Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4425-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-4425-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jun 2021
Research article |
| 16 Jun 2021
| 16 Jun 2021
Identifying insects, clouds, and precipitation using vertically pointing polarimetric radar Doppler velocity spectra
Christopher R. Williams
CORRESPONDING AUTHOR
Ann and H. J. Smead Aerospace Engineering Sciences Department,
University of Colorado, Boulder, CO, 80309, United States
Karen L. Johnson
Brookhaven National Laboratory, Upton, NY, 11973, United States
Scott E. Giangrande
Brookhaven National Laboratory, Upton, NY, 11973, United States
Joseph C. Hardin
Pacific Northwest National Laboratory, Richland, WA, 99354, United
States
Ruşen Öktem
Department of Earth and Planetary Science, University of California, Berkeley, CA, 94720, United States
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, United States
David M. Romps
Department of Earth and Planetary Science, University of California, Berkeley, CA, 94720, United States
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, United States
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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.
Atmospheric Radiation Measurement (ARM) user facility: Clouds Optically Gridded by Stereo (COGS) product (COGSPI), updated hourly, 2018-01-01 to
2019-12-31, Southern Great Plains (SGP) Southern Great Plains Network (N1),
compiled by: Gaustad, K., ARM Data Center,
https://doi.org/10.5439/1644322, 2017.
Baldini, L. and Gorcucci, E.: Identification of the melting layer through
dual-polarization radar measurements at vertical incidence, J. Atmos. Ocean. Technol., 23,
829-839, 2006.
Bauer-Pfundstein, M. R. and Görsdorf, U.: Target separation and
classification using cloud radar Doppler-spectra. 33rd Int. Conf. on Radar
Meteorology, 6–10 August 2007, Cairns, QLD, Australia, Amer. Meteor. Soc., 11B.2, available
at: https://ams.confex.com/ams/33Radar/techprogram/paper_123456.htm (last access: 26 March 2021), 2007.
Carter, D. A., Gage, K. S., Ecklund, W. L., Angevine, W. M., Johnston, P.
E., Riddle, A. C., Wilson, J. S., and Williams, C. R.: Developments in UHF
lower tropospheric wind profiling at NOAA's Aeronomy Laboratory, Radio
Sci., 30, 977–1001, 1995.
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Del Genio, A. D., Déqué, M., Dymnikov, V., Galin, V., Gates, W. L., Ghan, S. J., Kiehl, J. T., Lacis, A. A., Le Treut, H., Li, Z.-X., Liang, X.-Z., McAvaney, B. J., Meleshko, V. P., Mitchell, J. F. B., Morcrette, J.-J., Randall, D. A., Rikus, L., Roeckner, E., Royer, J. F., Schlese, U., Sheinin, D. A., Slingo, A., Sokolov, A. P., Taylor, K. E., Washington, W. M., Wetherald, R. T., Yagai, I., and Zhang, M.-H.: Intercomparison and interpretation of climate
feedback processes in 19 atmospheric general circulation models, J. Geophys.
Res., 95, 16601–16615, https://doi.org/10.1029/JD095iD10p16601, 1990.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R. T., Miller, M. A., and Martner, B. E.: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites, J. Appl. Meteor. Clim., 39, 645–665, https://doi.org/10.1175/1520-0450(2000)039<0645:ODOCHA>2.0.CO;2, 2000.
Drake, V. A., Hatty, S., Symons, C., and Wang, H.: Insect monitoring radar:
Maximizing performance and utility, Remote Sens., 12, 596,
https://doi.org/10.3390/rs12040596, 2020.
Görsdorf, U., Lehmann, V., Bauer-Pfundstein, M., Peters, G., Vavriv, D.,
Vinogradov, V., and Volkov, V.: A 35-GHz polarimetric Doppler radar for long-term
observations of cloud parameters – Description of system and data
processing, J. Atmos. Ocean. Tecnhol., 32, 675–690, 2015.
