# METplus solutions for Multicategorical Forecast Verification

METplus solutions for Multicategorical Forecast Verification## METplus solutions for Multicategorical Forecast Verification

One important note regarding how to define multicategorical thresholds within the MET and METplus wrapper configuration files should be discussed. METplus requires that when multiple thresholds are listed using the **cat_thresh** variable to calculate any multicategorical line types, the thresholds must be monotonically increasing and use the same inequality type. This is done in order to ensure that the thresholds create unique and discrete bins of values, rather than overlapping thresholds that do not provide any sound statistical value. In practice, this means that the following two examples would result in a METplus error:

Example 1.

Example 2.

In example 1, the thresholds decrease with each entry which violates the requirement of monotonically increasing. With a simple reordering of the thresholds, example 1 can use the same thresholds and run with success:

In example 2, the final threshold uses a different inequality than the other two thresholds which violates the requirement that all multicategorical thresholds use the same inequality type. This rewrite of example 2 will provide the same information desired from the original thresholds, but will now successfully run in METplus:

This time, the rewrite changed the final inequality to match the first two while also keeping the final bin of values that METplus will calculate, >=15.5, consistent with the desired information. More information on how MET creates the value bins from multicategory thresholds is provided in the MET example of Multicategorical Forecast Verification.

Now that you know a bit more about verification measures for multicategorical, deterministic forecasts, it’s time to show how you can access those same statistics in METplus!

In order to better understand the delineation between METplus, MET, and METplus wrappers which are used frequently throughout this tutorial but are NOT interchangeable, the following definitions are provided for clarity:

- METplus is best visualized as an overarching framework with individual components. It encapsulates all of the repositories: MET, METplus wrappers, METdataio, METcalcpy, and METplotpy.
- MET serves as the core statistical component that ingests the provided fields and commands to compute user-requested statistics and diagnostics.
- METplus wrappers is a suite of Python wrappers that provide low-level automation of MET tools and plotting capability. While there are examples of calling METplus wrappers without any underlying MET usage, these are the exception rather than the rule

### MET solutions

The MET User’s Guide provides an Appendix that dives into statistical measures that it calculates, as well as the line type it is a part of. Statistics are grouped together by application and type and are available to METplus users in line types. To delineate between the calculation method for binary categorical and multicategorical forecast skill scores, METplus has two pairs of separate, but similar line types. As discussed in detail in the Binary Categorical forecasts section, the Contingency Table Statistics (CTS) line type and Contingency Table Counts (CTC) line type are for users who want single category forecast statistics. It’s important to note that the CTS line type must also be utilized by users who want scalar statistics from multicategorical forecasts, except for Accuracy. To accomplish this, simply follow the guidance listed in the Verification Statistics section for Multicategorical Forecasts. The complements to CTS and CTC in the multicategory group are the aptly named Multicategory Contingency Table Statistics (MCTS) line type and Multicategory Contingency Table Counts (MCTC) line type. Similar to the CTC, MCTC allows direct access to each of the counts from the contingency table of multicategorical forecasts. MCTS contains all of the skill scores that were discussed in the Multicategorical Verification statistics section, as well as the scalar statistic Accuracy, which are linked to their appendix description here for your convenience (except for Gerrity, which does not appear in the appendix):

### METplus Wrapper Solutions

The same statistics that are available in MET are also available with the METplus wrappers. To better understand how MET configuration options for statistics translate to METplus wrapper configuration options, you can utilize the Statistics and Diagnostics Section of the METplus wrappers User’s Guide, which lists all of the statistics available through the wrappers, including which tools can output which statistics. To access the line type through the tool, find your desired tool in the list of available commands for that tool. Once you do, you’ll see the tool will have several options that contain _OUTPUT_FLAG_, which will exhibit the same behavior and accept the same settings as the line types in MET’s output_flag dictionary, so be sure to review the available settings to get the line type output you want.