• Bader, M. J., , Clough S. A. , , and Cox G. P. , 1987: Aircraft and dual polarization radar observations of hydrometeors in light stratiform precipitation. Quart. J. Roy. Meteor. Soc., 113 , 491515.

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

  • Brunkow, D. A., 1999: A new receiver and signal processor for the CSU–CHILL radar. Preprints, 29th Int. Conf. on Radar Meteorology, Montreal, QC, Canada, Amer. Meteor. Soc., 256–258.

    • Search Google Scholar
    • Export Citation
  • Brunkow, D. A., , Bringi V. N. , , Kennedy P. C. , , Rutledge S. A. , , Chandrasekar V. , , Mueller E. A. , , and Bowie R. K. , 2000: A description of the CSU–CHILL National Radar Facility. J. Atmos. Oceanic Technol., 17 , 15961608.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gordon, G. D., , and Morgan W. L. , 1993: Principles of Communications Satellites. John Wiley and Sons, 533 pp.

  • Gorgucci, E., , Scarchilli G. , , and Chandrasekar V. , 1999: A procedure to calibrate multiparameter weather radar data using the properties of the rain medium. IEEE Trans. Geosci. Remote Sens., 17 , 269276.

    • Search Google Scholar
    • Export Citation
  • Huang, G. J., , Hubbert J. C. , , and Bringi V. , 2001: Precipitation canting angle distribution estimates from covariance matix analysis of CSU–CHILL radar data. Preprints, 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., 651–653.

    • Search Google Scholar
    • Export Citation
  • Hubbert, J., , and Bringi V. N. , 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. J. Atmos. Oceanic Technol., 12 , 643648.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hubbert, J., , and Bringi V. N. , 2001: Estimation of polarization errors from covariance matrices of CSU–CHILL radar data. Preprints, 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., 45–47.

    • Search Google Scholar
    • Export Citation
  • Hubbert, J., , and Bringi V. N. , 2003: Studies of the polarimetric covariance matrix. Part II: Modeling and polarization errors. J. Atmos. Oceanic Technol., in press.

    • Search Google Scholar
    • Export Citation
  • Hubbert, J., , Bringi V. N. , , and Huang G. , 1999: Construction and interpretation of S-band covariance matrices. Preprints, 29th Int. Conf. on Radar Meteorology, Montreal, Canada, Amer. Meteor. Soc., 205–207.

    • Search Google Scholar
    • Export Citation
  • Jameson, A. R., 1985: Microphysical interpretation of multi-parameter radar measurements in rain. Part III: Interpretation and measurement of propagation differential phase shift between orthogonal linear polarizations. J. Atmos. Sci., 42 , 607614.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keeler, R. J., , Lutz J. , , and Vivekanandan J. , 2000: S-POL: NCAR’s polarimetric Doppler research radar. Preprints, IGARSS-2000, Honolulu, HI, IEEE, 1570–1573.

    • Search Google Scholar
    • Export Citation
  • Mueller, E. A., 1984: Calculation procedures for differential propagation phase shift. Preprints, 22d Conf. on Radar Meteorology, Zürich, Switzerland, Amer. Meteor. Soc., 397–399.

    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sachidananda, M., , and Zrnić D. S. , 1986: Differential propagation phase shift and rainfall rate estimation. Radio Sci., 21 , 235247.

  • Saxon, D. S., 1955: Tensor scattering matrix for the electromagnetic field. Phys. Rev., 100 , 17711775.

  • Seminario, M., , Gojara K. , , and Chandrasekar V. , 2001: Noise correction of polarimetric radar measurements. Preprints, 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., 38–40.

    • Search Google Scholar
    • Export Citation
  • Tragl, K., 1990: Polarimetric radar backscattering from reciprocal random targets. IEEE Trans. Geosci. Remote Sens., 8 , 856864.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 116 116 7
PDF Downloads 26 26 12

Studies of the Polarimetric Covariance Matrix. Part I: Calibration Methodology

View More View Less
  • 1 Colorado State University, Fort Collins, Colorado
© Get Permissions
Restricted access

Abstract

A procedure for calibration of the radar covariance matrix for the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar and S-Band Dual-Polarization Doppler Radar (S-Pol) systems is described. Two relative magnitudes and three offset phases are determined that allow for the calibrated covariance matrix to be constructed. Precise calibration of Zdr is accomplished with use of only sun calibration measurements and crosspolar power measurements from precipitation. No assumptions about the precipitation medium are made. It is also shown how to determine the co-to-cross phase offsets for the CSU–CHILL radar from precipitation data. A novel method for calculating linear depolarization ratio (LDR) that is effective in low signal-to-noise-ratio regions and that requires no knowledge of the background noise temperature is given. This technique utilizes the cross-to-cross covariances. CSU–CHILL data from the Severe Thunderstorm Electrification and Precipitation Study (STEPS) are used to illustrate the LDR estimator and the Zdr calibration technique.

Corresponding author address: Dr. J. C. Hubbert, NCAR/ATD, PO Box 3000, Boulder, CO 80307. Email: hubbert@ucar.edu

Abstract

A procedure for calibration of the radar covariance matrix for the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar and S-Band Dual-Polarization Doppler Radar (S-Pol) systems is described. Two relative magnitudes and three offset phases are determined that allow for the calibrated covariance matrix to be constructed. Precise calibration of Zdr is accomplished with use of only sun calibration measurements and crosspolar power measurements from precipitation. No assumptions about the precipitation medium are made. It is also shown how to determine the co-to-cross phase offsets for the CSU–CHILL radar from precipitation data. A novel method for calculating linear depolarization ratio (LDR) that is effective in low signal-to-noise-ratio regions and that requires no knowledge of the background noise temperature is given. This technique utilizes the cross-to-cross covariances. CSU–CHILL data from the Severe Thunderstorm Electrification and Precipitation Study (STEPS) are used to illustrate the LDR estimator and the Zdr calibration technique.

Corresponding author address: Dr. J. C. Hubbert, NCAR/ATD, PO Box 3000, Boulder, CO 80307. Email: hubbert@ucar.edu

Save