• Aydin, K., , Seliga T. A. , , and Balaji V. , 1986: Remote sensing of hail with a dual linear polarization radar. J. Appl. Meteor., 25, 14751484, doi:10.1175/1520-0450(1986)025<1475:RSOHWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., , Kessinger C. J. , , Tuttle J. D. , , and Vivekanandan J. , 1993: An evaluation of multiparameter radar measurements for detecting hail. Preprints, 26th Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 522–524.

  • Bringi, V. N., , Chandrasekar V. , , Hubbert J. , , Gorgucci E. , , Randeu W. L. , , and Schoenhuber M. , 2003: Raindrop size distribution in different climatic regimes from disdrometer and dual-polarized radar analysis. J. Atmos. Sci., 60, 354365, doi:10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , Thurai M. , , Nakagawa K. , , Huang G.-J. , , Kobayashi T. , , Adachi A. , , Hanado H. , , and Sekizawa S. , 2006: Rainfall estimation from C-band polarimetric radar in Okinawa, Japan: Comparisons with 2D-video disdrometer and 400 MHz wind profiler. J. Meteor. Soc. Japan, 84, 705724, doi:10.2151/jmsj.84.705.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , Williams C. R. , , Thurai M. , , and May P. T. , 2009: Using dual-polarized radar and dual-frequency profiler for DSD characterization: A case study from Darwin, Australia. J. Atmos. Oceanic Technol., 26, 21072122, doi:10.1175/2009JTECHA1258.1.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , Rico-Ramirez M. A. , , and Thurai M. , 2011: Rainfall estimation with an operational polarimetric C-band radar in the United Kingdom: Comparison with a gauge network and error analysis. J. Hydrometeor., 12, 935954, doi:10.1175/JHM-D-10-05013.1.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , Huang G.-J. , , Munchak S. J. , , Kummerow C. D. , , Marks D. A. , , and Wolff D. B. , 2012: Comparison of drop size distribution parameter (D0) and rain rate from S-band dual-polarized ground radar, TRMM Precipitation Radar (PR), and combined PR–TMI: Two events from Kwajalein Atoll. J. Atmos. Oceanic Technol., 29, 16031616, doi:10.1175/JTECH-D-11-00153.1.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., , Thurai M. , , Petersen W. A. , , and Gatlin P. N. , 2013a: Using a network of 2D video disdrometers for external radar calibration of NASA’s S-band polarimetric radar. 36th Conf. on Radar Meteorology, Breckenridge, CO, Amer. Meteor. Soc., 10.3. [Available online at https://ams.confex.com/ams/36Radar/webprogram/Paper228161.html.]

  • Bringi, V. N., , Tolstoy L. , , Thurai M. , , and Petersen W. A. , 2013b: Estimation of spatial correlation of rain drop size distribution parameters and rain rates using NASA’s S-band polarimetric radar and 2D video disdrometer network: Two case studies from MC3E. 36th Conf. on Radar Meteorology, Breckenridge, CO, 240. [Available online at https://ams.confex.com/ams/36Radar/webprogram/Paper228159.html.]

  • Chandrasekar, V., , Bringi V. N. , , Rutledge S. A. , , Hou A. , , Smith E. , , Skofronick Jackson G. , , Gorgucci E. , , and Petersen W. A. , 2008: Potential role of dual-polarization radar in the validation of satellite precipitation measurements: Rationale and opportunities. Bull. Amer. Meteor. Soc., 89, 11271145, doi:10.1175/2008BAMS2177.1.

    • Search Google Scholar
    • Export Citation
  • Ciach, G. J., , and Krajewski W. F. , 2006: Analysis and modeling of spatial correlation structure of small-scale rainfall in central Oklahoma. Adv. Water Resour., 29, 14501463, doi:10.1016/j.advwatres.2005.11.003.

