Step One (Optional): Run Python to Create Image of Domain

A Python script has been provided to plot the computational domain that is being run for this case. If desired, run the dtcenter/python container to execute Python in docker-space using the namelist.wps in the local scripts directory, mapping the output into the local pythonprd directory.

A successful completion of the Python plotting script will result in the following file in the pythonprd directory. This is the same image that is shown at the top of the page showing the model domain.

Step Two: Run WPS

Using the previously downloaded data (in ${PROJ_DIR}/data), while pointing to the namelists in the local scripts directory, run the dtcenter/wps_wrf container to run WPS in docker-space and map the output into the local wpsprd directory.

Once WPS begins running, you can watch the log files being generated in another window by setting the ${CASE_DIR} environment variable and tailing the log files:

Type CTRL-C to exit the tail utility.

A successful completion of the WPS steps will result in the following files (in addition to other files) in the wpsprd directory

Step Three: Run real.exe

Using the previously downloaded data (in ${PROJ_DIR}/data), output from WPS in step one, and pointing to the namelists in the local scripts directory, run the dtcenter/wps_wrf container to this time run real.exe in docker-space and map the output into the local wrfprd directory. 

The real.exe program should take less than a minute to run, but you can follow its progress as well in the wrfprd directory:

Type CTRL-C to exit the tail utility.

Step One (Optional): Run Python to Create Image of Domain

A Python script has been provided to plot the computational domain that is being run for this case. If desired, run the dtcenter/python container to execute Python in singularity-space using the namelist.wps in the local scripts directory, mapping the output into the local pythonprd directory.

A successful completion of the Python plotting script will result in the following file in the pythonprd directory. This is the same image that is shown at the top of the page showing the model domain.

Step Two: Run WPS

Using the previously downloaded data (in ${PROJ_DIR}/data), while pointing to the namelists in the local scripts directory, run the dtcenter/wps_wrf container to run WPS in singularity-space and map the output into the local wpsprd directory. 

Once WPS begins running, you can watch the log files being generated in another window by setting the ${CASE_DIR} environment variable and tailing the log files:

Type CTRL-C to exit the tail utility.

Step Three: Run real.exe

Using the previously downloaded data (in ${PROJ_DIR}/data), output from WPS in step one, and pointing to the namelists in the local scripts directory, run the dtcenter/wps_wrf container to this time run real.exe in singularity-space and map the output into the local wrfprd directory.

The real.exe program should take less than a minute to run, but you can follow its progress as well in the wrfprd directory:

Type CTRL-C to exit the tail utility.

A successful completion of the WPS steps will result in the following files (in addition to other files) in the wpsprd directory

Using the previously downloaded data (in ${PROJ_DIR}/data), while pointing to the namelist in the local scripts directory, run the dtcenter/gsi container to run GSI in docker-space and map the output into the local gsiprd directory:

As GSI is run the output files will appear in the local gsiprd/. Please review the contents of that directory to interrogate the data.

Once GSI begins running, you can watch the log file being generated in another window by setting the ${CASE_DIR} environment variable and tailing the log file:

Type CTRL-C to exit the tail.

A successful completion of the GSI step will result in the following files (in addition to other files) in the gsiprd directory

Using the previously downloaded data (in ${PROJ_DIR}/data), while pointing to the namelist in the local scripts directory, create a container using the gsi image to run GSI in singularity-space and map the output into the local gsiprd directory:

As GSI is run the output files will appear in the local gsiprd/. Please review the contents of that directory to interrogate the data.

Once GSI begins running, you can watch the log file being generated in another window by setting the ${CASE_DIR} environment variable and tailing the log file:

A successful completion of the GSI step will result in the following files (in addition to other files) in the gsiprd directory

Type CTRL-C to exit the tail.

Using the previously downloaded data (in ${PROJ_DIR}/data), while pointing to the namelists in the local scripts directory, run the dtcenter/wps_wrf container to run WRF in docker-space and map the output into the local wrfprd directory.

