Prerequisites: Software

The Requirements section in the Software Installation chapter of the METplus User's Guide lists the software and Python packages that are required to run the METplus wrappers. Note that there is a core set of requirements needed to run the METplus wrappers and additional requirements needed to utilize some of the more advanced features.

Prerequisites: Environment

The following instructions are required so the commands in this tutorial can be copied and run without modification. The steps involve creating a working directory to store files used/generated by the tutorial and configuring a simple script that can be run to set up the shell environment. Once configured correctly, the script can be run upon returning to the tutorial content to easily resume progress.

Pre-Configured Environments

Setting up the Tutorial Environment on Hera (NOAA)

Setting up the Tutorial Environment on Jet (NOAA)

Setting up the Tutorial Environment on Cheyenne (NCAR)

Setting up the Tutorial Environment on Seneca (NCAR)

User Configured Environments

Setting up the Tutorial Environment (bash)

Setting up the Tutorial Environment (csh)

Verify Environment is Set Correctly

Run the Tutorial Setup Script

The tutorial setup script sets the paths for METPLUS_TUTORIAL_DIR, METPLUS_BUILD_BASE, MET_BUILD_BASE, and METPLUS_DATA. It also appends the $PATH environment variable to include the directory where the METplus scripts are located. If necessary, it may also load modules needed for the METplus software to run correctly.

Check Path

Make sure that all of the environment variables are set to the appropriate values and that the path is set up to locate the METplus components.

You should see the usage statement for Point-Stat. The version number listed should correspond to the version listed in MET_BUILD_BASE. If it does not, you will need to either reload the met module, or add ${MET_BUILD_BASE}/bin to your PATH.

$METPLUS_TUTORIAL_DIR

The directory you created to store all of your tutorial files

Example value:

Example contents:

$MET_BUILD_BASE

The directory where MET is installed

Example value:

Example contents

Check contents of MET bin directory

Example contents:

$METPLUS_BUILD_BASE

The directory where METplus is installed

Example value:

Example contents:

$METPLUS_DATA

The directory containing sample input data to use for the tutorial

Example value:

Example contents:

METplus directory structure

A brief description and overview of the METplus/ directory structure can be found in the METplus User's Guide section called METplus Wrappers Directory Structure. The files/directories in ${METPLUS_BUILD_BASE} should match this list.

METplus default configuration file

Look inside the directory ${METPLUS_BUILD_BASE}/parm

Look at the METplus default configuration file:

The METplus default configuration file (defaults.conf) is always read first. Any additional configuration files passed in on the command line are then processed in the order in which they are specified. This allows for each successive conf file the ability to override variables defined in any previously processed conf files. It also allows for defining and setting up conf files from a general (settings used by all use cases, ie. MET install dir) to more specific (Plot type when running track and intensity plotter) structure. The idea is to created a hiearchy of conf files that is easier to maintain, read, and manage. It is important to note, running METplus creates a single configuration file, which can be viewed to understand the result of all the conf file processing.

When METplus is run, the final metplus conf file is generated here:
metplus_runtime.conf:METPLUS_CONF={OUTPUT_BASE}/metplus_final.conf.YYYYMMDDHHmmss

This is a unique final config file for each METplus run. Use this file to see the result of all the conf file processing; this can be very helpful when troubleshooting.

NOTE: The syntax for METplus configuration files MUST include a "[config]" section header with the variable names and values on subsequent lines.

More information about the default configuration variables can be found in the METplus User's Guide section called Default Configuration File.
 

The met_config directory (in ${METPLUS_BUILD_BASE}/parm) contains "wrapped" MET configuration files that are used by calls to the MET applications via the METplus wrappers. The wrappers set environment variables that control settings in the wrapped MET configuration files through these environment variables. See the METplus User's Guide section called How METplus controls MET configuration variables for more information.

METplus Use Cases

The use_cases directory - this is where the use cases you will be running exist. Under the use_cases directory are two directories: met_tool_wrapper and model_applications. The met_tool_wrapper directory contains use cases that run a single METplus wrapper. They are a good starting point to see how the wrapper scripts generate commands that run the MET tools. The model_applications directory contains more complex use cases that often run multiple wrappers and demonstrate real evaluations from users.

MET Tool Wrapper Use Cases

Look at the MET Tool Wrapper Use Cases

The met_tool_wrapper use case files are organized into subdirectories by wrapper, e.g. Example or GridStat. They contain METplus configuration files (ending with .conf). If a MET tool that is called by a use case uses a MET configuration file, the file used for the met_tool_wrapper use cases is found in parm/met_config.

• met_tool_wrapper/Example - directory
• met_tool_wrapper/Example/Example.conf - use case configuration file
• met_tool_wrapper/GridStat - directory
• met_tool_wrapper/GridStat/GridStat.conf - use case configuration file
• parm/met_config/GridStatConfig_wrapped - MET configuration file used in the GridStat.conf use case

Model Application Use Cases

Look at the Model Application use cases

The model_applications use case files are organized in directories by category, e.g. precipitation or data_assimilation. They contain METplus configuration files (ending with .conf). If additional files such as Python Embedding scripts are included with a use case, these files are found in a directory named after the METplus configuration file without the .conf extension.

