Preprints
https://doi.org/10.5194/amt-2024-155
https://doi.org/10.5194/amt-2024-155
18 Sep 2024
 | 18 Sep 2024
Status: this preprint is currently under review for the journal AMT.

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

Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev

Abstract. Accurate and precise KDP 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 ΦDP, 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 KDP 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 KDP to reflectivity and differential reflectivity in rain, providing a reference self-consistency KDP  (KDPSC ) for comparison. This approach allows for the construction of the reference KDP observations that can be used  to assess the accuracy and robustness of the studied KDP 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.

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Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev

Status: open (until 16 Nov 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on amt-2024-155', Kultegin Aydin, 25 Sep 2024 reply
  • RC1: 'Comment on amt-2024-155', Anonymous Referee #1, 04 Oct 2024 reply
  • RC2: 'Comment on amt-2024-155', Anonymous Referee #2, 04 Nov 2024 reply
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev

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Short summary
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as framework for the examination. Our study provides guidance in the performance, uncertainties and optimisation of the methods, focusing mainly on accuracy and robustness.