Note: Read the following two options carefully and decide which one is right for you to run - you DO NOT need to run both. Option One runs with 4 processors by default and Option Two allows for a user specified number of processors using the "-np #" option.

Option One: Default number (4) of processors

By default WRF will run with 4 processors using the following command: 

Option Two: User-specified number of processors

If you run into trouble on your machine when using 4 processors, you may want to run with fewer (or more!) processors by passing the "-np #" option to the script. For example the following command runs with 2 processors:

As WRF is run the NetCDF output files will appear in the local wrfprd/. Please review the contents of that directory to interrogate the data.

Once WRF begins running, you can watch the log file being generated in another window by setting the ${CASE_DIR} environment variable and tailing the log file:

Type CTRL-C to exit the tail.

A successful completion of the WRF step will result in the following files (in addition to other files) in the wrfprd directory:

Using the previously downloaded data in ${PROJ_DIR}/data, while pointing to the namelists in the local scripts directory, create a container from the wps_wrf image to run WRF in singularity-space and map the output into the local wrfprd directory.

Note: Read the following two options carefully and decide which one is right for you to run - you DO NOT need to run both. Option One runs with 4 processors by default and Option Two allows for a user specified number of processors using the "-np #" option.

Option One: Default number (4) of processors

By default WRF will run with 4 processors using the following command: 

Option Two: User-specified number of processors

If you run into trouble on your machine when using 4 processors, you may want to run with fewer (or more!) processors by passing the "-np #" option to the script. For example the following command runs with 2 processors:

As WRF is run the NetCDF output files will appear in the local wrfprd/. Please review the contents of that directory to interrogate the data.

Once WRF begins running, you can watch the log file being generated in another window by setting the ${CASE_DIR} environment variable and tailing the log file:

tail -f ${CASE_DIR}/wrfprd/rsl.out.0000

Type CTRL-C to exit the tail.

A successful completion of the WRF step will result in the following files (in addition to other files) in the wrfprd directory:

Using the previously created netCDF wrfout files in the wrfprd directory, while pointing to the namelist in the local scripts directory, run the dtcenter/upp container to run UPP in docker-space to post-process the WRF data into grib2 format, and map the output into the local postprd directory:

As UPP is run, the post-processed GRIB output files will appear in the postprd/. Please review the contents of that directory to interrogate the data.

UPP runs quickly for each forecast hour, but you can see the log files generated in another window by setting the ${CASE_DIR} environment variable and tailing the log files:

Type CTRL-C to exit the tail.

A successful completion of the UPP step will result in the following files (in addition to other files) in the postprd directory:

Using the previously created netCDF wrfout data in the wrfprd directory, while pointing to the namelists in the local scripts directory, create a container using the upp image to run UPP in singularity-space and map the output into the local postprd directory:

As UPP is run the post-processed GRIB output files will appear in the postprd/. Please review the contents of those directories to interrogate the data.

UPP runs quickly for each forecast hour, but you can see the log files generated in another window by setting the ${CASE_DIR} environment variable and tailing the log file:

Type CTRL-C to exit the tail.

A successful completion of the UPP step will result in the following files (in addition to other files) in the postprd directory:

Pointing to the scripts in the local scripts directory, run the dtcenter/python container to create graphics in docker-space and map the images into the local pythonprd directory:

After Python has been run, the plain image output files will appear in the local pythonprd/ directory.

The images may be visualized using your favorite display tool.

Pointing to the scripts in the local scripts directory, create a container using the python_3.5 singularity image to create graphics in singularity-space and map the images into the local pythonprd directory:

After Python has been run, the plain image output files will appear in the local pythonprd/ directory.

The images may be visualized using your favorite display tool.

Using the previously downloaded data (in ${PROJ_DIR}/data), while pointing to the output in the local scripts and postprd directories, run the dtcenter/met container to run the verification software in docker-space and map the statistical output into the local metprd directory:

MET will write a variety of ASCII and netCDF output files to the local metprd/. Please review the contents of the directories: grid_stat, pb2nc, pcp_combine, and point_stat, to interrogate the data.

