Ice Crystal Alignment: A Concept for High-Temporal-Resolution Monitoring Using Polarimetric Phased Array Radar

Mark Weber aLincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts
bNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Mark Weber in
Current site
Google Scholar
PubMed
Close
,
Dusan Zrnic bNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Dusan Zrnic in
Current site
Google Scholar
PubMed
Close
,
Pengfei Zhang bNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
cCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

Search for other papers by Pengfei Zhang in
Current site
Google Scholar
PubMed
Close
, and
Edward Mansell bNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Edward Mansell in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This article describes a concept whereby future operational polarimetric phased array radars (PPAR) routinely monitor ice crystal alignment regions caused by thundercloud electric fields with volume scan updates (∼12 min−1) sufficient to resolve the temporal variation due to lightning and subsequent rapid electric field regeneration in nonsevere thunderstorms. Routine observations of crystal alignment regions may enhance thunderstorm nowcasting through comparison of their temporal and spatial structure with other polarimetric signatures, integration with lightning detection data, and assimilation into convection resolving numerical weather prediction models. If crystal alignment observations indicate strong electrification well in advance of the first lightning strike and likewise reliably indicate the decay of strong electric fields at the end of a storm, this capability may improve warning for lightning-sensitive activities such as airport ramp operations and space launch. Experimental observations of crystal alignment volumes in central Oklahoma severe storms and their relation to those storms’ structures are presented and used to motivate discussion of possible PPAR architectures. In one case—a tornadic supercell—these observations illustrate an important limitation. Even the hypothesized 12 min−1 volume scan update rate would not resolve the temporal variation of the crystal alignment regions in such storms, suggesting that special, adaptive scanning methods may be appropriate for such storms. We describe how future operational phased array radars could support a crystal alignment measurement mode via parallel, time-multiplexed processing and discuss potential impacts on the radar’s primary weather observation mission. We conclude by discussing research needed to better understand technical challenges and operational benefits.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Weber’s current affiliation: Retired.

Corresponding author: Mark Weber, mark.edward.weber@gmail.com

Abstract

This article describes a concept whereby future operational polarimetric phased array radars (PPAR) routinely monitor ice crystal alignment regions caused by thundercloud electric fields with volume scan updates (∼12 min−1) sufficient to resolve the temporal variation due to lightning and subsequent rapid electric field regeneration in nonsevere thunderstorms. Routine observations of crystal alignment regions may enhance thunderstorm nowcasting through comparison of their temporal and spatial structure with other polarimetric signatures, integration with lightning detection data, and assimilation into convection resolving numerical weather prediction models. If crystal alignment observations indicate strong electrification well in advance of the first lightning strike and likewise reliably indicate the decay of strong electric fields at the end of a storm, this capability may improve warning for lightning-sensitive activities such as airport ramp operations and space launch. Experimental observations of crystal alignment volumes in central Oklahoma severe storms and their relation to those storms’ structures are presented and used to motivate discussion of possible PPAR architectures. In one case—a tornadic supercell—these observations illustrate an important limitation. Even the hypothesized 12 min−1 volume scan update rate would not resolve the temporal variation of the crystal alignment regions in such storms, suggesting that special, adaptive scanning methods may be appropriate for such storms. We describe how future operational phased array radars could support a crystal alignment measurement mode via parallel, time-multiplexed processing and discuss potential impacts on the radar’s primary weather observation mission. We conclude by discussing research needed to better understand technical challenges and operational benefits.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Weber’s current affiliation: Retired.

Corresponding author: Mark Weber, mark.edward.weber@gmail.com

Supplementary Materials

    • Supplemental Materials (ZIP 12.696 MB)
Save
  • Bruning, E. C., and D. R. MacGorman, 2013: Theory and observations of controls on lightning flash size spectra. J. Atmos. Sci., 70, 40124029, https://doi.org/10.1175/JAS-D-12-0289.1.