    • Search Google Scholar
    • Export Citation
  • Depue, T., , Kennedy P. , , and Rutledge S. , 2007: Performance of the hail differential reflectivity (HDR) polarimetric hail indicator. J. Appl. Meteor. Climatol., 46, 12901301, doi:10.1175/JAM2529.1.

    • Search Google Scholar
    • Export Citation
  • Gebremichael, M., , and Krajewski W. F. , 2004: Assessment of the statistical characterization of small-scale rainfall variability from radar: Analysis of TRMM ground validation datasets. J. Appl. Meteor., 43, 11801199, doi:10.1175/1520-0450(2004)043<1180:AOTSCO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Habib, E., , and Krajewski W. F. , 2002: Uncertainty analysis of the TRMM ground-validation radar–rainfall products: Application to the TEFLUN-B field campaign. J. Appl. Meteor., 41, 558572, doi:10.1175/1520-0450(2002)041<0558:UAOTTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and et al. , 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation
  • Huang, G-J., , Bringi V. N. , , and Thurai M. , 2008: Orientation angle distributions of drops after an 80-m fall using a 2D video disdrometer. J. Atmos. Oceanic Technol., 25, 17171723, doi:10.1175/2008JTECHA1075.1.

    • 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, doi:10.1175/1520-0426(1995)012<0643:AIFTFT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huff, F. A., 1970: Spatial and temporal correlation of precipitation in Illinois. ISWS/CIR-141/79, Illinois Institute of Natural Resources, 14 pp.

  • Illingworth, A. J., , and Blackman T. M. , 2002: The need to represent raindrop size spectra as normalized gamma distributions for the interpretation of polarimetric radar observations. J. Appl. Meteor., 41, 286297, doi:10.1175/1520-0450(2002)041<0286:TNTRRS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jaffrain, J., , and Berne A. , 2012: Influence of the subgrid variability of the raindrop size distribution on radar rainfall estimators. J. Appl. Meteor. Climatol., 51, 780785, doi:10.1175/JAMC-D-11-0185.1.

    • Search Google Scholar
    • Export Citation
  • Kozu, T., , and Iguchi T. , 1999: Nonuniform beamfilling correction for spaceborne radar rainfall measurement: Implications from TOGA COARE radar data analysis. J. Atmos. Oceanic Technol., 16, 17221735, doi:10.1175/1520-0426(1999)016<1722:NBCFSR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., , Ciach G. J. , , and Habib E. , 2003: An analysis of small-scale rainfall variability in different climatic regimes. Hydrol. Sci., 48, 151162, doi:10.1623/hysj.48.2.151.44694.

    • Search Google Scholar
    • Export Citation
  • Lee, C. K., , Lee G. W. , , Zawadzki I. , , and Kim K.-E. , 2009: A preliminary analysis of spatial variability of raindrop size distributions during stratiform rain events. J. Appl. Meteor. Climatol., 48, 270283, doi:10.1175/2008JAMC1877.1.

    • Search Google Scholar
    • Export Citation
  • Löffler-Mang, M., , and Joss J. , 2000: An optical disdrometer for measuring size and velocity of hydrometeors. J. Atmos. Oceanic Technol., 17, 130139, doi:10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Meneghini, R., , Kim H. , , Liao L. , , and Jones J. , 2013: Dual-frequency spaceborne retrieval methods and the role of non-uniform beam filling. 36th Conf. on Radar Meteorology, Breckenridge, CO, Amer. Meteor. Soc., 9A.4. [Available online at https://ams.confex.com/ams/36Radar/webprogram/Paper228626.html.]

  • Moore, R. J., , Jones D. A. , , Cox D. R. , , and Isham V. S. , 2000: Design of the HYREX raingauge network. Hydrol. Earth Syst. Sci., 4, 521530, doi:10.5194/hess-4-521-2000.

    • Search Google Scholar
    • Export Citation
  • Moreau, E., , Testud J. , , and Le Bouar E. , 2009: Rainfall spatial variability observed by X-band weather radar and its implication for the accuracy of rainfall estimates. Adv. Water Resour., 32, 10111019, doi:10.1016/j.advwatres.2008.11.007.

