.2*). The fit files located in the run directory should agree with the following fit files for temperature (fit_t1); wind(fit_w1); moisture (fit_q1); surface pressure (fit_p1); and radiance (fit_rad1); and GPS (fort.212); and radar radial velocity(fort.209).

Convergence information is available in the file: fort.220

Visualizing the Analysis

Use the same method as the practical case 3 (ARW 3DVAR) to make plots of the analysis increments. This time, plots will be made for the 2nd level (kmax=1) and level 21 (kmax=20). Once done pdf files GSI_Analysis_increment_1.pdf and GSI_Analysis_increment_20.pdf will be genera ted in the run directory. Compare these images with the reference solution [level 2 ] and [level 21].