Editorial Type:
Article
Article Type:
Research Article

An Automated Technique to Quality Control Radar Reflectivity Data

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

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Angela Fritz Valparaiso University, Valparaiso, Indiana

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Travis Smith Cooperative Institute of Mesoscale Meteorological Studies, University of Oklahoma, and National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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Kurt Hondl National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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Gregory Stumpf Cooperative Institute of Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma, and National Weather Service/Meteorological Development Laboratory, Silver Spring, Maryland

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Print Publication:
01 Mar 2007
DOI:
https://doi.org/10.1175/JAM2460.1
Page(s):
288–305
Restricted access

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Abstract

Echoes in radar reflectivity data do not always correspond to precipitating particles. Echoes on radar may result from biological targets such as insects, birds, or wind-borne particles; from anomalous propagation or ground clutter; or from test and interference patterns that inadvertently seep into the final products. Although weather forecasters can usually identify and account for the presence of such contamination, automated weather-radar algorithms are drastically affected. Several horizontal and vertical features have been proposed to discriminate between precipitation echoes and echoes that do not correspond to precipitation. None of these features by themselves can discriminate between precipitating and nonprecipitating areas. In this paper, a neural network is used to combine the individual features, some of which have already been proposed in the literature and some of which are introduced in this paper, into a single discriminator that can distinguish between “good” and “bad” echoes (i.e., precipitation and nonprecipitation, respectively). The method of computing the horizontal features leads to statistical anomalies in their distributions near the edges of echoes. Also described is how to avoid presenting such range gates to the neural network. The gate-by-gate discrimination provided by the neural network is followed by more holistic postprocessing based on spatial contiguity constraints and object identification to yield quality-controlled radar reflectivity scans that have most of the bad echo removed while leaving most of the good echo untouched. A possible multisensor extension, utilizing satellite data and surface observations, to the radar-only technique is also demonstrated. It is demonstrated that the resulting technique is highly skilled and that its skill exceeds that of the currently operational algorithm.

Corresponding author address: V. Lakshmanan, NSSL/RRDD, 120 David L. Boren Blvd., Norman, OK 73071. Email: lakshman@ou.edu

Abstract

Echoes in radar reflectivity data do not always correspond to precipitating particles. Echoes on radar may result from biological targets such as insects, birds, or wind-borne particles; from anomalous propagation or ground clutter; or from test and interference patterns that inadvertently seep into the final products. Although weather forecasters can usually identify and account for the presence of such contamination, automated weather-radar algorithms are drastically affected. Several horizontal and vertical features have been proposed to discriminate between precipitation echoes and echoes that do not correspond to precipitation. None of these features by themselves can discriminate between precipitating and nonprecipitating areas. In this paper, a neural network is used to combine the individual features, some of which have already been proposed in the literature and some of which are introduced in this paper, into a single discriminator that can distinguish between “good” and “bad” echoes (i.e., precipitation and nonprecipitation, respectively). The method of computing the horizontal features leads to statistical anomalies in their distributions near the edges of echoes. Also described is how to avoid presenting such range gates to the neural network. The gate-by-gate discrimination provided by the neural network is followed by more holistic postprocessing based on spatial contiguity constraints and object identification to yield quality-controlled radar reflectivity scans that have most of the bad echo removed while leaving most of the good echo untouched. A possible multisensor extension, utilizing satellite data and surface observations, to the radar-only technique is also demonstrated. It is demonstrated that the resulting technique is highly skilled and that its skill exceeds that of the currently operational algorithm.

Corresponding author address: V. Lakshmanan, NSSL/RRDD, 120 David L. Boren Blvd., Norman, OK 73071. Email: lakshman@ou.edu

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Journal of Applied Meteorology and Climatology

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