    • Search Google Scholar
    • Export Citation
  • Petersen, W., , and Jensen M. , 2012: The NASA-GPM and DOE-ARM Midlatitude Continental Convective Clouds Experiment (MC3E). The Earth Observer, Vol. 24, Issue 1, Earth Observing System Project Science Office, NASA GSFC, Greenbelt, MD, 1218. [Available online at http://eospso.gsfc.nasa.gov/sites/default/files/eo_pdfs/Jan_Feb_2012_col_508.pdf.]

  • Ryzhkov, A. V., , Giangrande S. E. , , and Schuur T. J. , 2005: Rainfall estimation with a polarimetric prototype of WSR-88D. J. Appl. Meteor., 44, 502515, doi:10.1175/JAM2213.1.

    • Search Google Scholar
    • Export Citation
  • Schleiss, M., , and Berne A. , 2012: Stochastic space–time disaggregation of rainfall into DSD fields. J. Hydrometeor., 13, 19541969, doi:10.1175/JHM-D-12-013.1.

    • Search Google Scholar
    • Export Citation
  • Schönhuber, M., , Lammer G. , , and Randeu W. L. , 2008: The 2D-video-distrometer. Precipitation: Advances in Measurement, Estimation and Prediction, S. Michaelides, Ed., Springer, 3–31, doi:10.1007/978-3-540-77655-0_1.

  • Short, D. A., , and Iguchi T. , 2011: Model simulations of non-uniform beam filling correction for spaceborne precipitation radar. Preprints, 2011 IEEE Int. Geoscience and Remote Sensing Symp., Vancouver, BC, Canada, IEEE, 2594–2597, doi:10.1109/IGARSS.2011.6049772.

  • Tapiador, F. J., , Checa R. , , and de Castro M. , 2010: An experiment to measure the spatial variability of rain drop size distribution using sixteen laser disdrometers. Geophys. Res. Lett., 37, L16803, doi:10.1029/2010GL044120.

    • Search Google Scholar
    • Export Citation
  • Thurai, M., , and Bringi V. N. , 2008: Rain microstructure from polarimetric radar and advanced disdrometers. Precipitation: Advances in Measurement, Estimation and Prediction, S. Michaelides, Ed., Springer, 233–284, doi:10.1007/978-3-540-77655-0_10.

  • Thurai, M., , Huang G.-J. , , Bringi V. N. , , Randeu W. L. , , and Schönhuber M. , 2007: Drop shapes, model comparisons, and calculations of polarimetric radar parameters in rain. J. Atmos. Oceanic Technol., 24, 10191032, doi:10.1175/JTECH2051.1.

    • Search Google Scholar
    • Export Citation
  • Thurai, M., , Bringi V. N. , , Carey L. D. , , Gatlin P. , , Schultz E. , , and Petersen W. A. , 2012: Estimating the accuracy of polarimetric radar–based retrievals of drop-size distribution parameters and rain rate: An application of error variance separation using radar-derived spatial correlations. J. Hydrometeor., 13, 10661079, doi:10.1175/JHM-D-11-070.1.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., , and Bashor P. G. , 2010: An experimental study of small-scale variability of raindrop size distribution. J. Appl. Meteor. Climatol., 49, 23482365, doi:10.1175/2010JAMC2269.1.

    • Search Google Scholar
    • Export Citation
  • Villarini, G., , Mandapaka P. V. , , Krajewski W. F. , , and Moore R. J. , 2008: Rainfall and sampling uncertainties: A rain gauge perspective. J. Geophys. Res., 113, D11102, doi:10.1029/2007JD009214.

    • Search Google Scholar
    • Export Citation
  • Williams, C. R., , and Gage K. S. , 2009: Raindrop size distribution variability estimated using ensemble statistics. Ann. Geophys., 27, 555567, doi:10.5194/angeo-27-555-2009.

