Articles | Volume 14, issue 6
Atmos. Meas. Tech., 14, 4425–4444, 2021
https://doi.org/10.5194/amt-14-4425-2021
Atmos. Meas. Tech., 14, 4425–4444, 2021
https://doi.org/10.5194/amt-14-4425-2021

Research article 16 Jun 2021

Research article | 16 Jun 2021

Identifying insects, clouds, and precipitation using vertically pointing polarimetric radar Doppler velocity spectra

Christopher R. Williams et al.

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Cited articles

Ackerman, T. P. and Stokes, G. M.: The Atmospheric Radiation Measurement Program, Phys. Today, 56, 38–44, https://doi.org/10.1063/1.1554135, 2003. 
Atmospheric Radiation Measurement (ARM) user facility: Ceilometer (CEIL10M), updated hourly, 2018-01-01 to 2019-12-31, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), compiled by: Morris, V., Zhang, D., and Ermold, B., ARM Data Center [data set], https://doi.org/10.5439/1181954, 2010. 
Atmospheric Radiation Measurement (ARM) user facility: Ka ARM Zenith Radar (KAZRSPECCMASKMDCOPOL), updated hourly, 2018-01-01 to 2019-12-31, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), compiled by: Lindenmaier, I., Bharadwaj, N., Nelson, D., Isom, B., Hardin, J., Matthews, A., Wendler, T., and Castro, V., ARM Data Center [data set], https://doi.org/10.5439/1095603, 2011a. 
Atmospheric Radiation Measurement (ARM) user facility: Ka ARM Zenith Radar (KAZRSPECCMASKMDXPOL), updated hourly, 2018-01-01 to 2019-12-31, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), compiled by: Lindenmaier, I., Bharadwaj, N., Nelson, D., Isom, B., Hardin, J., Matthews, A., Wendler, T., and Castro, V., ARM Data Center [data set], https://doi.org/10.5439/1095604, 2011b. 
Atmospheric Radiation Measurement (ARM) user facility: Active Remote Sensing of CLouds (ARSCL) product using Ka-band ARM Zenith Radars (ARSCLKAZR1KOLLIAS), updated hourly, 2018-01-01 to 2019-12-31, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), compiled by: Johnson, K. and Scott, T., ARM Data Center, https://doi.org/10.5439/1393437, 2014. 
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Short summary
In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.