Editorial Type:
Article
Article Type:
Research Article

A Technique to Censor Biological Echoes in Radar Reflectivity Data

Valliappa Lakshmanan Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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Jian Zhang National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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Kenneth Howard National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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Print Publication:
01 Mar 2010
DOI:
https://doi.org/10.1175/2009JAMC2255.1
Page(s):
453–462
Restricted access

Abstract

Existing techniques of quality control of radar reflectivity data rely on local texture and vertical profiles to discriminate between precipitating echoes and nonprecipitating echoes. Nonprecipitating echoes may be due to artifacts such as anomalous propagation, ground clutter, electronic interference, sun strobe, and biological contaminants (i.e., birds, bats, and insects). The local texture of reflectivity fields suffices to remove most artifacts, except for biological echoes. Biological echoes, also called “bloom” echoes because of their circular shape and expanding size during the nighttime, have proven difficult to remove, especially in peak migration seasons of various biological species, because they can have local and vertical characteristics that are similar to those of stratiform rain or snow. In this paper, a technique is described that identifies candidate bloom echoes based on the range variance of reflectivity in areas of bloom and uses the global, rather than local, characteristic of the echo to discriminate between bloom and rain. Every range gate is assigned a probability that it corresponds to bloom using morphological (shape based) operations, and a neural network is trained using this probability as one of the input features. It is demonstrated that this technique is capable of identifying and removing echoes due to biological targets and other types of artifacts while retaining echoes that correspond to precipitation.

Corresponding author address: V. Lakshmanan, 120 David L. Boren Blvd., Norman, OK 73072. Email: lakshman@ou.edu

Abstract

Existing techniques of quality control of radar reflectivity data rely on local texture and vertical profiles to discriminate between precipitating echoes and nonprecipitating echoes. Nonprecipitating echoes may be due to artifacts such as anomalous propagation, ground clutter, electronic interference, sun strobe, and biological contaminants (i.e., birds, bats, and insects). The local texture of reflectivity fields suffices to remove most artifacts, except for biological echoes. Biological echoes, also called “bloom” echoes because of their circular shape and expanding size during the nighttime, have proven difficult to remove, especially in peak migration seasons of various biological species, because they can have local and vertical characteristics that are similar to those of stratiform rain or snow. In this paper, a technique is described that identifies candidate bloom echoes based on the range variance of reflectivity in areas of bloom and uses the global, rather than local, characteristic of the echo to discriminate between bloom and rain. Every range gate is assigned a probability that it corresponds to bloom using morphological (shape based) operations, and a neural network is trained using this probability as one of the input features. It is demonstrated that this technique is capable of identifying and removing echoes due to biological targets and other types of artifacts while retaining echoes that correspond to precipitation.

Corresponding author address: V. Lakshmanan, 120 David L. Boren Blvd., Norman, OK 73072. Email: lakshman@ou.edu

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Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

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