MET Stat Use Case: Fractions Skill Score (FSS)

This use case demonstrates how to run VCasT to compute the Fractions Skill Score (FSS) using MET .stat files and a YAML configuration.

It uses a sample configuration file (NBRCNT.yaml) designed to evaluate neighborhood-based categorical skill metrics.

Prerequisites

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

Run the Example

  1. Clone the test repository:

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

  2. Run VCasT with the provided YAML file:

    vcast NBRCNT.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 NBRCNT.yaml, which configures VCasT to:

  • Use the NBRCNT line type for FSS

  • Focus on REFC forecasts

  • Filter by model, variable, threshold, lead time, and region.

  • Aggregate results by model and lead time

Sample NBRCNT.yaml configuration
 1input_stat_folder: ./stats
 2line_type: nbrcnt
 3date_column: "fcst_valid_beg"
 4start_date: "2024-05-01_00:00:00"
 5end_date: "2024-06-10_12:00:00"
 6
 7string_filters:
 8  model: ["NAMnest_mem000"]
 9  fcst_var: ["REFC"]
10  fcst_lead: ["000000", "010000", "020000", "030000", "040000", "050000", "060000", "070000", "080000", "090000",
11              "100000", "110000", "120000", "130000", "140000", "150000", "160000", "170000", "180000", "190000",
12              "200000", "210000", "220000", "230000", "240000", "250000", "260000", "270000", "280000", "290000",
13              "300000", "310000", "320000", "330000", "340000", "350000", "360000", "370000", "380000", "390000",
14              "400000", "410000", "420000", "430000", "440000", "450000", "460000", "470000", "480000", "490000",
15              "500000", "510000", "520000", "530000", "540000", "550000", "560000", "570000", "580000", "590000",
16              "600000"]
17  vx_mask: ["CONUS"]
18  fcst_thresh: [">=20", ">=30", ">=40", ">=50"]
19  interp_pnts: ["81", "361", "729", "25", "9"]
20
21stat_vars: ["fss"]
22
23reformat_file: true
24output_reformat_file: "./filtered_output.data"
25
26output_file: true
27output_plot_file: "./vars.data"
28
29aggregate: true
30group_by: ["model", "fcst_var", "fcst_lead", "fcst_thresh", "interp_pnts"]
31output_agg_file: "./agg.data"

Output

Three outputs are expected: - filtered_output.data - File containing all the information filtered from MET stat files, including all the columns associated with nbrcnt line type. - vars.data - File containing all the information filtered from MET stat files with only the specific stat_vars columns. - agg.data - File containing the aggregated result.