A Probabilistic Radar Forward Model for Branched Planar Ice Crystals

Robert S. Schrom Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

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Matthew R. Kumjian Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

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Abstract

Polarimetric radar measurements provide information about ice particle growth and offer the potential to evaluate and better constrain ice microphysical models. To achieve these goals, one must map the ice particle physical properties (e.g., those predicted by a microphysical model) to electromagnetic scattering properties using a radar forward model. Simplified methods of calculating these scattering properties using homogeneous, reduced-density spheroids produce large errors in the polarimetric radar measurements, particularly for low-aspect-ratio branched planar crystals. To overcome these errors, an empirical method is introduced to more faithfully represent branched planar crystal scattering using scattering calculations for a large number of detailed shapes. Additionally, estimates of the uncertainty in the scattering properties, owing to ambiguity in the crystal shape given a set of bulk physical properties, are also incorporated in the forward model. To demonstrate the utility of the forward model developed herein, the radar variables are simulated from microphysical model output for an Arctic cloud case. The simulated radar variables from the empirical forward model are more consistent with the observations compared to those from the homogeneous, reduced-density-spheroid model, and have relatively low uncertainty.

Current affiliation: NASA Goddard Space Flight Center, Greenbelt, and Universities Space Research Association, Columbia, Maryland.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Robert S. Schrom, rss5116@psu.edu

Abstract

Polarimetric radar measurements provide information about ice particle growth and offer the potential to evaluate and better constrain ice microphysical models. To achieve these goals, one must map the ice particle physical properties (e.g., those predicted by a microphysical model) to electromagnetic scattering properties using a radar forward model. Simplified methods of calculating these scattering properties using homogeneous, reduced-density spheroids produce large errors in the polarimetric radar measurements, particularly for low-aspect-ratio branched planar crystals. To overcome these errors, an empirical method is introduced to more faithfully represent branched planar crystal scattering using scattering calculations for a large number of detailed shapes. Additionally, estimates of the uncertainty in the scattering properties, owing to ambiguity in the crystal shape given a set of bulk physical properties, are also incorporated in the forward model. To demonstrate the utility of the forward model developed herein, the radar variables are simulated from microphysical model output for an Arctic cloud case. The simulated radar variables from the empirical forward model are more consistent with the observations compared to those from the homogeneous, reduced-density-spheroid model, and have relatively low uncertainty.

Current affiliation: NASA Goddard Space Flight Center, Greenbelt, and Universities Space Research Association, Columbia, Maryland.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Robert S. Schrom, rss5116@psu.edu
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  • Andrić, J., M. R. Kumjian, D. S. Zrnić, J. M. Straka, and V. M. Melnikov, 2013: Polarimetric signatures above the melting layer in winter storms: An observational and modeling study. J. Appl. Meteor. Climatol., 52, 682700, https://doi.org/10.1175/JAMC-D-12-028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Auer, A. H., and D. L. Veal, 1970: The dimension of ice crystals in natural clouds. J. Atmos. Sci., 27, 919926, https://doi.org/10.1175/1520-0469(1970)027<0919:TDOICI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bailey, M. P., and J. Hallett, 2009: A comprehensive habit diagram for atmospheric ice crystals: Confirmation from the laboratory, AIRS II, and other field studies. J. Atmos. Sci., 66, 28882899, https://doi.org/10.1175/2009JAS2883.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bohren, C. F., and L. J. Battan, 1982: Radar backscattering of microwaves by spongy ice spheres. J. Atmos. Sci., 39, 26232628, https://doi.org/10.1175/1520-0469(1982)039<2623:RBOMBS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bohren, C. F., and D. R. Huffman, 1983: Absorption and Scattering of Light by Small Particles. 1st ed. John Wiley and Sons, 530 pp.

  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar. 1st ed. Cambridge University Press, 636 pp.

