Articles | Volume 15, issue 2
https://doi.org/10.5194/amt-15-261-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-261-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jan 2022
Research article |
| 19 Jan 2022
| 19 Jan 2022
Improvement in algorithms for quality control of weather radar data (RADVOL-QC system)
Katarzyna Ośródka
CORRESPONDING AUTHOR
Centre of Meteorological Modelling, Institute of Meteorology and Water Management, National Research Institute, 01-673 Warsaw, ul.
Podleśna 61, Poland
Centre of Meteorological Modelling, Institute of Meteorology and Water Management, National Research Institute, 01-673 Warsaw, ul.
Podleśna 61, Poland
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Cited
18 citations as recorded by crossref.
- Analysis of Two Convective Storms Using Polarimetric X-Band Radar and Satellite Data G. Bobotová et al. https://doi.org/10.3390/rs14102294
- Enhancing the accuracy of weather radar heavy rainfall estimates in mountainous regions using combined radar quality indices M. Methaprayun et al. https://doi.org/10.1016/j.jhydrol.2025.133907
- Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation M. Pasierb et al. https://doi.org/10.1002/met.2218
- Ground Clutter Filtering in Reflectivity-Only Weather Radar Remote Sensing Imagery: Event-Grouped Learning with Auxiliary Gauge Validation W. Qi et al. https://doi.org/10.3390/rs18213753
- Adaptation of RainGaugeQC algorithms for quality control of rain gauge data from professional and non-professional measurement networks K. Ośródka et al. https://doi.org/10.5194/amt-18-3229-2025
- Removal of RLAN Interference from C-Band Weather Radar Data: Algorithm and Case Studies K. Specht et al. https://doi.org/10.3390/rs18152625
- Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC) K. Ośródka et al. https://doi.org/10.5194/amt-15-5581-2022
- Can we reliably estimate precipitation with high resolution during disastrously large floods? J. Szturc et al. https://doi.org/10.5194/hess-29-5405-2025
- A Multi-Source Radar Data Complementary Enhancement Generation Method Based on Diffusion Model Y. Peng et al. https://doi.org/10.3390/rs18070992
- Giant hail in Poland produced by a supercell merger in extreme instability – A sign of a warming climate? K. Piasecki et al. https://doi.org/10.1016/j.atmosres.2023.106843
- Evaluation of radar-based precipitation estimates during a flood event using rain gauge validation K. Dzwonkowski et al. https://doi.org/10.1038/s41598-026-40456-z
- Monitoring and quantifying wind turbine clutter in DWD weather radar measurements M. Frech et al. https://doi.org/10.5194/amt-19-1711-2026
- The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection N. Mikidadi et al. https://doi.org/10.3390/rs17071176
- MW-UNet: Multi-Scale Weighted Connection UNet for Identification and Classification of Non-Meteorological Clutter over Big Radar Data M. Cui et al. https://doi.org/10.26599/BDMA.2024.9020032
- A satellite soil moisture– and radar rainfall–based methodology for slope-scale seepage–stability modelling of rainfall-induced landslides A. Jotisankasa et al. https://doi.org/10.1007/s10346-026-02732-x
- Long-term multi-source precipitation estimation with high resolution (RainGRS Clim) A. Jurczyk et al. https://doi.org/10.5194/amt-16-4067-2023
- Quality control of the composite radar quantitative precipitation estimation product for Great Britain X. Qiu et al. https://doi.org/10.1016/j.jhydrol.2025.134755
- 3D-S2RNet: Reconstructing 3-D Radar Reflectivity Based on Himawari AHI Measurements Y. Qin et al. https://doi.org/10.1109/TGRS.2026.3738780
18 citations as recorded by crossref.
- Analysis of Two Convective Storms Using Polarimetric X-Band Radar and Satellite Data G. Bobotová et al. https://doi.org/10.3390/rs14102294
- Enhancing the accuracy of weather radar heavy rainfall estimates in mountainous regions using combined radar quality indices M. Methaprayun et al. https://doi.org/10.1016/j.jhydrol.2025.133907
- Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation M. Pasierb et al. https://doi.org/10.1002/met.2218
- Ground Clutter Filtering in Reflectivity-Only Weather Radar Remote Sensing Imagery: Event-Grouped Learning with Auxiliary Gauge Validation W. Qi et al. https://doi.org/10.3390/rs18213753
- Adaptation of RainGaugeQC algorithms for quality control of rain gauge data from professional and non-professional measurement networks K. Ośródka et al. https://doi.org/10.5194/amt-18-3229-2025
- Removal of RLAN Interference from C-Band Weather Radar Data: Algorithm and Case Studies K. Specht et al. https://doi.org/10.3390/rs18152625
- Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC) K. Ośródka et al. https://doi.org/10.5194/amt-15-5581-2022
- Can we reliably estimate precipitation with high resolution during disastrously large floods? J. Szturc et al. https://doi.org/10.5194/hess-29-5405-2025
- A Multi-Source Radar Data Complementary Enhancement Generation Method Based on Diffusion Model Y. Peng et al. https://doi.org/10.3390/rs18070992
- Giant hail in Poland produced by a supercell merger in extreme instability – A sign of a warming climate? K. Piasecki et al. https://doi.org/10.1016/j.atmosres.2023.106843
- Evaluation of radar-based precipitation estimates during a flood event using rain gauge validation K. Dzwonkowski et al. https://doi.org/10.1038/s41598-026-40456-z
- Monitoring and quantifying wind turbine clutter in DWD weather radar measurements M. Frech et al. https://doi.org/10.5194/amt-19-1711-2026
- The Application of the Convective–Stratiform Classification Algorithm for Feature Detection in Polarimetric Radar Variables and QPE Retrieval During Warm-Season Convection N. Mikidadi et al. https://doi.org/10.3390/rs17071176
- MW-UNet: Multi-Scale Weighted Connection UNet for Identification and Classification of Non-Meteorological Clutter over Big Radar Data M. Cui et al. https://doi.org/10.26599/BDMA.2024.9020032
- A satellite soil moisture– and radar rainfall–based methodology for slope-scale seepage–stability modelling of rainfall-induced landslides A. Jotisankasa et al. https://doi.org/10.1007/s10346-026-02732-x
- Long-term multi-source precipitation estimation with high resolution (RainGRS Clim) A. Jurczyk et al. https://doi.org/10.5194/amt-16-4067-2023
- Quality control of the composite radar quantitative precipitation estimation product for Great Britain X. Qiu et al. https://doi.org/10.1016/j.jhydrol.2025.134755
- 3D-S2RNet: Reconstructing 3-D Radar Reflectivity Based on Himawari AHI Measurements Y. Qin et al. https://doi.org/10.1109/TGRS.2026.3738780
Saved (final revised paper)
Latest update: 12 Nov 2026
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.
Weather radar data are used in weather monitoring and forecasting, but they are affected by...
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