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.
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 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.
Einfalt, T., Szturc, J., and Ośródka, K.: The quality index for radar
precipitation data – a tower of Babel?, Atmos. Sci. Lett., 11, 139–144,
https://doi.org/10.1002/asl.271, 2010.
Fornasiero, A., Alberoni, P. P., Amorati, R., and Marsigli, C.: Improving
the radar data mosaicking procedure by means of a quality descriptor, 4th
European Conference on Radar in Meteorology and Hydrology, Barcelona, Spain, 18–22 September 2006, 378–381, 2006.
Gourley, J. J., Tabary, P., and Parent du Chatelet, J.: A Fuzzy logic
algorithm for the separation of precipitating from nonprecipitating echoes
using polarimetric radar observations, J. Atmos. Ocean. Tech., 24,
1439–1451, https://doi.org/10.1175/JTECH2035.1, 2007.
Hood, K., Torres, S., and Palmer, R.: Automatic detection of wind turbine
clutter for weather radars, J. Atmos. Ocean. Tech., 27, 1868–1880,
https://doi.org/10.1175/2010JTECHA1437.1, 2010.
Huuskonen, A., Saltikoff, E., and Holleman, I.: The operational weather radar network in Europe, B. Am. Meteorol. Soc., 95, 897–907, https://doi.org/10.1175/BAMS-D-12-00216.1, 2014.
Isom, B. M., Palmer, R. D., Secrest, G. S., Rhoton, R. D., Saxion, D., Allmon,
T. L., Reed, J., Crum, T., and Vogt, R.: Detailed observations of wind
turbine clutter with scanning weather radars, J. Atmos. Ocean. Tech., 26, 894–910, https://doi.org/10.1175/2008JTECHA1136.1, 2009.
Jatho, N., Pluntke, T., Kurbjuhn, C., and Bernhofer, C.: An approach to combine radar and gauge based rainfall data under consideration of their qualities in low mountain ranges of Saxony, Nat. Hazards Earth Syst. Sci., 10, 429–446, https://doi.org/10.5194/nhess-10-429-2010, 2010.
Jurczyk, A., Szturc, J., and Ośródka, K.: Convective cell
identification using multi-source data, IAHS Publications, 351, 360–366, 2012.
Jurczyk, A., Szturc, J., and Ośródka, K.: Quality-based compositing
of weather radar-derived precipitation, Meteorol. Appl., 27, e1812,
https://doi.org/10.1002/met.1812, 2020a.
Jurczyk, A., Szturc, J., Otop, I., Ośródka, K., and Struzik, P.:
Quality-based combination of multi-source precipitation data, Remote Sens.,
12, 1709, https://doi.org/10.3390/rs12111709, 2020b.
Krajewski, W. F., Vignal, B., Seo, B.-C., and Villarini, G.: Statistical
model of the range-dependent error in radar-rainfall estimates due to the
vertical profile of reflectivity, J. Hydrol., 402, 306–316,
https://doi.org/10.1016/j.jhydrol.2011.03.024, 2011.
Méri, L., Gaál, L., Bartok, J., Gažák, M., Gera, M.,
Jurašek, M., and Kelemen, M.: Improved radar composites and enhanced
value of meteorological radar data using different quality indices,
Sustainability, 13, 5285, https://doi.org/10.3390/su13095285, 2021.
Michelson, D., Einfalt, T., Holleman, I., Gjertsen, U., Friedrich, K., Haase, G., Lindskog, M., and Jurczyk, A.: Weather radar data quality in Europe – quality control and characterization, Review, COST Action 717, COST Office, Luxembourg, 87 pp., ISBN 92-898-0018-6, 2005.
Michelson, D., Henja, A., Ernes, S., Haase, G., Koistinen, J., Ośródka, K., Peltonen, T., Szewczykowski, M., and Szturc, J.: BALTRAD advanced weather radar networking, Journal of Open Research Software, 6, 12, https://doi.org/10.5334/jors.193, 2018.
Michelson, D. B., Lewandowski, R., Szewczykowski, M., Beekhuis, H.,
Haase, G., Mammen, T., and Johnson, D.: EUMETNET OPERA weather radar information model for implementation
with the HDF5 file format, Version 2.4, EUMETNET OPERA Document, 2021.
Norin, L.: A quantitative analysis of the impact of wind turbines on operational Doppler weather radar data, Atmos. Meas. Tech., 8, 593–609, https://doi.org/10.5194/amt-8-593-2015, 2015.
Norin, L. and Haase, G.: Doppler weather radars and wind turbines, in:
Doppler Radar Observations – Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications, edited by: Bech, J. and Chau, J. L., InTech, Rijeka, 333–354, ISBN 978-953-51-0496-4, 2012.
