Evaluation of K_DP estimation algorithm performance in rain using a known-truth framework

Accurate estimation of specific differential phase (K_DP) is necessary for rain rate estimation, attenuation correction, and hydrometeor classification algorithms. There are numerous published methods to process polarimetric radar observations of propagation differential phase shift (Φ_DP) and estimate K_DP, but the corresponding K_DP estimate uncertainty is unquantified. This study provides guidance on how commonly used K_DP estimation algorithms perform in various environments. We create numerous synthetic (‘‘true’’) K_DP profiles, integrate over them to obtain ‘‘smoothed’’ Φ_DP, and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy Φ_DP profiles and compared to the true K_DP profile such that the errors and uncertainty are quantified. The synthetic K_DP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing K_DP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the Φ_DP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic K_DP profiles. Some K_DP algorithms allow users to change various tuning parameters; a subset of these tuning parameters is tested to provide guidance on how changing these parameters impacts algorithm performance. We then provide evidence that our known-truth framework provides insight into algorithm performance in observed data through two case studies.