Radar Quality Index Processing

This repository contains the full processing scripts developed for the Quality Index (Q) applied to weather radar reflectivity data from the XRAD C‑band radar network of the Servei Meteorològic de Catalunya (SMC). The software computes gate‑level quality indicators, transforms them into Cartesian radar products, and generates network‑wide composites suitable for operational meteorology.

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Repository Structure

The repository structure is as follows:

QI_radar/
├── MAIN.py                         # Main script orchestrating the pipeline
├── Import_config.py                # Loads and parses the configuration file
├── FindIRISFiles.py                # Search and locate IRIS radar data files
├── Polar2Cartesian_PPI.py          # Polar to Cartesian conversion of PPI radar data
├── Composite_tools.py              # Compositing radar data from multiple radars
├── CAPPI_LUE_tools.py              # Tools for generating CAPPI and LUE products
├── AutoPlot.py                     # Plotting a PNG image file of each product generated
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├── config_template.txt             # Configuration file with processing parameters
├── HIST_TOP12.nc                   # Climatological echo tops data file
├── README.md                       # This documentation file
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└── visualization/                  # Directory for manually visualization outputs

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Data Format

▶ Input Files

The pipeline requires the following input data to be configured in a new configuration file named config.txt (take example from the config_template.txt):

• Raw Radar Data Files: IRIS format (.RAW) files containing polar radar reflectivity data from the XRAD C-band radar network. Examples of such files can be found in the following repository: https://github.com/meteocat/QI_radar_data.

• Auxiliary Data:

  • DEM files (GeoTIFF format) for terrain correction, specified in config.txt.
  • Climatological echo tops data (NetCDF format) for quality index computation (HIST_TOP12.nc).

• Configuration File: config.txt - A text file specifying processing parameters, including:

  • Initial and final UTC times for processing (note that the final time is not processed).
  • Volume scan type (VOLA, VOLB, or VOLBC). Choose according to the desired products.
  • Product type to compute: Lowest Usable Elevation (LUE) and/or Constant Altitude Plan Position Indicator (CAPPI).
  • Composite types to compute (MAXZ, MAXQI and/or MAXQI_dev). Each type has a different radar priorization methodology where more than one radar detects reflectivity.
  • CAPPI height in meters.
  • Cartesian grid resolution in meters. Note that modifying this parameter will significantly affect processing time.
  • Raw data directory path.
  • Processed netCDF directory path (where output products are saved).
  • Processed PNG directory path (where output PNG images are saved).
  • Paths to Digital Elevation Model (DEM) files for short-range and long-range processing.
  • Temporal storage directory name for individual radar PPI fields.
  • Path to echo tops 12dBZ climatology file.

▶ Output Files

The pipeline generates the following composite products as NetCDF (.nc) files organized by volume type, product type, composite type, and date:

• CAPPI (Constant Altitude Plan Position Indicator): Cartesian reflectivity fields at a specified height, with MAX-Z (maximum reflectivity) and MAX-QI (maximum quality index) composites.
• LUE (Lowest Usable Elevation): Products from the lowest usable elevation angles, with MAX-Z and MAX-QI composites.

Each output file contains variables:

• Z: Reflectivity (dBZ)
• QI: Quality Index (dimensionless, 0-1)
• RAD: Radar identifier chosen by the composite criteria (integer)
• ELEV: Elevation angle used (degrees)

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Visualization

Visualization output may be generated "manually" using the plotComposite.py script following this command:

• If only wanting to visualise: python plotComposite.py [product_file_path.nc]
• If wanting to save PNG: python plotComposite.py s [product_file_path.nc] [saving_directory]
• If wanting to save multiple PNGs: python plotComposite.py m [product_files_directory] [saving_directory]