Gustafson, W. I., Vogelmann, A. M., Cheng, X., Endo, S., Krishna, B., Li, Z., Toto, T., and Xiao, H.: Recommendations for implementation of the LASSO workflow, edited by: Stafford, R., DOE Atmospheric Radiation Measurement Climate Research Facility.
DOE/SC-ARM-17-031, https://doi.org/10.2172/1406259, 2017.
Hildebrand, P. H. and Sekhon, R.: Objective determination of the noise level
in Doppler spectra, J. Appl. Meteorol., 13, 808–811,
https://doi.org/10.1175/1520-0450(1974)013<0808:ODOTNL>2.0.CO;2, 1974.
Khandwalla, A., Sekelsky, S., Li, L., and Bergada, M.: Theory and observations between Ka-band and W-band to explain scattering differences between insects, Proc. 11th ARM Science Team Meeting, 19–23 March 2001, Atlanta, GA, USA, DOE ARM, available at: http://www.arm.gov/publications/proceedings/conf11/extended_abs/khandwalla_a.pdf (last access: 11 January 2021), 2001.
Khandwalla, A., Majurec, N., Sekelsky, S. M., Williams, C. R., and Gage, K. S.:
Characterization of radar boundary layer data collected during the 2001
multi-frequency radar IOP. Proc. 12th ARM Science Team Meeting, 8–12 April 2002, St.
Petersburg, FL, USA, ARM, available at:
http://www.arm.gov/publications/proceedings/conf12/extended_abs/khandwalla-a.pdf (last access: 11 January 2021), 2002.
Klingle, D. L., Smith, D. R., and Wolfson, M. M.: Gust front characteristics
as detected by Doppler radar, Mon. Weather Rev., 115, 905–918, 1987.
Kollias, P., Albrecht, B. A., and Marks Jr., F.: Why Mie? Accurate
observations of vertical air velocities and raindrops using a cloud radar,
Bull. Amer. Meteor. Soc., 83, 1471–1484, https://doi.org/10.1175/BAMS-83-10-1471,
2002.
Kollias, P., Clothiaux, E. E., Ackerman, T. P., Albrecht, B. A., Widener, K. B., Moran, K. P., Luke, E. P., Johnson, K. L., Bharadwaj, N., Mead, J. B.,
Miller, M. A., Verlinde, J., Marchand, R. T., and Mace, G. G.: Development and applications of ARM millimeter-wavelength cloud radars, Meteor. Monogr., 57, 17.1–17.19, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0037.1,
2016.
LASSO: Large Eddy Simulation (LES) ARM Symbiotic Simulation and Observation
(LASSO) bundle browser – Visualization & access, available at:
https://adc.arm.gov/lassobrowser, last access: 1 May 2020.
Lhermitte, R. M.: Probing air motion by Doppler analysis of radar clear air
returns, J. Atmos. Sci., 23, 575–591,
https://doi.org/10.1175/1520-0469(1966)023<0575:PAMBDA>2.0.CO;2, 1966.
Lohmeier, S. P., Sekelsky, S. M., Firda, J. M., Sadowy, G. A., and
McIntosh, R. E.: Classification of particles in stratiform clouds using the 33 and 95 GHz polarimetric Cloud Profiling Radar System, IEEE Trans. Geosci. Remote Sens., 35, 256–270, 1997.
Luke, E. P., Kollias, P., Johnson, K. L., and Clothiaux, E. E.: A Technique for the
automatic detection of insect clutter in cloud radar returns, J. Atmos.
Ocean. Technol., 25, 1498–1513, https://doi.org/10.1175/2007JTECHA953.1, 2008.
Martner, B. E. and Moran, K. P.: Using cloud radar polarization measurements
to evaluate stratus cloud and insect echoes, J. Geophys. Res., 106,
4891–4897, https://doi.org/10.1029/2000JD900623, 2001.
Mather, J. H. and Voyles, J. W.: The ARM Climate Research Facility: A
review of structure and capabilities, Bull. Amer. Meteor. Soc., 94, 377–392,
https://doi.org/10.1175/BAMS-D-11-00218.1, 2013.