    • Search Google Scholar
    • Export Citation
  • Byrne, G. J., A. A. Few, and M. E. Weber, 1983: Altitude, thickness and charge concentration of charged regions of four thunderstorms during TRIP 1981 based upon in situ balloon electric field measurements. Geophys. Res. Lett., 10, 3942, https://doi.org/10.1029/GL010i001p00039.

    • Search Google Scholar
    • Export Citation
  • Caylor, I. J., and V. Chandrasekar, 1996: Time-varying ice crystal orientation in thunderstorms observed with multiparameter radar. IEEE Trans. Geosci. Remote Sens., 34, 847858, https://doi.org/10.1109/36.508402.

    • Search Google Scholar
    • Export Citation
  • Deierling, W., W. A. Petersen, J. Latham, S. Ellis, and H. J. Christian, 2008: The relationship between lightning activity and ice fluxes in thunderstorms. J. Geophys. Res., 113, D15210, https://doi.org/10.1029/2007JD009700.

    • Search Google Scholar
    • Export Citation
  • Emersic, C., P. L. Heinselman, D. R. MacGorman, and E. C. Bruning, 2011: Lightning activity in a hail-producing storm observed with phased-array radar. Mon. Wea. Rev., 139, 18091825, https://doi.org/10.1175/2010MWR3574.1.

    • Search Google Scholar
    • Export Citation
  • Fierro, A. O., Y. Wang, J. Gao, and E. R. Mansell, 2019: Variational assimilation of radar data and GLM lightning-derived water vapor for the short-term forecasts of high-impact convective events. Mon. Wea. Rev., 147, 40454069, https://doi.org/10.1175/MWR-D-18-0421.1.

    • Search Google Scholar
    • Export Citation
  • Fulton, C., J. Salazar, D. Zrnic, D. Mirkovic, I. Ivić, and D. Doviak, 2018: Polarimetric phased array calibration for large-scale multi-mission radar applications. 2018 IEEE Radar Conf. (RadarConf18), Oklahoma City, OK, Institute of Electrical and Electronics Engineers, 1272–1277, https://doi.org/10.1109/RADAR.2018.8378746.

  • Gatlin, P. N., and S. J. Goodman, 2010: A total lightning trending algorithm to identify severe thunderstorms. J. Atmos. Oceanic Technol., 27, 322, https://doi.org/10.1175/2009JTECHA1286.1.

    • Search Google Scholar
    • Export Citation
  • Heitkemper, L., R. F. Price, and D. B. Johnson, 2008: Lightning-warning systems for use by airports. ACRP Rep. 8, 81 pp., https://www.idc-online.com/technical_references/pdfs/electrical_engineering/acrp_rpt_008.pdf.

  • Hendry, A., and G. C. McCormick, 1976: Radar observations of the alignment of precipitation particles by electrostatic fields in thunderstorms. J. Geophys. Res., 81, 53535357, https://doi.org/10.1029/JC081i030p05353.

    • Search Google Scholar
    • Export Citation
  • Hendry, A., and Y. M. M. Antar, 1982: Radar observations of polarization characteristics and lightning-induced realignment of atmospheric ice crystals. Radio Sci., 17, 12431250, https://doi.org/10.1029/RS017i005p01243.

    • Search Google Scholar
    • Export Citation
  • Hondl, K., and M. Weber, 2019: NOAA’s meteorological phased array radar research program. 2019 IEEE Int. Symp. on Phased Array System and Technology (PAST), Waltham, MA, Institute of Electrical and Electronics Engineers, 1–6, https://doi.org/10.1109/PAST43306.2019.9020994.

  • Hubbert, J. C., S. M. Ellis, W.-Y. Chang, and Y.-C. Liou, 2014a: X-band polarimetric observations of cross coupling in the ice phase of convective storms in Taiwan. J. Appl. Meteor. Climatol., 53, 16781695, https://doi.org/10.1175/JAMC-D-13-0360.1.

