Evaluation of Operational and Experimental Precipitation Algorithms and Microphysical Insights during IPHEx

Jessica M. Erlingis Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
School of Meteorology, University of Oklahoma, Norman, Oklahoma
NOAA/National Severe Storms Laboratory, Norman, Oklahoma
Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

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Jonathan J. Gourley School of Meteorology, University of Oklahoma, Norman, Oklahoma
NOAA/National Severe Storms Laboratory, Norman, Oklahoma

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Pierre-Emmanuel Kirstetter NOAA/National Severe Storms Laboratory, Norman, Oklahoma
Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma
Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma

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Emmanouil N. Anagnostou Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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John Kalogiros Institute of Environmental Research and Sustainable Development, National Observatory of Athens, Athens, Greece

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Marios N. Anagnostou Department of Water Resources, School of Civil Engineering, National Technical University of Athens, Athens, Greece

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Walt Petersen NASA Marshall Space Flight Center, Huntsville, Alabama

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Abstract

During May and June 2014, NOAA X-Pol (NOXP), the National Severe Storms Laboratory’s dual-polarized X-band mobile radar, was deployed to the Pigeon River basin in the Great Smoky Mountains of North Carolina as part of the NASA Integrated Precipitation and Hydrology Experiment. Rain gauges and disdrometers were positioned within the basin to verify precipitation estimates from various radar and satellite precipitation algorithms. First, the performance of the Self-Consistent Optimal Parameterization–Microphysics Estimation (SCOP-ME) algorithm for NOXP was examined using ground instrumentation as validation and was found to perform similarly to or slightly outperform other precipitation algorithms over the course of the intensive observation period (IOP). Radar data were also used to examine ridge–valley differences in radar and microphysical parameters for a case of stratiform precipitation passing over the mountains. Inferred coalescence microphysical processes were found to dominate within the upslope region, while a combination of processes were present as the system propagated over the valley. This suggests that enhanced updrafts aided by orographic lift sustain convection over the upslope regions, leading to larger median drop diameters.

© 2018 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: Jessica M. Erlingis, jerlingis@ou.edu

Abstract

During May and June 2014, NOAA X-Pol (NOXP), the National Severe Storms Laboratory’s dual-polarized X-band mobile radar, was deployed to the Pigeon River basin in the Great Smoky Mountains of North Carolina as part of the NASA Integrated Precipitation and Hydrology Experiment. Rain gauges and disdrometers were positioned within the basin to verify precipitation estimates from various radar and satellite precipitation algorithms. First, the performance of the Self-Consistent Optimal Parameterization–Microphysics Estimation (SCOP-ME) algorithm for NOXP was examined using ground instrumentation as validation and was found to perform similarly to or slightly outperform other precipitation algorithms over the course of the intensive observation period (IOP). Radar data were also used to examine ridge–valley differences in radar and microphysical parameters for a case of stratiform precipitation passing over the mountains. Inferred coalescence microphysical processes were found to dominate within the upslope region, while a combination of processes were present as the system propagated over the valley. This suggests that enhanced updrafts aided by orographic lift sustain convection over the upslope regions, leading to larger median drop diameters.

© 2018 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: Jessica M. Erlingis, jerlingis@ou.edu
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  • Anagnostou, M. N., J. Kalogiros, F. S. Marzano, E. N. Anagnostou, M. Montopoli, and E. Piccioti, 2013: Performance evaluation of a new dual-polarization microphysical algorithm based on long-term X-band radar and disdrometer observations. J. Hydrometeor., 14, 560576, https://doi.org/10.1175/JHM-D-12-057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anagnostou, M. N., J. Kalogiros, E. Nikolopoulos, Y. Derin, E. N. Anagnostou, and M. Borga, 2017: Satellite rainfall error analysis with the use of high-resolution X-band dual-polarization radar observations over the Italian Alps. Perspectives on Atmospheric Sciences, T. Karacostas, A. Bais, and P. Nastos, Eds., Springer, 279–286, https://doi.org/10.1007/978-3-319-35095-0_39.

    • Crossref
    • Export Citation
  • Barros, A. P., and Coauthors, 2014: NASA GPM-ground validation: Integrated Precipitation and Hydrology Experiment 2014. NASA Tech. Rep., 64 pp., https://doi.org/10.7924/G8CC0XMR.

