Jensen–Shannon Distance-Based Filter and Unsupervised Evaluation Metrics for Polarimetric Weather Radar Processing

Abstract

An effective filtering technique is required for rainfall rate measurement by weather radar. A Jensen–Shannon distance (JSD)-based thresholding filter is proposed to mitigate nonmeteorological signals, either in clear air or rain situations. This algorithm classifies range-Doppler bins into two classes, hydrometeors and nonhydrometeors, based on spectral polarimetric variable features. The result is a mask to be applied on the spectrograms. The variable selected here is the spectral co-polar correlation coefficient, available in dual-polarization and full polarimetric radars. The algorithm first does global thresholding by finding an optimized threshold value based on the averaged clear-air spectral polarimetric variable distribution. Next, classical filtering steps are carried out like a ground clutter notch filter around 0 ms−1, a mathematical morphology to fill gaps in the hydrometeor areas, and a removal of narrow Doppler power spectra. The second part of this article is the assessment of filtering techniques without ground truth. An assessment without ground truth is useful to select optimal algorithm configurations from a large solution space. Criteria of good filtering are defined both in the spectral and time domain. Based on those criteria, subjective and objective unsupervised evaluation metrics are derived, with a focus on the objective ones. Data, including clear air and rain collected from a full polarimetric Doppler X-band radar in the urban area, are used. With the proposed unsupervised evaluation metrics, the JSD-based thresholding filter is compared to two spectral polarimetric filters. Overall, the JSD-based filter performs very well considering both the subjective and the objective evaluation metrics.