    • Search Google Scholar
    • Export Citation
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Estimation of Spatial Correlation of Drop Size Distribution Parameters and Rain Rate Using NASA’s S-Band Polarimetric Radar and 2D Video Disdrometer Network: Two Case Studies from MC3E

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  • 1 Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, Colorado
  • | 2 Wallops Flight Facility, NASA Goddard Space Flight Center, Wallops Island, Virginia
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Abstract

Polarimetric radar data obtained at high spatial and temporal resolutions offer a distinct advantage in estimating the spatial correlation function of drop size distribution (DSD) parameters and rain rate compared with a fixed gauge–disdrometer network. On two days during the 2011 Midlatitude Continental Convective Clouds Experiment (MC3E) campaign in Oklahoma, NASA’s S-band polarimetric radar (NPOL) performed repeated PPI scans every 40 s over six 2D video disdrometer (2DVD) sites, located 20–30 km from the radar. The two cases were 1) a rapidly evolving multicell rain event (with large drops) and 2) a long-duration stratiform rain event. From the time series at each polar pixel, the Pearson correlation coefficient is computed as a function of distance along each radial in the PPI scan. Azimuthal dependence is found, especially for the highly convective event. A pseudo-1D spatial correlation is computed that is fitted to a modified-exponential function with two parameters (decorrelation distance R0 and shape F). The first event showed significantly higher spatial variability in rain rate (shorter decorrelation distance R0 = 3.4 km) compared with the second event with R0 = 10.2 km. Further, for the second event, the spatial correlation of the DSD parameters and rain rate from radar showed good agreement with 2DVD-based spatial correlations over distances ranging from 1.5 to 7 km. The NPOL also performed repeated RHI scans every 40 s along one azimuth centered over the 2DVD network. Vertical correlations of the DSD parameters as well as the rainwater content were determined below the melting level, with the first event showing more variability compared with the second event.

Corresponding author address: Prof. V. N. Bringi, Department of Electrical and Computer Engineering, Colorado State University, Campus Mail 1373, Fort Collins, CO 80523-1373. E-mail: bringi@engr.colostate.edu

Abstract

Polarimetric radar data obtained at high spatial and temporal resolutions offer a distinct advantage in estimating the spatial correlation function of drop size distribution (DSD) parameters and rain rate compared with a fixed gauge–disdrometer network. On two days during the 2011 Midlatitude Continental Convective Clouds Experiment (MC3E) campaign in Oklahoma, NASA’s S-band polarimetric radar (NPOL) performed repeated PPI scans every 40 s over six 2D video disdrometer (2DVD) sites, located 20–30 km from the radar. The two cases were 1) a rapidly evolving multicell rain event (with large drops) and 2) a long-duration stratiform rain event. From the time series at each polar pixel, the Pearson correlation coefficient is computed as a function of distance along each radial in the PPI scan. Azimuthal dependence is found, especially for the highly convective event. A pseudo-1D spatial correlation is computed that is fitted to a modified-exponential function with two parameters (decorrelation distance R0 and shape F). The first event showed significantly higher spatial variability in rain rate (shorter decorrelation distance R0 = 3.4 km) compared with the second event with R0 = 10.2 km. Further, for the second event, the spatial correlation of the DSD parameters and rain rate from radar showed good agreement with 2DVD-based spatial correlations over distances ranging from 1.5 to 7 km. The NPOL also performed repeated RHI scans every 40 s along one azimuth centered over the 2DVD network. Vertical correlations of the DSD parameters as well as the rainwater content were determined below the melting level, with the first event showing more variability compared with the second event.

Corresponding author address: Prof. V. N. Bringi, Department of Electrical and Computer Engineering, Colorado State University, Campus Mail 1373, Fort Collins, CO 80523-1373. E-mail: bringi@engr.colostate.edu
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