Integration of Clutter Mitigation Algorithm into PYIWR Framework: A Python Toolkit for Analyzing Weather Radar Data

The Doppler Weather Radar (DWR) plays an important role in providing valuable 3D observations of precipitation systems. The advent of radar polarimetry enhances radar capabilities by providing detailed precipitation target characteristics like shape, size, etc. However, the complexities associated with radar data processing pose several challenges to its effective and widespread use. To address these challenges in radar data handling and analyzing, several open-source Python modules have been developed to facilitate radar data processing and analysis, such as WRADLIB, PYART, PYCWR, etc. One such value addition to these open-source tools is an in-house developed Python Indian weather radar toolkit (PYIWR). The toolkit incorporates standard procedures for processing and visualizing polarimetric weather radar data, making it easier for radar users to work with raw radar data, mitigating various challenges due to different data structures and formats. The present work focuses on integrating a novel ground clutter mitigation algorithm developed into the PYIWR framework. The algorithm leverages the statistical properties of long-term radar observations to identify persistent ground clutter using a probabilistic clutter map. It has been extensively tested and evaluated using long-term data from the C-band Doppler Weather Radar at the Thumba Equatorial Rocket Launching Station (TERLS) in Thiruvananthapuram, Kerala, India, spanning 2017 to 2024. Quantitative evaluation of the clutter removal ratio demonstrates that the proposed technique outperforms existing methods, like standalone Gabella filter and fuzzy logic approaches, in mitigating persistent ground clutter, especially in complex terrain. The integration of this newly developed algorithm into the PYIWR framework significantly enhances its capabilities for radar data quality control, making it a more robust and effective tool for the radar user community.