Python implementation of the Koistinen & Pohjola VPR (Vertical Profile Reflectivity) correction algorithm for weather radar data. This modernization of a circa-2003 Perl codebase is designed for use in Apache Airflow workflows at FMI.
📖 Introduction · Quick Start · Configuration
- Ground clutter removal – Gradient-based filtering of low-altitude echoes
- Spike smoothing – Boundary correction and isolated echo removal
- Profile classification – Automatic layer segmentation (Precipitation, Altostratus, Clear Air Echo, Clutter)
- Bright band detection – Melting layer identification using gradient analysis
- VPR correction – Range-dependent correction factors for CAPPI products
- TOML configuration – Flexible radar metadata management with environment variable support
Requires Python 3.12+.
# From source (development)
git clone https://github.com/fmidev/vprc.git
cd vprc
pip install -e .
# Or directly from GitHub
pip install git+https://github.com/fmidev/vprc.gitsrc/vprc/ # Package implementation
tests/ # Test suite (see tests/README.md)
docs/ # Documentation
pytest tests/See tests/README.md for details on test structure and coverage.
Contributions are welcome through Github.
Koistinen, J., and H. Pohjola, 2014: Estimation of Ground-Level Reflectivity Factor in Operational Weather Radar Networks Using VPR-Based Correction Ensembles. J. Appl. Meteor. Climatol., 53, 2394–2411, https://doi.org/10.1175/JAMC-D-13-0343.1.