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Radar Polarimetry for Weather Observations pp 309–372Cite as

Polarimetric Classification of Radar Echo

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Abstract

Automatic classification of radar returns using the polarimetric variables and environmental conditions is presented in this chapter. General principles of classification are reviewed with emphasis on the fuzzy logic method. Then, the hydrometeor classification algorithm operational on WSR-88D network is described, and other classification algorithms are discussed. The method for melting layer detection as an important part of the most classification schemes is described in detail. A section of the chapter is devoted to detection of hail and estimation of its size together with some verification. Also presented is automated detection of tornado debris signatures in the context of tornado detection, and tracks of detections are plotted along the damage paths of several tornadoes. Automatic detection of convective updrafts is based on the columns of differential reflectivity, and examples are included. A separate section is devoted to classification specifically tailored for winter precipitation. This implies combined use of the polarimetric data and numerical weather prediction model output. Finally, classification of radar returns other than from hydrometeors is described. Specifically, polarimetric methods to identify land and sea clutter, biological scatterers, chaff, smoke plumes, dust storms, and volcanic ash are presented.

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Authors and Affiliations

  1. Cooperative Institute for Mesoscale Meteorological Studies, The University of Oklahoma, Norman, OK, USA

    Alexander V. Ryzhkov

  2. National Severe Storms Laboratory, National Oceanic and Atmospheric Administration, Norman, OK, USA

    Dusan S. Zrnic

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Ryzhkov, A.V., Zrnic, D.S. (2019). Polarimetric Classification of Radar Echo. In: Radar Polarimetry for Weather Observations. Springer Atmospheric Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-05093-1_9

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