    • Search Google Scholar
    • Export Citation
  • Hubbert, J. C., S. M. Ellis, W.-Y. Chang, S. Rutledge, and M. Dixon, 2014b: Modeling and interpretation of S-band ice crystal depolarization signatures from data obtained by simultaneously transmitting horizontally and vertically polarized fields. J. Appl. Meteor. Climatol., 53, 16591677, https://doi.org/10.1175/JAMC-D-13-0158.1.

    • Search Google Scholar
    • Export Citation
  • Hubbert, J. C., W. Deierling, P. Kennedy, and M. Dixon, 2015: Detection of electrification with the co-to-cross correlation coefficient with storm microphysics analysis. 37th Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 63, https://ams.confex.com/ams/37RADAR/webprogram/37RADAR.html.

  • Hubbert, J. C., J. W. Wilson, T. M. Weckwerth, S. M. Ellis, M. Dixon, and E. Loew, 2018: S-Pol’s polarimetric data reveal detailed storm features (and insect behavior). Bull. Amer. Meteor. Soc., 99, 20452060, https://doi.org/10.1175/BAMS-D-17-0317.1.

    • Search Google Scholar
    • Export Citation
  • Ivić, I. R., and R. J. Doviak, 2016: Evaluation of phase coding to mitigate differential reflectivity bias in polarimetric PAR. IEEE Trans. Geosci. Remote Sens., 54, 431451, https://doi.org/10.1109/TGRS.2015.2459047.

    • Search Google Scholar
    • Export Citation
  • Ivić, I. R., M. D. Conway, S. M. Torres, J. S. Herd, D. S. Zrnic, and M. E. Weber, 2022: Meteorological polarimetric phased array radar. Polarimetric Radar Signal Processing, A. Aubry, A. De Maio, and A. Farina, Eds., Institute of Engineering and Technology, 433–479.

  • Jones, T. A., K. Knopfmeier, D. Wheatley, G. Creager, P. Minnis, and R. Palikondo, 2016: Storm-scale data assimilation and ensemble forecasting with the NSSL experimental warn-on-forecast system. Part II: Combined radar and satellite data experiments. Wea. Forecasting, 31, 297327, https://doi.org/10.1175/WAF-D-15-0107.1.

    • Search Google Scholar
    • Export Citation
  • Krehbiel, P., T. Chen, S. McCray, W. Wilson, G. Gray, and M. Brook, 1996: The use of dual channel circular polarized radar observations for remotely sensing storm electrification. Meteor. Atmos. Phys., 59, 6582, https://doi.org/10.1007/BF01032001.

    • Search Google Scholar
    • Export Citation
  • MacGorman, D. R., 1993: Lightning in tornadic storms: A review. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 173–182, https://doi.org/10.1029/GM079p0173.

  • Marshall, T. C., and W. P. Winn, 1982: Measurements of charged precipitation in a New Mexico thunderstorm: Lower positive charge centers. J. Geophys. Res., 87, 71417157, https://doi.org/10.1029/JC087iC09p07141.

    • Search Google Scholar
    • Export Citation
  • Mazzetti, T. O., and H. E. Fuelberg, 2017: An analysis of total lightning flash rates over Florida. J. Geophys. Res. Atmos., 122, 12 81212 826, https://doi.org/10.1002/2017JD027579.

    • Search Google Scholar
    • Export Citation
  • McCormick, G. C., and A. Hendry, 1979: Radar measurement of precipitation-related depolarization in thunderstorms. IEEE Trans. Geosci. Electron., 17, 142150, https://doi.org/10.1109/TGE.1979.294641.

    • Search Google Scholar
    • Export Citation
  • Melnikov, V., D. S. Zrnić, M. E. Weber, A. O. Fierro, and D. R. MacGorman, 2019: Electrified cloud areas observed in the SHV and LDR radar modes. J. Atmos. Oceanic Technol., 36, 151159, https://doi.org/10.1175/JTECH-D-18-0022.1.