    • Crossref
    • Export Citation
  • de Boer, G., and Coauthors, 2018: A bird’s eye view: Development of an operational ARM unmanned aerial capability for atmospheric research in Arctic Alaska. Bull. Amer. Meteor. Soc., 99, 11971212, https://doi.org/10.1175/BAMS-D-17-0156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draine, B. T., and P. J. Flatau, 1994: Discrete-dipole approximation for scattering calculations. J. Opt. Soc. Amer., 11A, 14911499, https://doi.org/10.1364/JOSAA.11.001491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffin, E. M., T. J. Schuur, and A. V. Ryzhkov, 2018: A polarimetric analysis of ice microphysical processes in snow, using quasi-vertical profiles. J. Appl. Meteor. Climatol., 57, 3150, https://doi.org/10.1175/JAMC-D-17-0033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gultepe, I., G. Isaac, D. Hudak, R. Nissen, and J. W. Strapp, 2000: Dynamical and microphysical characteristics of Arctic clouds during BASE. J. Climate, 13, 12251254, https://doi.org/10.1175/1520-0442(2000)013<1225:DAMCOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrington, J. Y., K. Sulia, and H. Morrison, 2013: A method for adaptive habit prediction in bulk microphysical models. Part I: Theoretical development. J. Atmos. Sci., 70, 349364, https://doi.org/10.1175/JAS-D-12-040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hashino, T., and G. J. Tripoli, 2007: The Spectral Ice Habit Prediction System (SHIPS). Part I: Model description and simulation of the vapor deposition process. J. Atmos. Sci., 64, 22102237, https://doi.org/10.1175/JAS3963.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Helmus, J. J., and S. M. Collis, 2016: The Python ARM radar toolkit (Py-ART), a library for working with weather radar data in the Python programming language. J. Open Res. Software, 4, 25, https://doi.org/10.5334/jors.119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, J. D., 1975: Classical Electrodynamics. 2nd ed. John Wiley and Sons, 848 pp.

  • Jensen, A. A., and J. Y. Harrington, 2015: Modeling ice crystal aspect ratio evolution during riming: A single-particle growth model. J. Atmos. Sci., 72, 25692590, https://doi.org/10.1175/JAS-D-14-0297.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, A. A., J. Y. Harrington, H. Morrison, and J. A. Milbrandt, 2017: Predicting ice shape evolution in a bulk microphysics model. J. Atmos. Sci., 74, 20182104, https://doi.org/10.1175/JAS-D-16-0350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalesse, H., and Coauthors, 2016: Understanding rapid changes in phase partitioning between cloud liquid and ice in stratiform mixed-phase clouds: An Arctic case study. Mon. Wea. Rev., 144, 48054826, https://doi.org/10.1175/MWR-D-16-0155.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kennedy, P. C., and S. A. Rutledge, 2011: S-band dual-polarization radar observations of winter storms. J. Appl. Meteor. Climatol., 50, 844858, https://doi.org/10.1175/2010JAMC2558.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and K. A. Lombardo, 2017: Insights into the evolving microphysical and kinematic structure of northeastern U.S. winter storms from dual-polarization Doppler radar. Mon. Wea. Rev., 145, 10331061, https://doi.org/10.1175/MWR-D-15-0451.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, K., and Coauthors, 2016: The microwave radiative properties of falling snow derived from nonspherical ice particle models. Part I: An extensive database of simulated pristine crystals and aggregate particles, and their scattering properties. J. Appl. Meteor. Climatol., 55, 691708, https://doi.org/10.1175/JAMC-D-15-0130.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., B. A. Baker, and C. G. Schmitt, 2001: An overview of microphysical properties of Arctic clouds observed in May and July 1998 during FIRE ACE. J. Geophys. Res., 106, 14 98915 014, https://doi.org/10.1029/2000JD900789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leinonen, J., and Coauthors, 2018: Retrieval of snowflake microphysical properties from multifrequency radar observations. Atmos. Meas. Tech., 11, 54715488, https://doi.org/10.5194/amt-11-5471-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Libbrecht, K., 2005: The physics of snow crystals. Rep. Prog. Phys., 68, 855895, https://doi.org/10.1088/0034-4885/68/4/R03.