• model_applications/data_assimilation - directory
• model_applications/data_assimilation/StatAnalysis_fcstHAFS_obsPrepBufr_JEDI_IODA_interface.conf - use case configuration file
• model_applications/data_assimilation/StatAnalysis_fcstHAFS_obsPrepBufr_JEDI_IODA_interface - directory containing supplemental files for the use case
• model_applications/data_assimilation/StatAnalysis_fcstHAFS_obsPrepBufr_JEDI_IODA_interface/read_ioda_mpr.py - script called by the use case

Example Use Case (aka "Hello World" Example - METplus style)

Let's look at the Example use case, Example.conf, under met_tool_wrapper/Example

In this file are variables, denoted in ALL_CAPS. These can be modified as needed. In the METplus system, unmodified config files remain in the directories under ${MET_BUILD_BASE}, while user-modified files are stored in ${METPLUS_TUTORIAL_DIR}/user_config.

No changes are needed in Example.conf. Close it and continue to the next page.

Unless otherwise indicated, all directories are relative to your ${METPLUS_TUTORIAL_DIR} directory.

Modify your Tutorial/User conf files

In this section you will modify the configuration files that will be read for each call to METplus.

The paths in this practical session guide assume:

• You have created a user_config directory in your ${METPLUS_TUTORIAL_DIR} directory
• You have added the shared METplus ush directory to your PATH (done in the Tutorial Setup script)
• You are using the shared installation of MET.

If not, then you need to adjust accordingly.

1. Change to the ${METPLUS_TUTORIAL_DIR} directory.  Try running run_metplus.py. You should see the usage statement output to the screen.

2. Now try to pass in the example.conf configuration file found in your parm directory under use_cases/met_tool_wrapper/Examples tutorial.conf

You should see output like this:

Note it ends with an error message stating that OUTPUT_BASE was not set correctly. You will need to configure the METplus wrappers to be able to run a use case.

Some variables in the system conf are set to '/path/to' and must be overridden to run METplus, such as OUTPUT_BASE in defaults.conf.

3. View the defaults.conf file and notice how OUTPUT_BASE = /path/to . This implies it is REQUIRED to be overridden to a valid path.

4. View the tutorial configuration files in your ${METPLUS_TUTORIAL_DIR} directory.

The INPUT_BASE, OUTPUT_BASE, and MET_INSTALL_DIR variables must all be set to run METplus. Since MET_INSTALL_DIR (and possibly INPUT_BASE) should already be set in the default METplus configuration file (completed on install of METplus), only OUTPUT_BASE is required to run. If INPUT_BASE is not set in the tutorial configuration file, it should be set correctly in the defaults configuration file.

We will test out using these configurations on the next page.

Running METplus

Running METplus involves invoking the python script run_metplus.py followed by a list of configuration files.

Reminder: The default configuration file (defaults.conf) is always read in and processed first before the configuration files passed in on the command line.

If you have configured METplus correctly and call run_metplus.py without passing in any configuration files, it will generate a usage statement to indicate that other config files are required to perform a useful task. It will generate an error statement if something is amiss.

1. Review the Example.conf configuration file - which is METplus' version of a "Hello World" example

2. Call the run_metplus.py script again, this time passing in the Example.conf configuration file and the tutorial.conf configuration file. You should see logs output to the screen.

Note: The environment variable METPLUS_BUILD_BASE determines where to look for paths to use_case files. The METPLUS_TUTORIAL_DIR environment variable determines where to look for user-modified config files.

3. Check the directory specified by the OUTPUT_BASE configuration variable. You should see that files and sub-directories have been created

4. Review the METplus log file to see what was run. Compare the log output to the Example.conf configuration file to see how they correspond to each other. The log file will have today's date in the filename. Since METplus was configured to list today's timestamp in YYYYMMDDHHMMSS format, each run of METplus will generate a separate log file. This is the same behavior as the METplus final configuration file. List all of the log files and view the latest METplus log file:

You will notice that METplus ran for 5 valid times, processing 4 forecast hours for each valid time. For each run time, it ran twice using two different input templates to find files.

5. Now run METplus passing in the Example.conf and tutorial.conf files from the previous run AND an explicit override of the OUTPUT_BASE variable.

6. Check the directory that corresponds to the config.OUTPUT_BASE value that you set in the command line override. You should see that files and sub-directories have been created in the new location. Note that you can reference an environment variable when setting values on the command line.

Remember: Additional conf files and config variable overrides are processed after the default METplus config file (defaults.conf). OUTPUT_BASE was set in tutorial.conf and then overridden with config.OUTPUT_BASE.
Order matters, since each successive conf file and/or explicit variable override will take precedence over any value set for variables defined previously.