Using the previously downloaded data (in ${PROJ_DIR}/data), while pointing to the output in the local scripts and postprd directories, create a container using the nwp-container-met image to run the verification software in singularity-space and map the statistical output into the local metprd directory:

MET will write a variety of ASCII and netCDF output files to the local metprd/. Please review the contents of the directories: grid_stat, pb2nc, pcp_combine, and point_stat, to interrogate the data.

In order to visualize the MET output using the METviewer database and display system, you first need to launch the METviewer container.

The MET statistical output then needs to be loaded into the MySQL database for querying and plotting by METviewer

The METviewer GUI can then be accessed with the following URL copied and pasted into your web browser:

The METviewer GUI can be run interactively to create verification plots on the fly. However, to get you going, two sample plots are provided. Do the following in the METviewer GUI:

• Click the "Choose File" button and navigate on your file system to:
       ${PROJ_DIR}/container-dtc-nwp/components/scripts/snow_20160123/metviewer/plot_APCP_03_ETS.xml

• Click "OK" to load the XML to the GUI and populate all the required options.
• Click the "Generate Plot" button on the top of the page to create the image.

Next, follow the same steps to create a plot of 10-meter wind components with this XML file:

• ${PROJ_DIR}/container-dtc-nwp/components/scripts/snow_20160123/metviewer/plot_WIND_Z10.xml

Feel free to make changes in the METviewer GUI and use the "Generate Plot" button to make new plots. For example, the following plot was created by changing the "Independent Variable" field to "VALID_HOUR", including times from 0 through 12 hours, and changing the X label appropriately:

Note: Use of METviewer with Singularity is only supported on AWS!

In order to visualize the MET output using the METviewer database and display system, you first need to build Singularity sandbox from the docker container using 'fix-perms' options. The execution of this step creates a metv4singularity directory.

Next, start the Singularity instance as 'writable' and call it 'metv':

Then, initialize and start MariaDB and Tomcat:

Then, navigate to the scripts area and run a shell in the Singularity container:

Now it is time to load the MET output into a METviewer database. As a note, the metv_load_singularity.ksh script requires two command-line arguments: 1) name of the METviewer database (e.g., mv_snow), and 2) the ${CASE_DIR}

The METviewer GUI can then be accessed with the following URL copied and pasted into your web browser (where IPV4_public_IP is your IPV4Public IP from the AWS “Active Instances” web page):

http://IPV4_public_IP:8080/metviewer/metviewer1.jsp

Click the "Load XML" button in the top-right corner.

The METviewer GUI can be run interactively to create verification plots on the fly. However, to get you going, two sample plots are provided. Do the following in the METviewer GUI:

• Click the "Choose File" button and navigate on your file system to:
       ${PROJ_DIR}/container-dtc-nwp/components/scripts/snow_20160123/metviewer/plot_APCP_03_ETS.xml

• Click "OK" to load the XML to the GUI and populate all the required options.
• Click the "Generate Plot" button on the top of the page to create the image.

Next, follow the same steps to create a plot of 10-meter wind components with this XML file:

• ${PROJ_DIR}/container-dtc-nwp/components/scripts/snow_20160123/metviewer/plot_WIND_Z10.xml

Feel free to make changes in the METviewer GUI and use the "Generate Plot" button to make new plots.

You can also create plots via the METviewer batch plotting capability (i.e., not the METviewer GUI). A script to run the two supplied METviewer XMLs provides an example on how to create plots. Note you must be in your metviewer singularity shell to run it, as shown below:

singularity shell instance://metv
cd ${PROJ_DIR}/container-dtc-nwp/components/scripts/snow_20160123/metviewer ./metv_plot_singularity.ksh ${CASE_DIR}

The output goes to: ${CASE_DIR}/metviewer/plots, and you can use display to view the images.