Ośródka, K. and Szturc, J.: Quality-based generation of weather radar Cartesian products, Atmos. Meas. Tech., 8, 2173–2181, https://doi.org/10.5194/amt-8-2173-2015, 2015.
Ośródka, K., Szturc, J., and Jurczyk, A.: Chain of data quality
algorithms for 3-D single-polarization radar reflectivity (RADVOL-QC
system), Meteorol. Appl., 21, 256–270, https://doi.org/10.1002/met.1323, 2014.
Park, H., Ryzhkov, A. V., Zrnic, D. S., and Kim, K.-E.: The hydrometeor
classification algorithm for the polarimetric WSR-88D: description and
application to an MCS, Weather Forecast., 24, 730–748,
https://doi.org/10.1175/2008WAF2222205.1, 2009.
Peura, M.: Computer vision methods for anomaly removal, 2nd European
Conference on Radar Meteorology (ERAD), Delft, Netherlands, Proceedings of ERAD 2002, 312–317, 2002.
Rossa, A., Haase, G., Keil, C., Alberoni, P., Ballard, S., Bech, J., Germann, U., Pfeifer, M., and Salonen, K.: Propagation of uncertainty from observing systems into NWP: COST-731 Working Group 1, Atmos. Sci. Lett., 11, 145–152, https://doi.org/10.1002/asl.274, 2010.
Saltikoff, E., Cho, J. Y., Tristant, P., Huuskonen, A., Allmon, L., Cook,
R., Becker, E., and Joe, P.: The threat to weather radars by wireless
technology, B. Am. Meteorol. Soc., 97, 1159–1167,
https://doi.org/10.1175/BAMS-D-15-00048.1, 2016.
Saltikoff, E., Haase, G., Delobbe, L., Gaussiat, N., Martet, M., Idziorek,
D., Leijnse, H., Novák, P., Lukach, M., and Stephan, K.: OPERA the Radar
Project, Atmosphere, 10, 320, https://doi.org/10.3390/atmos10060320, 2019.
Sandford, C. and Gaussiat, N.: Use of a radar quality index to mitigate the
effects of attenuation at C-band in the UK composite, in: ERAD 2012 – 7th European
Conference on Radar in Meteorology and Hydrology, Toulouse, France, 25–29 June 2012.
Seo, B.-C., Krajewski, W. F., and Mishra, K. V.: Using the new
dual-polarimetric capability of WSR-88D to eliminate anomalous propagation
and wind turbine effects in radar-rainfall, Atmos. Res., 153, 296–309, https://doi.org/10.1016/j.atmosres.2014.09.004, 2015.
Sokol, Z., Szturc, J., Orellana-Alvear, J., Popová, J., Jurczyk, A., and
Célleri, R.: The role of weather radar in rainfall estimation and its
application in meteorological and hydrological modelling – A review, Remote
Sens., 13, 351, https://doi.org/10.3390/rs13030351, 2021.
Steiner, M. and Smith, J. A.: Use of three-dimensional reflectivity
structure for automated detection and removal of nonprecipitating echoes in
radar data, J. Atmos. Ocean. Tech., 19, 673–686,
https://doi.org/10.1175/1520-0426(2002)019<0673:UOTDRS>2.0.CO;2, 2002.
Szturc, J., Ośródka, K., and Jurczyk, A.: Quality control algorithms
applied on weather radar reflectivity data, in: Doppler Radar Observations – Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications, edited by: Bech, J. and Chau, J. L., InTech, Rijeka, 289–306 ISBN 978-953-51-0496-4, 2012.
Szturc, J., Jurczyk, A., Ośródka, K., Wyszogrodzki, A., and
Giszterowicz, M.: Precipitation estimation and nowcasting at IMGW (SEiNO
system), Meteorology Hydrology and Water Management, 6, 3–12,
https://doi.org/10.26491/mhwm/76120, 2018.
Villarini, G. and Krajewski, W. F.: Review of the different sources of
uncertainty in single polarization radar-based estimates of rainfall, Surv.
Geophys., 31, 107–129, https://doi.org/10.1007/s10712-009-9079-x, 2010.
Zhang, J., Howard, K., Langston, C., Vasiloff, S., Kaney, B., Arthur, A.,
Cooten, S. V., Kelleher, K., Kitzmiller, D., Ding, F., Seo, D.-J., Wells,
E., and Dempsey, C.: National Mosaic and multi-sensor QPE (NMQ) system:
description, results, and future plans, B. Am. Meteorol. Soc., 92,
1321–1338, https://doi.org/10.1175/2011BAMS-D-11-00047.1, 2011.
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...