Note: The processing order allows for structuring your conf files to contain system/user configurations (settings used for every run) and use case specific configurations (settings only used for a given use case).

Timing Control in METplus

METplus configuration variables that control timing information are described in the Timing Control section of the System Configuration chapter in the METplus User's Guide. The Example wrapper is a good tool to help understand how these settings control what is run by the METplus wrappers.

1. Copy the Example.conf configuration file in your user_config directory, renaming it Example_timing.conf

2. Open the new Example_timing.conf file with an editor.

3. Make the following changes:

3a. Change VALID_BEG and VALID_END:

    to:

3b. Change LEAD_SEQ:

    to:

3c. Change EXAMPLE_CUSTOM_LOOP_LIST:

    to:

4. Call the run_metplus.py script again, this time passing in the Example_timing.conf configuration file and the tutorial.conf configuration file. You should see logs output to the screen.

5. Review the screen output and/or log file output.

6. Open Example_timing.conf again to increase the frequency of output by making the following changes:

6a. Change VALID_END:

    to:

7. Call the run_metplus.py script again and see how the output has changed.

8. Change the timing settings to loop by initialization (or retrospective) time. Open Example_timing.conf again and make the following changes:

8a. Change LOOP_BY:

    to:

8b. Change VALID_TIME_FMT to INIT_TIME_FMT:

    to:

8c. Change VALID_BEG to INIT_BEG:

    to:

8d. Change VALID_END to INIT_END:

    to:

8e. Change VALID_INCREMENT to INIT_INCREMENT:

    to:

9. Call the run_metplus.py script once again and see how the output has changed.

10. Change the value for LOOP_BY from INIT to its nickname RETRO. Open Example_timing.conf again and make the following changes:

10a. Change LOOP_BY:

    to:

11. Call the run_metplus.py script another time.

Filename Template Settings in METplus

METplus configuration variables that control filename templates are described in the Directory and Filename Template Info section of the System Configuration chapter in the METplus User's Guide. The Example wrapper is a good tool to help understand how these settings control what is run by the METplus wrappers.

1. List the sample available in the ${METPLUS_DATA} directory

The output should list:

2. Copy the Example_timing.conf configuration file in your user_config directory, renaming it Example_filename.conf

3. Open the new Example_filename.conf file with an editor.

4. Make the following changes:

4a. Change EXAMPLE_INPUT_DIR:

    to:

4b. Change EXAMPLE_INPUT_TEMPLATE:

    to:

5. Call the run_metplus.py script again, this time passing in the Example_timing.conf configuration file and the tutorial.conf configuration file. You should see logs output to the screen.

6. Review the screen output and/or log file output.

7. Open the new Example_filename.conf file with an editor.

8. Make the following changes:

8a. Change INIT_BEG and INIT_END:

    to:

9. Call the run_metplus.py script again, passing in the modified Example_timing.conf configuration file and the tutorial.conf configuration file. You should see logs output to the screen.

10. Review the screen output and/or log file output.  It appears it is now looking for the right file.

The METplus configuration variable named LOG_TIMESTAMP_TEMPLATE controls to timestamp that is included in the log file names. The default value of LOG_TIMESTAMP_TEMPLATE is %Y%m%d%H%M%S, which will include the year, month, day, hour, minute, and second when the run_metplus.py command was executed. This will create a new log file each time run_metplus.py is called from the command line.

Starting in METplus v5.0.0, the log timestamp is also included by default in the final configuration file that is generated by the METplus wrappers. This helps with debugging because the final config file and its corresponding log file(s) are more easily identified. The path to the final config file is set by METPLUS_CONF.

The values of these settings can be changed by including them in a METplus configuration file that is passed into run_metplus.py. They can also be set directly in the run_metplus.py command using the syntax config.VARIABLE_NAME=VALUE. The following examples will use the latter.

Changing the Log Timestamp Template

In this example we will change the log timestamp template to only include the year, month, and day of the run. This will create a single log file each day. Each call to run_metplus.py will add its log output to the daily file.

1. Run the Example_timing use case from the previous exercise and override the log timestamp template to only include year, month, and day.

2. List the contents of the log directory and notice that there is now a log file that matches the format metplus.log.YYYYMMDD

3. Run the Example_filename use case from the previous exercise and again override the log timestamp template to only include YYYYMMDD.

4. Review the log file and notice that it contains the log output from both runs.

5. Search for the word "Running" to see each command.

Adding Run ID in Log Timestamp Template

The METplus configuration variable RUN_ID was added in METplus v5.0.0. This variable contains an 8 character string that is automatically generated by and is unique to each call to run_metplus.py. This variable can be referenced in other METplus configuration variables. This can be useful in a variety of ways. For example, it can be used to distinguish log files for METplus runs that may have started within the same second.