    • Search Google Scholar
    • Export Citation
  • Metcalf, J. I., 1995: Radar observations of changing orientations of hydrometeors in thunderstorms. J. Appl. Meteor., 34, 757772, https://doi.org/10.1175/1520-0450(1995)034<0757:ROOCOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Metcalf, J. I., 1997: Temporal and spatial variations of hydrometeor orientations in thunderstorms. J. Appl. Meteor., 36, 315321, https://doi.org/10.1175/1520-0450(1997)036<0315:TASVOH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • NWS, 2015: NOAA/National Weather Service radar functional requirements. NOAA Tech. Doc., 58 pp., www.roc.noaa.gov/WSR88D/PublicDocs/NOAA_Radar_Functional_Requirements_Final_Sept%202015.pdf.

  • Rutledge, S. A., E. R. Williams, and W. A. Petersen, 1993: Lightning and electrical structure of mesoscale convective systems. Atmos. Res., 29, 2753, https://doi.org/10.1016/0169-8095(93)90036-N.

    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., and D. S. Zrnić, 2007: Depolarization in ice crystals and its effect on radar polarimetric measurements. J. Atmos. Oceanic Technol., 24, 12561267, https://doi.org/10.1175/JTECH2034.1.

    • Search Google Scholar
    • Export Citation
  • Salazar, J. L., and Coauthors, 2019: An ultra-fast scan C-band Polarimetric Atmospheric Imaging Radar (PAIR). 2019 IEEE Int. Symp. on Phased Array System & Technology (PAST), Waltham, MA, Institute of Electrical and Electronics Engineers, 1–5, https://doi.org/10.1109/PAST43306.2019.9021042.

  • Schenkman, A. D., M. Xue, A. Shapiro, K. Brewster, and J. Gao, 2011: The analysis and prediction of the 8–9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR. Mon. Wea. Rev., 139, 224246, https://doi.org/10.1175/2010MWR3336.1.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., W. A. Petersen, and L. D. Carey, 2009: Preliminary development and evaluation of lightning jump algorithms for the real-time detection of severe weather. J. Appl. Meteor. Climatol., 48, 25432563, https://doi.org/10.1175/2009JAMC2237.1.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., W. A. Petersen, and L. D. Carey, 2011: Lightning and severe weather: A comparison between total and cloud-to-ground lightning trends. Wea. Forecasting, 26, 744755, https://doi.org/10.1175/WAF-D-10-05026.1.

    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., L. D. Carey, E. V. Schultz, and R. L. Blakeslee, 2015: Insight into the kinematic and microphysical processes that control lightning jumps. Wea. Forecasting, 30, 15911621, https://doi.org/10.1175/WAF-D-14-00147.1.

    • Search Google Scholar
    • Export Citation
  • Schvartzman, D., M. Weber, S. Torres, H. Thomas, D. Zrnić, and I. Ivić, 2021: Scanning concepts and architectures supporting rotating meteorological phased-array radar. 37th Conf. Environmental. Information Processing, Online, Amer. Meteor. Soc., 10.8, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/379726.

  • Stailey, J. E., and K. D. Hondl, 2016: Multifunction phased array radar for aircraft and weather surveillance. Proc. IEEE, 104, 649659, https://doi.org/10.1109/JPROC.2015.2491179.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., W. Deierling, K. Ikeda, and R. G. Bass, 2014: Ground delays from lightning ramp closures and decision uncertainties. Air Traffic Control Quart., 22, 223249, https://doi.org/10.2514/atcq.22.3.223.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and J. Gao, 2010: Importance of horizontally inhomogeneous environmental initial conditions to ensemble storm-scale radar data assimilation and very short-range forecasts. Mon. Wea. Rev., 138, 12501272, https://doi.org/10.1175/2009MWR3027.1.

    • Search Google Scholar
    • Export Citation
  • Weber, M. E., and M. L. Stone, 1995: Low altitude wind shear detection using airport surveillance radars. IEEE Aerosp. Electron. Syst., 10, 39, https://doi.org/10.1109/62.387970.