  • Lu, Y., Z. Jiang, K. Aydin, J. Verlinde, E. E. Clothiaux, and G. Botta, 2016: A polarimetric scattering database for non-spherical ice particles at microwave wavelengths. Atmos. Meas. Tech., 9, 51195134, https://doi.org/10.5194/amt-9-5119-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., R. F. Reinking, and I. V. Djalalova, 2005: Inferring fall attitudes of pristine dendritic crystals from polarimetric radar data. J. Atmos. Sci., 62, 241250, https://doi.org/10.1175/JAS-3356.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., C. G. Schmitt, M. Maahn, and G. de Boer, 2017: Atmospheric ice particle shape estimates from polarimetric radar measurements and in situ observations. J. Atmos. Oceanic Technol., 34, 25692587, https://doi.org/10.1175/JTECH-D-17-0111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and J. A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci., 72, 287311, https://doi.org/10.1175/JAS-D-14-0065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. de Boer, G. Feingold, J. Harrington, M. D. Shupe, and K. Sulia, 2012: Resilience of persistent Arctic mixed-phase clouds. Nat. Geosci., 5, 1117, https://doi.org/10.1038/ngeo1332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mugnai, A., and W. J. Wiscombe, 1980: Scattering of radiation by moderately nonspherical particles. J. Atmos. Sci., 37, 12911307, https://doi.org/10.1175/1520-0469(1980)037<1291:SORBMN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nettesheim, J. J., and P. K. Wang, 2018: A numerical study on the aerodynamics of freely falling planar ice crystals. J. Atmos. Sci., 75, 28492865, https://doi.org/10.1175/JAS-D-18-0041.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oue, M., M. Galletti, J. Verlinde, A. Ryzhkov, and Y. Lu, 2016: Use of X-band differential reflectivity measurements to study shallow Arctic mixed-phase clouds. J. Appl. Meteor. Climatol., 55, 403424, https://doi.org/10.1175/JAMC-D-15-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oue, M., P. Kollias, A. Ryzhkov, and E. P. Luke, 2018: Toward exploring the synergy between cloud radar polarimetry and Doppler spectral analysis in deep cold precipitating systems in the arctic. J. Geophys. Res., 123, 27972815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., 2001: Interpretation of polarimetric radar covariance matrix for meteorological scatterers: Theoretical analysis. J. Atmos. Oceanic Technol., 18, 315328, https://doi.org/10.1175/1520-0426(2001)018<0315:IOPRCM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., M. Pinsky, A. Pokrovsky, and A. Khain, 2011: Polarimetric radar observation operator for a cloud model with spectral microphysics. J. Appl. Meteor. Climatol., 50, 873894, https://doi.org/10.1175/2010JAMC2363.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schrom, R. S., 2018: Radar measurements and simulations of ice crystal growth in Arctic mixed-phase clouds. Ph.D. dissertation, The Pennsylvania State University, 181 pp.

  • Schrom, R. S., and M. R. Kumjian, 2018: Bulk-density representations of branched planar ice crystals: Errors in the polarimetric radar variables. J. Appl. Meteor. Climatol., 57, 333346, https://doi.org/10.1175/JAMC-D-17-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schrom, R. S., M. R. Kumjian, and Y. Lu, 2015: Polarimetric radar observations of dendritic growth zones in Colorado winter storms. J. Appl. Meteor. Climatol., 54, 23652388, https://doi.org/10.1175/JAMC-D-15-0004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheridan, L. M., J. Y. Harrington, D. Lamb, and K. Sulia, 2009: Influence of ice crystal aspect ratio on the evolution of ice size spectral during vapor depositional growth. J. Atmos. Sci., 66, 37323743, https://doi.org/10.1175/2009JAS3113.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sulia, K. J., and M. R. Kumjian, 2017: Simulated polarimetric fields of ice vapor growth using the adaptive habit model. Part I: Large-eddy simulations. Mon. Wea. Rev., 145, 22812302, https://doi.org/10.1175/MWR-D-16-0061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van de Hulst, H. C., 1981: Light Scattering by Small Particles. 1st ed. Dover, 481 pp.

  • Waterman, P. C., 1969: Scattering by dielectric obstacles. Alta Freq., (Speciale), 348–352.

  • Westbrook, C. D., 2014: Notes and correspondence Rayleigh scattering by hexagonal ice crystals and the interpretation of dual-polarisation radar measurements. Quart. J. Roy. Meteor. Soc., 140, 20902096, https://doi.org/10.1002/qj.2262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, Y., and B. Gustafson, 2001: A generalized multiparticle Mie-solution: Further experimental verification. J. Quant. Spectrosc. Radiat. Transfer, 70, 395419, https://doi.org/10.1016/S0022-4073(01)00019-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yurkin, M. A., and A. G. Hoekstra, 2011: The discrete-dipole-approximation code ADDA: Capabilities and known limitations. J. Quant. Spectrosc. Radiat. Transfer, 112, 22342247, https://doi.org/10.1016/j.jqsrt.2011.01.031.

    • Crossref
    • Search Google Scholar
    • Export Citation
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