1. Run the Example_timing use case and override the log timestamp template to include the RUN_ID.

2. List the contents of the log directory and notice that there is now a log file that matches the format metplus.log.YYYYMMDDHHMMSS.XXXXXXXX where XXXXXXXX is a random set of characters.

Since PCP-Combine performs a simple operation and reformatting step, no configuration file is needed.

1. Start by making an output directory for PCP-Combine and changing directories:

2. Now let's run PCP-Combine twice using some sample data that's included with the MET tarball:

The "\" symbols in the commands above are used for ease of reading. They are line continuation markers enabling us to spread a long command line across multiple lines. They should be followed immediately by "Enter". You may copy and paste the command line OR type in the entire line with or without the "\".

Both commands run the sum command which searches the contents of the -pcpdir directory for the data required to create the requested accmululation interval.

In the first command, PCP-Combine summed up 4 3-hourly accumulation forecast files into a single 12-hour accumulation forecast. In the second command, PCP-Combine summed up 12 1-hourly accumulation observation files into a single 12-hour accumulation observation. PCP-Combine performs these tasks very quickly.

We'll use these PCP-Combine output files as input for Grid-Stat. So make sure that these commands have run successfully!

When PCP-Combine is finished, you may view the output NetCDF files it wrote using the ncdump and ncview utilities. Run the following commands to view contents of the NetCDF files:

The ncview windows display plots of the precipitation data in these files. The output of ncdump indicates that the gridded fields are named APCP_12, the GRIB code abbreviation for accumulated precipitation. The accumulation interval is 12 hours for both the forecast (3-hourly * 4 files = 12 hours) and the observation (1-hourly * 12 files = 12 hours).

Note, if ncview is not found when you run it on your system, you may need to load it first.  For example, on hera, you can use this command:

Plot-Data-Plane Tool

The Plot-Data-Plane tool can be run to visualize any gridded data that the MET tools can read. It is a very helpful utility for making sure that MET can read data from your file, orient it correctly, and plot it at the correct spot on the earth. When using new gridded data in MET, it's a great idea to run it through Plot-Data-Plane first:

Ghostview (gv) can take a little while before it displays.  If you don't have gv on your computer, try using display, or any tool that can visualize PostScript files, e.g.:

Another option is to create a PNG file from the PS file, also rotating it to appear the right way:

Next try re-running the command list above, but add the convert(x)=x/25.4; function to the config string (Hint: after the level setting and ; but before the last closing tick) to change units from millimeters to inches. What happened to the values in the colorbar?

Now, try re-running again, but add the censor_thresh=lt1.0; censor_val=0.0; options to the config string to reset any data values less 1.0 to a value of 0.0. How has your plot changed?

The convert(x) and censor_thresh/censor_val options can be used in config strings and MET config files to transform your data in simple ways.

We have run examples of the PCP-Combine -sum command, but the tool also supports the -add, -subtract, and -derive commands. While the -sum command defines a directory to be searched, for -add, -subtract, and -derive we tell PCP-Combine exactly which files to read and what data to process. The following command adds together 3-hourly precipitation from 4 forecast files, just like we did in the previous step with the -sum command:

By default, PCP-Combine looks for accumulated precipitation, and the 03 tells it to look for 3-hourly accumulations. However, that 03 string can be replaced with a configuration string describing the data to be processed, which doesn't have to be accumulated precipation. The configuration string should be enclosed in single quotes. Below, we add together the U and V components of 10-meter wind from the same input file. You would not typically want to do this, but this demonstrates the functionality. We also use the -name command line option to define a descriptive output NetCDF variable name:

While the -add command can be run on one or more input files, the -subtract command requires exactly two. Let's rerun the wind example from above but do a subtraction instead:

Now run Plot-Data-Plane to visualize this output. Use the -plot_range option to specify a the desired plotting range, the -title option to add a title, and the -color_table option to switch from the default color table to one that's good for positive and negative values:

Now view the results:

While the PCP-Combine -add and -subtract commands compute exactly one output field of data, the -derive command can compute multiple output fields in a single run. This command reads data from one or more input files and derives the output fields requested on the command line (sum, min, max, range, mean, stdev, vld_count).

Run the following command to derive several summary metrics for both the 10-meter U and V wind components:

In the above example, we used a wildcard to list multiple input file names.  And we used the -field command line option twice to specify two input fields.  For each input field, PCP-Combine loops over the input files, derives the requested metrics, and writes them to the output NetCDF file.  Run ncview to visualize this output:

This output file contains 8 variables: 2 input fields * 4 metrics. Note the output variable names the tool chose.  You can still override those names using the -name command line argument, but you would have to specify a comma-separated list of 8 names, one for each output variable.

Defining the field string

As you'll see throughout these exercises, the behavior of the MET and METplus tools is controlled using ASCII configuration files, and you will learn more about those options in the coming sessions. The field_string command line argument is actually processed as a miniature configuration file. In fact, that string is written to a temporary file which is then read by MET's configuration file library code.

