Articles | Volume 15, issue 2
Atmos. Meas. Tech., 15, 261–277, 2022
https://doi.org/10.5194/amt-15-261-2022
Atmos. Meas. Tech., 15, 261–277, 2022
https://doi.org/10.5194/amt-15-261-2022
Research article
19 Jan 2022
Research article | 19 Jan 2022

Improvement in algorithms for quality control of weather radar data (RADVOL-QC system)

Katarzyna Ośródka and Jan Szturc

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Preprint under review for AMT
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Cited articles

Angulo, I., Grande, O., Jenn, D., Guerra, D., and de la Vega, D.: Estimating reflectivity values from wind turbines for analyzing the potential impact on weather radar services, Atmos. Meas. Tech., 8, 2183–2193, https://doi.org/10.5194/amt-8-2183-2015, 2015. 
Berenguer, M., Sempere-Torres, D., Corral, C., and Sánchez-Diezma, R.: A fuzzy logic technique for identifying nonprecipitating echoes in radar scans, J. Atmos. Ocean. Tech., 23, 1157–1180, https://doi.org/10.1175/JTECH1914.1, 2006. 
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press, Cambridge, UK, ISBN 978-0-521-62384-1, 2001. 
Bringi, V. N., Rico-Ramirez, M. A., and Thurai, M.: Rainfall estimation with an operational polarimetric C-band radar in the United Kingdom: comparison with a gauge network and error analysis, J. Hydrometeorol., 12, 935–954, https://doi.org/10.1175/JHM-D-10-05013.1, 2011. 
Cho, Y.-H., Lee, G. W., Kim, K.-E., and Zawadzki, I.: Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes, J. Atmos. Ocean. Tech., 23, 1206–1222, https://doi.org/10.1175/JTECH1913.1, 2006. 
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Short summary
Weather radar data are used in weather monitoring and forecasting, but they are affected by numerous errors and require advanced corrections. Different systems are designed and implemented to suit specific local conditions, like the RADVOL-QC system. The radar errors are divided into several groups: disturbance by non-meteorological echoes (from the mountains, RLAN signals, wind turbines, etc.), beam blockage, attenuation, etc. Each of them has different properties and is corrected differently.