Detection of Nonprecipitating Clouds with the WSR-88D: A Theoretical and Experimental Survey of Capabilities and Limitations

Mark A. Miller Department of Applied Science, Brookhaven National Laboratory, Upton, New York

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Johannes Verlinde Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Craig V. Gilbert Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Gregory J. Lehenbauer Department of Physics and Astronomy, University of Kansas, Lawrence, Kansas

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Jeffrey S. Tongue National Weather Service Weather Forecast Office, Upton, New York

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Eugene E. Clothiaux Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Abstract

Theoretical calculations and experiments verify that the National Weather Service WSR-88D radars have the sensitivity to detect nonprecipitating clouds, but show that significant obstacles impair the generality of this cloud sensing technique. Bragg scatter from refractive index inhomogeneities can be of the same magnitude as cloud echoes under many conditions, whereupon interpretation of WSR-88D echoes can be either complicated or impossible. Moreover, problems with echoes from ground clutter and from insects, birds, and other floating debris hinder WSR-88D cloud detection capabilities, particularly at low elevation angles. To illustrate these problems WSR-88D reflectivities collected using volume coverage pattern (VCP) 21 from the months of March and October 1996 were compared with collocated reflectivities obtained from a zenith-pointing 94-GHz cloud radar located in central Pennsylvania. Coincident echo detection occurred 82% and 39% of the time, while the WSR-88D had significant detections 7% and 44% more of the time, during the March and October periods, respectively. For the coincident detections, there were a number of discrepancies in the reflectivity values that were a function of season, height, and time of day. In a separate analysis the WSR-88D located in Upton, Long Island, New York, was used to validate the theoretical minimum detectable signal of the radar in VCP 31 and show that ground clutter contamination may remain despite the use of clutter suppression techniques.

In summary, the results of this study suggest that WSR-88D reflectivity data may contain useful information about cloud structure when cloud droplets are the dominant scatterers. To determine if they are dominant requires information about the turbulent structure of the measurement volume and a method to discriminate undesirable targets such as ground clutter, insects, and birds. These requirements suggest that remotely sensing nonprecipitating and lightly precipitating clouds with the WSR-88D would be a difficult process to automate. Extrapolation of the results of this study imply that the best time to remotely sense low clouds with the WSR-88D may be winter nights and mornings when 1) clouds and wind prevent the development of a strong surface-based inversion that can produce beam ducting that enhances ground clutter, 2) insects and birds are not present, and 3) moisture gradients in the lower troposphere are relatively weak, which may reduce the likelihood that Bragg scatter from turbulent eddies would dominate Rayleigh scatter from cloud droplets.

Corresponding author address: Dr. Mark A. Miller, Department of Applied Science, Brookhaven National Laboratory, P.O. Box 5000, Upton, NY 11973-5000.

Abstract

Theoretical calculations and experiments verify that the National Weather Service WSR-88D radars have the sensitivity to detect nonprecipitating clouds, but show that significant obstacles impair the generality of this cloud sensing technique. Bragg scatter from refractive index inhomogeneities can be of the same magnitude as cloud echoes under many conditions, whereupon interpretation of WSR-88D echoes can be either complicated or impossible. Moreover, problems with echoes from ground clutter and from insects, birds, and other floating debris hinder WSR-88D cloud detection capabilities, particularly at low elevation angles. To illustrate these problems WSR-88D reflectivities collected using volume coverage pattern (VCP) 21 from the months of March and October 1996 were compared with collocated reflectivities obtained from a zenith-pointing 94-GHz cloud radar located in central Pennsylvania. Coincident echo detection occurred 82% and 39% of the time, while the WSR-88D had significant detections 7% and 44% more of the time, during the March and October periods, respectively. For the coincident detections, there were a number of discrepancies in the reflectivity values that were a function of season, height, and time of day. In a separate analysis the WSR-88D located in Upton, Long Island, New York, was used to validate the theoretical minimum detectable signal of the radar in VCP 31 and show that ground clutter contamination may remain despite the use of clutter suppression techniques.

In summary, the results of this study suggest that WSR-88D reflectivity data may contain useful information about cloud structure when cloud droplets are the dominant scatterers. To determine if they are dominant requires information about the turbulent structure of the measurement volume and a method to discriminate undesirable targets such as ground clutter, insects, and birds. These requirements suggest that remotely sensing nonprecipitating and lightly precipitating clouds with the WSR-88D would be a difficult process to automate. Extrapolation of the results of this study imply that the best time to remotely sense low clouds with the WSR-88D may be winter nights and mornings when 1) clouds and wind prevent the development of a strong surface-based inversion that can produce beam ducting that enhances ground clutter, 2) insects and birds are not present, and 3) moisture gradients in the lower troposphere are relatively weak, which may reduce the likelihood that Bragg scatter from turbulent eddies would dominate Rayleigh scatter from cloud droplets.

Corresponding author address: Dr. Mark A. Miller, Department of Applied Science, Brookhaven National Laboratory, P.O. Box 5000, Upton, NY 11973-5000.

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