Matrosov, S. Y.: Theoretical study of radar polarization parameters obtained
from cirrus clouds, J. Atmos. Sci., 48, 1062–1070, https://doi.org/10.1175/1520-0469(1991)048<1062:TSORPP>2.0.CO;2, 1991.
Moran, K. P., Martner, B. E., Post, M. J., Kropfli, R. A., Welsh, D. C., and Widener, K.
B.: An unattended cloud-profiling radar for use in climate research,
Bull. Amer. Meteor. Soc., 79, 443–455,
https://doi.org/10.1175/1520-0477(1998)079<0443:AUCPRF>2.0.CO;2, 1998.
Morris, V.: Ceilmeter Instrument Handbook. DOE Atmospheric Radiation
Mission (ARM), report DOE/SE-ARM-TR-020, available at:
https://www.arm.gov/publications/tech_reports/handbooks/ceil_handbook.pdf (last access: 27 March 2021), 2016.
Mueller, E. A. and Larkin, R. P.: Insects observed using dual polarization
radar, J. Atmos. Oceanic Technol., 2, 49–54, https://doi.org/10.1175/1520-0426(1985)002<0049:IOUDPR>2.0.CO;2, 1985.
Nansen, C. and Elliott, N.: Remote sensing and reflectance profiling in
Entomology, Ann. Rev. Entomol., 61, 139–158,
https://doi.org/10.1146/annurev-ento-010715-023834, 2016.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R.,
Ahmad, E., and Hartmann, D.: Cloud radiative forcing and climate: Results from
the Earth Radiation Budget Experiment, Science, 243, 57–63,
https://doi.org/10.1126/science.243.4887.57, 1989.
Reinking, R. F., Matrosov, S. Y., Bruintjes, R. T., and Martner, B. E.:
Identification of hydrometeors with elliptical and linear polarization
Ka-band radar, J. Appl. Meteor., 36, 322–339, https://doi.org/10.1175/1520-0450(1997)036<0322:IOHWEA>2.0.CO;2, 1997.
Riley, J. R.: Remote sensing in entomology, Ann. Rev. Entomol., 34, 247–271,
1989.
Romps, D. M. and Öktem, R.: Observing clouds in 4D with multiview
stereophotogrammetry, Bull. Amer. Meteor. Soc., 99, 2575–2586,
https://doi.org/10.1175/BAMS-D-18-0029.1, 2018.
Sekelsky, S. M., Li, L., Calloway, J., McIntosh, R. E., Miller, M. A.,
Clothiaux, E. E., Haimov, S., Mace, G. C., and Sassen, K.: Comparison of
millimeter-wave cloud radar measurements for the fall 1997 cloud IOP, in:
Proceedings of 8th ARM Science Team Meeting, 23–27 March 1998, Tucson, Ariz., USA, Dep. of Energy, 671–675, 1998.
Westbrook, J. K., Eyster, R. S., and Wolf, W. W.: WSR-88D Doppler radar
detection of corn earworm moth migration, Int. J. Biometeorol., 58, 931–940, https://doi.org/10.1007/s00484-013-0676-5, 2014.
Widener, K., Bharadwaj, N., and Johnson, K.: Ka-Band ARM Zenith Radar (KAZR)
Handbook. DOE Atmospheric Radiation Mission (ARM), report DOE/SC-ARM/TR-106,
available at:
https://www.arm.gov/publications/tech_reports/handbooks/kazr_handbook.pdf (last access: 27 March 2021), 2012.
Williams, C. R.: KAZRMASKS: KAZR hydrometeor and insect masks, DOE
Atmospheric Radiation Measurement (ARM) Archive [data set],
https://doi.org/10.5439/1772490, 2021.
Williams, C. R. and Gage, K. S.: Raindrop size distribution variability estimated using ensemble statistics, Ann. Geophys., 27, 555–567, https://doi.org/10.5194/angeo-27-555-2009, 2009.
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.
In addition to detecting clouds, vertically pointing cloud radars detect individual insects...