In general, the name and level entries are required to extract a gridded field of data from a supported input file format. The conventions for specifying them vary based on the input file type:

1. For GRIB1 or GRIB2 inputs, set name as the abbreviation for the desired variable or data type that appears in the GRIB tables and set level to a single letter (A, Z, P, L, or R) to define the level type followed by a number to define the level value. For example 'name = "TMP"; level = "P500";' extracts 500 millibar temperature from a GRIB file.
2. For NetCDF inputs, set name as the NetCDF variable name and level to define how to extract a 2-dimensional slice of gridded data from that variable. For example, 'name = "temperature"; level = "(0,1,*,*)";' extracts a 2-dimensional field of data from the last two dimensions of a NetCDF temperature variable using the first two indicies as constant values.
3. For Python embedding, set name as the python script to be run along with any arguments for that script and do not set level. For example, 'name = "read_my_data.py input.txt";' runs a python script to read data from the specified input file.

Note that all field strings should be enclosed in single quotes, as shown above, so that they are processed on the command line as a single string which may contain embedded whitespace.

Examples for each of these input types are provided in the coming exercises. More details about setting the field string can be found in the Configuration File Overview section of the MET User's Guide. For example, if a field string matches multiple records in an input GRIB file, additional filtering criteria may be specified to further refine them. Additional options exist to explicitly specify the input file_type, define a function to convert the data, or define an operation to censor the data. Each configuration entry should be terminated with a semi-colon (;).

Since the field_string is processed using a temporary file, any syntax errors will produce a parsing error log message similar to the following:

Error messages like this typically mean there is a problem in a configuration string or configuration file being read by MET.

Plot GRIB Data

Start by creating a directory for our Plot-Data-Plane output:

Next, run Plot-Data-Plane to plot 2-meter temperature from a GRIB1 input file:

The default verbosity level of 2 only prints log messages about input and output files.

Next, re-run at verbosity level 4 to see more detailed log message about the grid being read, timing information, and the range of the data values:

It is a great idea to inspect the log messages to sanity check the metadata. Without needing to understand all the details, does the grid definition look reasonable? Are the range of values reasonable for this variable type? Are the timestamps of the data consistent with the input file name?

Next, open the PostScript output file. On some machines, the ghostview utility (or common gv alias) can display PostScript files. On other, the display command works well. On Macs, simply run open. The example below uses gv which is available in the METplus AMI:

Optionally, if the convert utility is in your path, it can be run to change the image file format. Indicate the desired output file format by specifying the suffix (.png shown below).

The plot is created using the default color table (met_default.ctable) and is scaled to the range of valid data (275 to 305). By default, no title is provided and the input file is listed as a sub-title. Does the pattern of the data look reasonable? Does it correspond well to the background map data?

If the plot of the data and the metadata listed in the log messages look reasonable, you can be confident that MET is reading your data well. In addition, the field_string you used to retrieve this data can be used in the configuration strings and configuration files for other MET tools.

While this Plot-Data-Plane validation step is not necessary for every input file, it is very useful when getting started with new input data sources.

Plot NetCDF Data

The NetCDF file format is very flexible and enables the creation of self-describing data files. However, that flexibility makes it impossible to write general purpose software to interpret all NetCDF files. For that reason, MET supports a few types of NetCDF file formats, but does not support all NetCDF files, in general. It can ingest NetCDF files that follow the Climate-Forecast Convention, are created by the WRF-Interp utility, or are created by other MET tools. Additional details can be found in the MET Data I/O chapter of the MET User's Guide.

As described in The Field String, set name to the name of the desired NetCDF variable and level to define how to index into the dimensions of that variable. In the NetCDF level strings, use *,* to indicate the two gridded dimensions. For other, non-gridded dimensions, pick a 0-based integer to specify the value to be used for that dimension. For the time dimension, if present, selecting a 0-based integer does work, however you can also specify a time string in YYYYMMDD[_HH[MMSS]] format. The square braces indicate optional elements of the format. So 19770807, 19770807_12, and 19770807_120000 are all valid time strings. It is often easier to specify a time string directly rather than finding the integer index corresponding to that time string.

Run Plot-Data-Plane to plot quantitative precipitation estimate (QPE) data from a CF-compliant NetCDF file. First run ncdump -h to take a look at the header:

Note the following from the header:

The variable P06M_NONE has 3 dimensions, but the time dimension only has length 1. Therefore, setting level = "(0,*,*)"; should do the trick. Also note that the global Conventions attribute identifies this file as being CF-compliant. Let's plot it, this time adding a title:

Running at verbosity level 4, we see the range of values:

If needed, run convert to reformat:

The next example extracts a specific time string from a precipitation analysis dataset:

Note the following from the header:

Let's request a specific time string, specify a title, and use the same plotting range as above, rather than defaulting to the range of input data values.

If running Plot-Data-Plane for multiple output times or data sources, consider using the -plot_range option to make their color scales comparable.

