• Bachmann, S., , and Zrnić D. , 2007: Spectral density of polarimetric variables separating biological scatterers in the VAD display. J. Atmos. Oceanic Technol., 24, 11851198.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , and Chandrasekar V. , 2001: Polarimetric Doppler Weather Radar Principles and Applications. Cambridge University Press, 636 pp.

  • Doviak, R. J., , and Zrnić D. S. , 1993: Doppler Radar and Weather Observations. Academic Press, 562 pp.

  • Dufournet, Y., 2010: Ice crystal properties retrieval using radar spectral polarimetric measurements within ice/mixed-phase clouds. Ph.D. thesis, Delft University of Technology, 138 pp.

  • Hildebrand, P. H., , and Sekhon R. S. , 1974: Objective determination of the noise level in Doppler spectra. J. Appl. Meteor., 13, 808811.

    • Search Google Scholar
    • Export Citation
  • Liu, L., , Bringi V. N. , , Chandrasekar V. , , Mueller E. A. , , and Mudukutore A. , 1994: Analysis of the Doppler copolar correlation coefficient between horizontal and vertical polarizations. J. Atmos. Oceanic Technol., 11, 950963.

    • Search Google Scholar
    • Export Citation
  • Meischner, P., Ed., 2003: Weather Radar: Principles and Advanced Applications. Springer, 337 pp.

  • Melnikov, V. M., , and Zrnić D. S. , 2007: Autocorrelation and cross-correlation estimators of polarimetric variables. J. Atmos. Oceanic Technol., 24, 13371350.

    • Search Google Scholar
    • Export Citation
  • Moisseev, D. N., , and Chandrasekar V. , 2007a: Nonparametric estimation of raindrop size distributions from dual-polarization radar spectral observations. J. Atmos. Oceanic Technol., 24, 10081018.

    • Search Google Scholar
    • Export Citation
  • Moisseev, D. N., , and Chandrasekar V. , 2007b: Ice particles classification using dual-polarization spectral observations. Preprints, 33rd Int. Conf. on Radar Meteorology, Cairns, QLD, Australia, Amer. Meteor. Soc., 8A6. [Available online at http://ams.confex.com/ams/33Radar/webprogram/Paper123713.html.]

  • Moisseev, D. N., , and Chandrasekar V. , 2009: Polarimetric spectral filter for adaptive clutter and noise suppression. J. Atmos. Oceanic Technol., 26, 215228.

    • Search Google Scholar
    • Export Citation
  • Moisseev, D. N., , Chandrasekar V. , , Unal C. M. H. , , and Russchenberg H. W. J. , 2006: Dual-polarization spectral analysis for retrieval of effective raindrop shapes. J. Atmos. Oceanic Technol., 23, 16821695.

    • Search Google Scholar
    • Export Citation
  • Moisseev, D. N., , Chandrasekar V. , , and Hudak D. , 2008: Dual-polarization spectral observations of winter precipitation during C3VP; comparison to in situ observations. Proc. Fifth European Conf. on Radar in Meteorology and Hydrology, Helsinki, Finland, European Meteorological Society.

  • Moisseev, D. N., , Leskinen M. , , and Aittomaki T. , 2010: Radar signal quality improvement by spectral processing of dual-polarization radar measurements. Proc. Sixth European Conf. on Radar in Meteorology and Hydrology, Sibiu, Romania, European Meteorological Society.

  • Papoulis, A., , and Pillai S. U. , 2002: Probability, Random Variables, and Stochastic Processes. McGraw-Hill, 852 pp.

  • Reed, I. S., 1962: On a moment theory for complex Gaussian processes. IRE Trans. Inf. Theory, 8, 194195.

  • Russchenberg, H., , Spek L. , , Moisseev D. , , Unal C. , , Dufournet Y. , , and Chandrasekar V. , 2008: On the use of spectral polarimetry to observe ice cloud microphysics with radar. Precipitation: Advances in Measurement, Estimation and Prediction, S. C. Michaelides, Ed., Springer Berlin Heidelberg, 285–312.

  • Scharfenberg, K. A., and Coauthors, 2005: The joint polarization experiment: Polarimetric radar in forecasting and warning decision making. Wea. Forecasting, 20, 775788.

    • Search Google Scholar
    • Export Citation
  • Spek, A. L. J., , Unal C. M. H. , , Moisseev D. N. , , Russchenberg H. W. J. , , Chandrasekar V. , , and Dufournet Y. , 2008: New technique to categorize and retrieve the microphysical properties of ice particles above the melting layer using radar dual-polarization spectral analysis. J. Atmos. Oceanic Technol., 25, 482497.

    • Search Google Scholar
    • Export Citation
  • Stoica, P., , and Moses R. , 1997: Introduction to Spectral Analysis. Prentice Hall, 319 pp.

  • Unal, C., 2009: Spectral polarimetric radar clutter suppression to enhance atmospheric echoes. J. Atmos. Oceanic Technol., 26, 17811797.

    • Search Google Scholar
    • Export Citation
  • Unal, C., , and Moisseev D. N. , 2004: Combined Doppler and polarimetric radar measurements: Correction for spectrum aliasing and nonsimultaneous polarimetric measurements. J. Atmos. Oceanic Technol., 21, 443456.