    • Search Google Scholar
    • Export Citation
  • Weber, M. E., H. J. Christian, A. A. Few, and M. F. Stewart, 1982: A thundercloud electric field sounding: Charge distribution and lightning. J. Geophys. Res., 87, 71587169, https://doi.org/10.1029/JC087iC09p07158.

    • Search Google Scholar
    • Export Citation
  • Weber, M. E., J. Y. N. Cho, J. S. Herd, J. M. Flavin, W. E. Benner, and G. S. Torok, 2007: The next-generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88, 17391752, https://doi.org/10.1175/BAMS-88-11-1739.

    • Search Google Scholar
    • Export Citation
  • Weber, M. E., J. Y. N. Cho, and H. G. Thomas, 2017: Command and control for multifunction phased array radar. IEEE Trans. Geosci. Remote Sens., 55, 58995912, https://doi.org/10.1109/TGRS.2017.2716935.

    • Search Google Scholar
    • Export Citation
  • Weber, M. E., and Coauthors, 2021: Towards the next generation operational meteorological radar. Bull. Amer. Meteor. Soc., 102, E1357E1383, https://doi.org/10.1175/BAMS-D-20-0067.1.

    • Search Google Scholar
    • Export Citation
  • Weinheimer, A. J., and A. A. Few, 1987: The electric field alignment of ice particles in thunderstorms. J. Geophys. Res., 92, 14 83314 844, https://doi.org/10.1029/JD092iD12p14833.

    • Search Google Scholar
    • Export Citation
  • Williams, E. R., M. E. Weber, and R. E. Orville, 1989: The relationship between lightning type and convective state of thunderclouds. J. Geophys. Res., 94, 13 21313 220, https://doi.org/10.1029/JD094iD11p13213.

    • Search Google Scholar
    • Export Citation
  • Williams, E. R., and Coauthors, 1999: The behavior of total lightning activity in severe Florida thunderstorms. Atmos. Res., 51, 245265, https://doi.org/10.1016/S0169-8095(99)00011-3.

    • Search Google Scholar
    • Export Citation
  • Winn, W. P., G. W. Schwede, and C. B. Moore, 1974: Measurements of electric fields in thunderclouds. J. Geophys. Res., 79, 17611767, https://doi.org/10.1029/JC079i012p01761.

    • Search Google Scholar
    • Export Citation
  • Winn, W. P., C. B. Moore, and C. R. Holmes, 1981: Electric field structure in an active part of a small, isolated thundercloud. J. Geophys. Res., 86, 1187–1193 https://doi.org/10.1029/JC086iC02p01187.

  • Yoshida, S., T. Adachi, K. Kusunoki, S. Hayashi, T. Wu, T. Ushio, and E. Yoshikawa, 2017: Relationship between thunderstorm electrification and storm kinetics revealed by phased array weather radar. J. Geophys. Res. Atmos., 122, 38213836, https://doi.org/10.1002/2016JD025947.

    • Search Google Scholar
    • Export Citation
  • Yussouf, N., J. S. Kain, and A. J. Clark, 2016: Short-term probabilistic forecasts of the 31 May 2013 Oklahoma tornado and flash flood event using a continuous-update-cycle storm-scale ensemble system. Wea. Forecasting, 31, 957983, https://doi.org/10.1175/WAF-D-15-0160.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, G., R. J. Doviak, D. S. Zrnić, R. Palmer, L. Lei, and Y. Al-Rashid, 2011: Polarimetric phased-array radar for weather measurement: A planar or cylindrical configuration? J. Atmos. Oceanic Technol., 28, 6373, https://doi.org/10.1175/2010JTECHA1470.1.

    • Search Google Scholar
    • Export Citation
  • Zrnic, D. S., and Coauthors, 2007: Agile beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 17531766, https://doi.org/10.1175/BAMS-88-11-1753.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 360 360 62
Full Text Views 129 129 7
PDF Downloads 129 129 7