Lastly, let's plot output a NetCDF mask file that was created by an earlier version of MET's Gen-Vx-Mask tool.

We will plot the NCEP_Region_ID variable with only 2 dimensions:

Let's specify a different color table rather than using the default one.

Python Embedding

While the MET tools can read data from a few input gridded data file types, its ability to read data in memory from python greatly enhances its utility. Support for python embedding is optional, and must be enabled at compilation time as described in Appendix F of the MET User's Guide. MET supports three types of python embedding:

1. Reading a field of gridded data values.
2. Passing a list of point observations.
3. Passing a list of matched forecast/observation pair values.

On this page, we'll demonstrate only the first type, reading a field of gridded data values. When getting started with a new dataset via python, validating the logic by running Plot-Data-Plane is crucial. When MET reads data from flat files, important information, like the location and orientation of the data, is defined in the metadata. That is not the case for python embedding, and the responsibility for confirming those details falls to the user. So while python embedding provide extra flexibility, it also requires additional diligence.

Let's run the simplest of examples using sample data included with the MET release. The first step is to confirm that python script runs by itself, outside of MET.

This sample read_ascii_numpy.py script reads data from the input fcst.txt ASCII file and gives it a name, Forecast. Always run new python scripts on the command line first to confirm there aren't any syntax errors in the script itself. The required conventions for the python script are details in the Python Embedding for 2D Data section of the MET User's Guide.

Next, let's run Plot-Data-Plane using this python script to define the input data. As described in The Field String, this is done with the name configuration string and the level string does not apply.

Since there is no input_filename to be specified as the first required argument for Plot-Data-Plane, we provide the constant string PYTHON_NUMPY in that spot. This triggers Plot-Data-Plane to interpret the field string as a python embedding script to be run. Specifying PYTHON_XARRAY also works but requires slightly different conventions in the python embedding script.

When MET is compiled, it links to python libraries that it uses to instantiate a python interpreter at runtime. That compile time instance does have a few required packages, but will likely not include all packages that every user may want to load. You may find that your python script runs fine on the command line, but Plot-Data-Plane's call to python can't load a requested module. In that case, set the ${MET_PYTHON_EXE} environment variable to tell MET which instance of python you'd like to run.

The following command just uses that version of python that is already present in your path:

Rerunning the command from above should produce the same result, but if you look closely at the log messages, you'll see that your custom python version writes a temporary file, and MET's compile time python version reads data from it.

You can find several python embedding examples on the Sample Analysis Scripts page of the MET website. Each example includes both a python script and sample input data file. Please also see METplus Python Embedding use case examples. 

Start by making an output directory for Gen-Vx-Mask and changing directories:

Since Gen-Vx-Mask performs a simple masking step, no configuration file is needed.

We'll run the Gen-Vx-Mask tool to apply a polyline for the CONUS (Contiguous United States) to our model domain. Run Gen-Vx-Mask on the command line using the following command:

Re-run using verbosity level 3 and look closely at the log messages. How many grid points were included in this mask?

Gen-Vx-Mask should run very quickly since the grid is coarse (185x129 points) and there are 243 lat/lon points in the CONUS polyline. The more you increase the grid resolution and number of polyline points, the longer it will take to run. View the NetCDF bitmap file generated by executing the following command:

Notice that the bitmap has a value of 1 inside the CONUS polyline and 0 everywhere else. We'll use the CONUS mask we just defined in the next step.

You could try running plot_data_plane to create a PostScript image of this masking region. Can you remember how?

Notice that there are several ways that gen_vx_mask can be run to define regions of interest, some of which will be demonstrated over the next few pages.

Using a pre-defined NCEP grid from the NCEP ON388 Grid Identification Table, we'll create a latitude band, using the "lat" masking type, for the tropics region. Run Gen-Vx-Mask on the command line using the following command:

Most of the grids defined in ON388 Table B can be referenced in MET as "GNNN" where NNN is the 3-digit grid number. Run "ncdump -h" on the output file and notice that the mask variable is named "lat_mask":

Use the "-name" command line option, as shown below, to override this default.

To compute the intersection or union of two masks, use the output from the first run as input to the second. Run Gen-Vx-Mask on the command line using the following command, which uses the "lon" masking type:

Compare this intersection output to the union of the two masks, computed below:

Now we'll use the "grid" masking type to select a subgrid from a larger grid. Run Gen-Vx-Mask on the command line using the following command:

On the next page, we'll demonstrate using the "data" and "solar_alt" masking types.

On this page, we provide examples for land/sea mask and also a solar altitude to show where it is daytime on a global grid.

Run Gen-Vx-Mask on the command line using the following command:

A corresponding water mask could be created using two different methods. One way is to simply rerun the land mask command above using the -complement option:

Another way is to specify a different threshold (eq1 instead of eq0) rather than taking the complement:

Now we'll run Gen-Vx-Mask to show where it’s daytime on a global grid:

Next, combine the LAND output from the previous run with the solar altitude mask for daytime:

This creates a mask for grid points on land experiencing daylight at 18:30 UTC on 20170601.