    • Search Google Scholar
    • Export Citation
  • Unal, C., , Moisseev D. N. , , Yanovsky F. J. , , and Russchenberg H. W. J. , 2001: Radar Doppler polarimetry applied to precipitation measurements: Introduction of the spectral differential reflectivity. Preprints, 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., 316–318. [Available online at http://ams.confex.com/ams/30radar/webprogram/Paper21280.html.]

  • Yanovsky, F. J., 2002: Phenomenological models of Doppler-polarimetric microwave remote sensing of clouds and precipitation. Proc. IEEE Int. Geoscience Remote Sensing Symp., Toronto, Canada, Institute of Electrical and Electronics Engineers, 1905–1907.

  • Yanovsky, F. J., 2011: Inferring microstructure and turbulence properties in rain through observations and simulations of signal spectra measured with Doppler-polarimetric radars. Polarimetric Detection, Characterization, and Remote Sensing, NATO Science for Peace and Security Series C, Springer, 501–542.

  • Yanovsky, F. J., , Russchenberg H. W. J. , , and Ligthart L. P. , 2001: Doppler-polarimetric models of microwave remote sensing of rain. Proc. 11th Conf. on Microwave Technique (COMITE), Pardubice, the Czech Republic, Institute of Electrical and Electronics Engineers, 47–62.

  • Yanovsky, F. J., , Russchenberg H. W. J. , , and Unal C. M. H. , 2005: Retrieval of information about turbulence in rain by using Doppler-polarimetric radar. IEEE Trans. Microwave Theory Tech., 53, 444450.

    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., 1975: Simulation of weatherlike Doppler spectra and signals. J. Appl. Meteor., 14, 619620.

  • Zrnić, D. S., 1977: Spectral moment estimates from correlated pulse pairs. IEEE Trans. Aerosp. Electron. Syst., 13, 344354.

  • Zrnić, D. S., , and Ryzhkov A. , 1999: Polarimetry for weather surveillance radars. Bull. Amer. Meteor. Soc., 80, 389406.

  • Zrnić, D. S., and Coauthors, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 17531766.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 35 35 16
PDF Downloads 40 40 17

Statistical Quality of Spectral Polarimetric Variables for Weather Radar

View More View Less
  • 1 School of Electrical and Computer Engineering, and Atmospheric Radar Research Center, and School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 2 School of Electrical and Computer Engineering, and Atmospheric Radar Research Center, University of Oklahoma, Norman, Oklahoma
  • | 3 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/National Severe Storms Laboratory, Norman, Oklahoma
© Get Permissions
Restricted access

Abstract

Spectral polarimetry for weather radar capitalizes on both Doppler and polarimetric measurements to reveal polarimetric variables as a function of radial velocity through spectral analysis. For example, spectral differential reflectivity at a velocity represents the differential reflectivity from all the scatterers that have the same radial velocity of interest within the radar resolution volume. Spectral polarimetry has been applied to suppress both ground and biological clutter, retrieve individual drop size distributions from a mixture of different types of hydrometeors, and estimate turbulence intensity, for example. Although spectral polarimetry has gained increasing attention, statistical quality of the estimation of spectral polarimetric variables has not been investigated. In this work, the bias and standard deviation (SD) of spectral differential reflectivity and spectral copolar correlation coefficient estimated from averaged spectra were derived using perturbation method. The results show that the bias and SD of the two estimators depend on the spectral signal-to-noise ratio, spectral copolar correlation coefficient, the number of spectrum average, and spectral differential reflectivity. A simulation to generate time series signals for spectral polarimetry was developed and used to verify the theoretical bias and SD of the two estimators.

Corresponding author address: T.-Y. Yu, School of Electrical and Computer Engineering, University of Oklahoma, 110 W. Boyd, DEH 433, Norman, OK 73019. E-mail: tyu@ou.edu

Abstract

Spectral polarimetry for weather radar capitalizes on both Doppler and polarimetric measurements to reveal polarimetric variables as a function of radial velocity through spectral analysis. For example, spectral differential reflectivity at a velocity represents the differential reflectivity from all the scatterers that have the same radial velocity of interest within the radar resolution volume. Spectral polarimetry has been applied to suppress both ground and biological clutter, retrieve individual drop size distributions from a mixture of different types of hydrometeors, and estimate turbulence intensity, for example. Although spectral polarimetry has gained increasing attention, statistical quality of the estimation of spectral polarimetric variables has not been investigated. In this work, the bias and standard deviation (SD) of spectral differential reflectivity and spectral copolar correlation coefficient estimated from averaged spectra were derived using perturbation method. The results show that the bias and SD of the two estimators depend on the spectral signal-to-noise ratio, spectral copolar correlation coefficient, the number of spectrum average, and spectral differential reflectivity. A simulation to generate time series signals for spectral polarimetry was developed and used to verify the theoretical bias and SD of the two estimators.

Corresponding author address: T.-Y. Yu, School of Electrical and Computer Engineering, University of Oklahoma, 110 W. Boyd, DEH 433, Norman, OK 73019. E-mail: tyu@ou.edu
Save