Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5515-2022
https://doi.org/10.5194/amt-15-5515-2022
Research article
 | 
28 Sep 2022
Research article |  | 28 Sep 2022

Image muting of mixed precipitation to improve identification of regions of heavy snow in radar data

Laura M. Tomkins, Sandra E. Yuter, Matthew A. Miller, and Luke R. Allen

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Cited articles

Alku, L., Moisseev, D., Aittomäki, T., and Chandrasekar, V.: Identification and Suppression of Nonmeteorological Echoes Using Spectral Polarimetric Processing, IEEE T. Geosci. Remote, 53, 3628–3638, https://doi.org/10.1109/TGRS.2014.2380476, 2015. a
Ansari, S., Del Greco, S., Kearns, E., Brown, O., Wilkins, S., Ramamurthy, M., Weber, J., May, R., Sundwall, J., Layton, J., Gold, A., Pasch, A., and Lakshmanan, V.: Unlocking the Potential of NEXRAD Data through NOAA's Big Data Partnership, B. Am. Meteorol. Soc., 99, 189–204, https://doi.org/10.1175/BAMS-D-16-0021.1, 2018. a
Battan, L. J.: Radar observation of the atmosphere, University of Chicago Press, Chicago, ISBN-10: 1878907271, ISBN-13: 978-1878907271, 1973. a
Bringi, V. and Chandrasekar, V.: Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press, ISBN-13: 978-0521623841, ISBN-10: 0521623847, 2001. a, b
Calvo, L., Christel, I., Terrado, M., Cucchietti, F., and Pérez-Montoro, M.: Users' Cognitive Load: A Key Aspect to Successfully Communicate Visual Climate Information, B. Am. Meteorol. Soc., 103, E1–E16, 2021. a
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Short summary
Locally higher radar reflectivity values in winter storms can mean more snowfall or a transition from snow to mixtures of snow, partially melted snow, and/or rain. We use the correlation coefficient to de-emphasize regions of mixed precipitation. Visual muting is valuable for analyzing and monitoring evolving weather conditions during winter storm events.
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