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Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations

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Publicado en:Atmospheric Measurement Techniques vol. 18, no. 3 (2025), p. 793
Autor principal: Aldana, Miguel
Otros Autores: Pulkkinen, Seppo, Annakaisa von Lerber, Kumjian, Matthew R, Moisseev, Dmitri
Publicado:
Copernicus GmbH
Materias:
Vantaa Finland
Finland
Datasets
Performance evaluation
Radar
Radar data
Estimates
C band
Atmospheric radiation
Precipitation
Meteorological radar
Python
Downward long wave radiation
Quality control
Noise reduction
Weather
Radiation measurement
Accuracy
Reflectance
Classification
Atmospheric radiation measurements
Algorithms
Methods
Errors
Rainfall
Calibration
Weather radar
Precipitation estimation
Data quality control
Parameter estimation
Variables
Estimation errors
Rain
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Accurate and precise <inline-formula>KDP</inline-formula> 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 <inline-formula>ΦDP</inline-formula>, 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 <inline-formula>KDP</inline-formula> estimation methods implemented in open-source libraries such as Py-ART (the Python ARM (atmospheric radiation measurement) Radar Toolkit) and <inline-formula>ω</inline-formula>radlib and the method used in the Vaisala weather radars. To benchmark these methods, we employ a polarimetric self-consistency approach that relates <inline-formula>KDP</inline-formula> to reflectivity and differential reflectivity in rain, providing a reference self-consistent <inline-formula>KDP</inline-formula> (<inline-formula>KDPsc</inline-formula>) for comparison. This approach allows for the construction of the reference <inline-formula>KDP</inline-formula> observations that can be used to assess the accuracy and robustness of the studied <inline-formula>KDP</inline-formula> 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 were able to define optimized parameter settings for the methods that have user-configurable parameters. Most of these methods showed a significant reduction in the estimation errors after the optimization, with respect to the default settings. We have found significant differences in the performance of the studied methods, where the best-performing methods showed smaller normalized biases in the high reflectivity values (i.e., <inline-formula>≥</inline-formula> 40 dBZ) and overall smaller normalized root-mean-square errors across the range of reflectivity values.
ISSN:1867-1381
1867-8548
DOI:10.5194/amt-18-793-2025
Fuente:Advanced Technologies & Aerospace Database