MET Stat Use Case: Categorical Scores

This use case demonstrates how to run VCasT to compute categorical statistics using MET .stat files and a YAML configuration.

It uses a sample configuration file (cts.yaml) that filters and aggregates categorical scores like POD, FAR, CSI, FBIAS, and GSS for reflectivity forecasts.

Prerequisites

Before running the example, make sure VCasT is installed. Follow the installation steps in Quick Start Guide.

Run the Example

Clone the test repository:

git clone https://github.com/NOAA-GSL/VCasT-tests
cd VCasT-tests/examples/MET/cts_metrics

Run VCasT with the provided YAML file:
```
vcast cts.yaml
```
This will load and process the .stat files under the ./stats directory using the configuration options defined below.

YAML Configuration Explained

Below is the content of cts.yaml, which configures VCasT to:

Load categorical statistics (line_type: cts)
Filter by model, variable, threshold, lead time, and region
Aggregate the results by model, variable, and lead time

Sample cts.yaml configuration

input_stat_folder: ./stats
line_type: cts

date_column: "fcst_valid_beg"
start_date: "2024-04-29_12:00:00"
end_date: "2024-05-31_12:00:00"

string_filters:
  model: ["RRFS_mem000"]
  fcst_var: ["REFC"]
  fcst_thresh: [">=20"]
  fcst_lead: ["0", "010000", "020000", "030000", "040000", "050000", "060000", "070000", "080000", "090000", "100000", "110000", "120000", "130000", "140000", "150000", "160000", "170000", "180000", "190000", "200000", "210000", "220000", "230000"]
  vx_mask: ["CONUS"]

stat_vars: ["pody", "far", "csi", "fbias", "gss"]

reformat_file: false
output_reformat_file: "./filtered_output.data"

output_file: false
output_plot_file: "./vars.data"

aggregate: true
group_by: ["model", "fcst_var", "fcst_lead"]
output_agg_file: "./REFC_agg.data"

Output

Since both reformat_file and output_file are set to false, the only output in this example will be an aggregated result saved to REFC_agg.data.

You can modify the YAML to enable full data dumps as needed.