Benchmarking KDP in Rainfall: A Quantitative Assessment of Estimation Algorithms Using C-Band Weather Radar Observations

Abstract

Abstract. Accurate and precise K_DP estimates are essential for radar-based applications, especially in quantitative precipitation estimation and radar data quality control routines. The accuracy of these estimates largely depends on the post-processing of the radar's measured Φ_DP, which aims to reduce noise and backscattering effects while preserving fine-scale precipitation features. In this study, we evaluate the performance of several publicly available K_DP estimation methods implemented in open-source libraries such as PyArt and Wradlib, and the method used in the Vaisala weather radars. To benchmark these methods, we employ a polarimetric self-consistency approach that relates K_DP to reflectivity and differential reflectivity in rain, providing a reference self-consistency K_DP  (K_DP^SC ) for comparison. This approach allows for the construction of the reference K_DP observations that can be used to assess the accuracy and robustness of the studied K_DP estimation methods. We assess each method by quantifying uncertainties using C-band weather radar observations where the reflectivity values ranged between 20 and 50 dBZ. Using the proposed evaluation framework we could define optimized parameter settings for the methods that have user-configurable parameters. Most of such methods showed significant reduction in the estimation errors after the optimization with respect to the default settings. We have found significant differences in the performances of the studied methods, where the best performing methods showed smaller normalized biases in the high reflectivity values (i.e., ≥ 40 dBZ) and overall smaller normalized root mean squared errors across the range of reflectivity values.