On the next page, we'll demonstrate using the "track" and "circle" masking types.

On this page, we provide examples for using the "track" masking type using BEST track hurricane data and "circle" masking type.

Start by extracting the lat/lon locations for Hurricane Dorian:

Inspect the dorian.poly file that begins with "DORIAN" and has the lat/lon storm locations on subsequent lines.

Compute a mask for all grid points within 200 km of the Hurricane Dorian track. Run Gen-Vx-Mask using the following command:

Extract the first track point for Dorian and use the circle masking option to select all grid points within 500 km:

Run Gen-Vx-Mask on the command line using the following command:

The "circle" mask type draws a circle around each of the lat/lon points in the input poly file. The "circle" option may be useful when verifying within a certain radius of known radar locations. If you rerun without the "-thresh" command line option, Gen-Vx-Mask still runs but prints a warning message:

Can you figure out what this output file now contains? Run ncview or plot_data_plane to visualize it.

Gen-Vx-Mask also supports the "box", "solar_azi", and "shape" masking types, not covered in these exercises. Interested users can download Natural Earth shapefiles and run Gen-Vx-Mask using the "-type shape" option.

Next, we'll take a look at using the "shape" masking type with Gen-Vx-Mask.

We will demonstrate the Gen-Vx-Mask "shape" masking type using freely available shapefiles from Natural Earth.  While multiple resolutions are provided, we'll use the coarsest version for this example since it's the smallest in size.

Download the Natural Earth administrative shapefiles for countries boundaries.

Run gen_vx_mask to define the mask for the USA (index 4).

Re-run to compute the union with Canada (index 3).

Re-run to add Mexico (index 27).

The result is good but not perfect. There are a few missing grid points along the boundary. But this demonstrates how the tool works. Consider re-running all three commands again, but this time use the "-value" command line option to define the mask value to be written. Just make "-value" match the "-shapeno" option (.e.g. -value 4 for USA, -value 3 for Canada, and -value 27, for Mexico). What impact does that have on the result?

Next, we'll take a look at the functionality that Grid-Stat offers.

Start by making an output directory for Grid-Stat and changing directories:

The behavior of Grid-Stat is controlled by the contents of the configuration file passed to it on the command line. The default Grid-Stat configuration file may be found in the data/config/GridStatConfig_default file. Prior to modifying the configuration file, users are advised to make a copy of the default:

Open up the GridStatConfig_tutorial file for editing with your preferred text editor.

The configurable items for Grid-Stat are used to specify how the verification is to be performed. The configurable items include specifications for the following:

• The forecast fields to be verified at the specified vertical level or accumulation interval
• The threshold values to be applied
• The areas over which to aggregate statistics - as predefined grids, configurable lat/lon polylines, or gridded data fields
• The confidence interval methods to be used
• The smoothing methods to be applied (as opposed to interpolation methods)
• The types of verification methods to be used

You may find a complete description of the configurable items in the grid_stat configuration file section of the MET User's Guide. Please take some time to review them.

For this tutorial, we'll configure Grid-Stat to verify the 12-hour accumulated precipitation output of PCP-Combine. We'll be using Grid-Stat to verify a single field using NetCDF input for both the forecast and observation files. However, Grid-Stat may in general be used to verify an arbitrary number of fields. Edit the GridStatConfig_tutorial file as follows:

• Set:
  
  fcst = {
     field = [
        {
          name       = "APCP_12";
          level      = [ "(*,*)" ];
          cat_thresh = [ >0.0, >=5.0, >=10.0 ];
        }
     ];
  }
  obs = fcst;

  To verify the field of precipitation accumulated over 12 hours using the 3 thresholds specified.

• Set:
  
  mask = {
     grid = [];
     poly = [ "../gen_vx_mask/CONUS_mask.nc",
              "MET_BASE/poly/NWC.poly",
              "MET_BASE/poly/SWC.poly",
              "MET_BASE/poly/GRB.poly",
              "MET_BASE/poly/SWD.poly",
              "MET_BASE/poly/NMT.poly",
              "MET_BASE/poly/SMT.poly",
              "MET_BASE/poly/NPL.poly",
              "MET_BASE/poly/SPL.poly",
              "MET_BASE/poly/MDW.poly",
              "MET_BASE/poly/LMV.poly",
              "MET_BASE/poly/GMC.poly",
              "MET_BASE/poly/APL.poly",
              "MET_BASE/poly/NEC.poly",
              "MET_BASE/poly/SEC.poly" ];
  }

  To accumulate statistics over the Continental United States (CONUS) and the 14 NCEP verification regions in the United States defined by the polylines specified. To see a plot of these regions, execute the following command:

  gv ${MET_BUILD_BASE}/share/met/poly/ncep_vx_regions.pdf &

• In the boot dictionary, set:
  
  n_rep = 500;

  To turn on the computation of bootstrap confidence intervals using 500 replicates.

