Evaluation of KDP Estimation Algorithm Performance in Rain Using a Known-Truth Framework
Abstract
Accurate estimation of specific differential phase (KDP) 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 (ΦDP) and estimate KDP, but the corresponding KDP estimate uncertainty is unquantified. This study provides guidance on how commonly used KDP estimation algorithms perform in various environments. We create numerous synthetic (“true”) KDP profiles, integrate over them to obtain “smoothed” ΦDP, and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy ΦDP profiles and compared to the true KDP profile such that the errors and uncertainty are quantified. The synthetic KDP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing KDP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the ΦDP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic KDP profiles. Some KDP 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.
Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JTECH-D-20-0060.s1.
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