• In the nbrhd dictionary, set:
  
  width          = [ 3, 5 ];
  cov_thresh = [ >=0.5, >=0.75 ];

  To define two neighborhood sizes and two fractional coverage field thresholds.

• Set:
  
  output_flag = {
     fho     = NONE;
     ctc      = BOTH;
     cts      = BOTH;
     mctc   = NONE;
     mcts   = NONE;
     cnt     = BOTH;
     sl1l2   = BOTH;
     sal1l2 = NONE;
     vl1l2   = NONE;
     val1l2 = NONE;
     vcnt = NONE;
     pct     = NONE;
     pstd   = NONE;
     pjc     = NONE;
     prc     = NONE;
     eclv    = NONE;
     nbrctc = BOTH;
     nbrcts = BOTH;
     nbrcnt = BOTH;
     grad    = BOTH;
     dmap  = NONE;
     seeps  = NONE;
  }

  To compute contingency table counts (CTC), contingency table statistics (CTS), continuous statistics (CNT), scalar partial sums (SL1L2), neighborhood contingency table counts (NBRCTC), neighborhood contingency table statistics (NBRCTS), and neighborhood continuous statistics (NBRCNT).

Next, run Grid-Stat on the command line using the following command:

Grid-Stat is now performing the verification tasks we requested in the configuration file. It should take a minute or two to run. The status messages written to the screen indicate progress.

In this example, Grid-Stat performs several verification tasks in evaluating the 12-hour accumulated precipiation field:

• For continuous statistics and partial sums (CNT and SL1L2), 15 output lines each:
  (1 field * 15 masking regions)
• For contingency table counts and statistics (CTC and CTS), 45 output lines each:
  (1 field * 3 raw thresholds * 15 masking regions)
• For neighborhood methods (NBRCNT, NBRCTC, and NBRCTS), 90 output lines each:
  (1 field * 3 raw thresholds * 2 neighborhood sizes * 15 masking regions)

To greatly increase the runtime performance of Grid-Stat, you could disable the computation of bootstrap confidence intervals in the configuration file. Edit the GridStatConfig_tutorial file as follows:

• In the boot dictionary, set:
  
  n_rep = 0;

  To disable the computation of bootstrap confidence intervals.

Now, try rerunning the Grid-Stat command listed above and notice how much faster it runs. While bootstrap confidence intervals are nice to have, they take a long time to compute, especially for gridded data.

The output of Grid-Stat is one or more ASCII files containing statistics summarizing the verification performed and a NetCDF file containing difference fields. In this example, the output is written to the current directory, as we requested on the command line. It should now contain 10 Grid-Stat output files beginning with the grid_stat_ prefix, one each for the CTC, CTS, CNT, SL1L2, GRAD, NBRCTC, NBRCTS, and NBRCNT ASCII files, a STAT file, and a NetCDF matched pairs file.

The format of the CTC, CTS, CNT, and SL1L2 ASCII files will be covered for the Point-Stat tool. The neighborhood method and gradient output are unique to the Grid-Stat tool.

• Rather than comparing forecast/observation values at individual grid points, the neighborhood method compares areas of forecast values to areas of observation values. At each grid box, a fractional coverage value is computed for each field as the number of grid points within the neighborhood (centered on the current grid point) that exceed the specified raw threshold value. The forecast/observation fractional coverage values are then compared rather than the raw values themselves.
• Gradient statistics are computed on the forecast and observation gradients in the X and Y directions.

Since the lines of data in these ASCII files are so long, we strongly recommend configuring your text editor to NOT use dynamic word wrapping. The files will be much easier to read that way.

Execute the following command to view the NetCDF output of Grid-Stat:

Click through the 2d vars variable names in the ncview window to see plots of the forecast, observation, and difference fields for each masking region. If you see a warning message about the min/max values being zero, just click OK.

Now dump the NetCDF header:

View the NetCDF header to see how the variable names are defined.

Notice how *MANY* variables there are, separate output for each of the masking regions defined. Try editing the config file again by setting apply_mask = FALSE; and gradient = TRUE; in the nc_pairs_flag dictionary. Re-run Grid-Stat and inspect the output NetCDF file. What affect did these changes have?

We have now successfully run the PCP-Combine and Grid-Stat tools to verify 12-hourly accumulated preciptation for a single output time. We did the following steps:

• Identified our forecast and observation datasets.
• Constructed PCP-Combine commands to put them into a common accumulation interval.
• Configured and ran Grid-Stat to compute our desired verification statistics.

Now that we've defined the logic for a single run, the next step would be writing a script to automate these steps for many model initializations and forecast lead times. Rather than every MET user rewriting the same type of scripts, use METplus to automate these steps in a use case!

Answers to Exercises from Session 1

These are the answers to the exercises from the previous page. Feel free to ask a MET representative if you have any questions!