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

  • Chandrasekar, V., M. Schwaller, M. Vega, J. Carswell, K. Mishra, R. Meneghini, and C. Nguyen, 2010: Scientific and engineering overview of the NASA dual-frequency dual-polarized Doppler radar (D3R) system for GPM ground validation. Proc. 2010 IEEE Int. Geoscience and Remote Sensing Symp., Honolulu, HI, IEEE, 1308–1311, https://doi.org/10.1109/IGARSS.2010.5649440.

    • Crossref
    • Export Citation
  • Cifelli, R., V. Chandrasekar, S. Lim, P. C. Kennedy, Y. Wang, and S. A. Rutledge, 2011: A new dual-polarization radar rainfall algorithm: Application in Colorado precipitation events. J. Atmos. Oceanic Technol., 28, 352364, https://doi.org/10.1175/2010JTECHA1488.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 20312064, https://doi.org/10.1002/joc.1688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duan, Y., A. M. Wilson, and A. P. Barros, 2015: Scoping a field experiment: Error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014. Hydrol. Earth Syst. Sci., 19, 15011520, https://doi.org/10.5194/hess-19-1501-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gori, E. G., and J. Joss, 1980: Changes of shape of raindrop size distributions simultaneously observed along a mountain slope. J. Rech. Atmos., 14, 239300.

    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., D. P. Jorgensen, S. Y. Matrosov, and Z. L. Flamig, 2009: Evaluation of incremental improvements to quantitative precipitation estimates in complex terrain. J. Hydrometeor., 10, 15071520, https://doi.org/10.1175/2009JHM1125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grams, H. M., J. Zhang, and K. L. Elmore, 2014: Automated identification of enhanced rainfall rates using the near-storm environment for radar precipitation estimates. J. Hydrometeor., 15, 12381254, https://doi.org/10.1175/JHM-D-13-042.1.

    • 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, e25, http://doi.org/10.5334/jors.119.

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

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jorgensen, D. P., M. N. Hanshaw, K. M. Schmidt, J. L. Laber, D. M. Staley, J. W. Kean, and P. J. Restrepo, 2011: Value of a dual-polarized gap-filling radar in support of Southern California post-fire debris-flow warnings. J. Hydrometeor., 12, 15811595, https://doi.org/10.1175/JHM-D-11-05.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalogiros, J., M. N. Anagnostou, E. N. Anagnostou, M. Montopoli, E. Picciotti, and F. S. Marzano, 2013: Correction of polarimetric radar reflectivity measurements and rainfall estimates for apparent vertical profile in stratiform rain. J. Appl. Meteor. Climatol., 52, 11701186, https://doi.org/10.1175/JAMC-D-12-0140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalogiros, J., M. N. Anagnostou, E. N. Anagnostou, M. Montopoli, E. Picciotti, and F. S. Marzano, 2014: Evaluation of a new polarimetric algorithm for rain-path attenuation correction of X-band radar observations against disdrometer data. IEEE Trans. Geosci. Remote Sens., 52, 13691380, https://doi.org/10.1109/TGRS.2013.2250979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klazura, G. E., and D. A. Imy, 1993: A description of the initial set of analysis products available from the NEXRAD WSR-88D system. Bull. Amer. Meteor. Soc., 74, 12931311, https://doi.org/10.1175/1520-0477(1993)074<1293:ADOTIS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., A. Ntelekos, and R. Goska, 2006: A GIS-based methodology for the assessment of weather radar beam blockage in mountainous regions: Two examples from the US NEXRAD network. Comput. Geosci., 32, 283302, https://doi.org/10.1016/j.cageo.2005.06.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and A. V. Ryzhkov, 2012: The impact of size sorting on the polarimetric radar variables. J. Atmos. Sci., 69, 20422060, https://doi.org/10.1175/JAS-D-11-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and O. P. Prat, 2014: The impact of raindrop collisional processes on the polarimetric radar variables. J. Atmos. Sci., 71, 30523067, https://doi.org/10.1175/JAS-D-13-0357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., S. Mishra, S. E. Giangrande, T. Toto, A. V. Ryzhkov, and A. Bansemer, 2016: Polarimetric radar and aircraft observations of saggy bright bands during MC3E. J. Geophys. Res. Atmos., 121, 35843607, https://doi.org/10.1002/2015JD024446.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2, http://ams.confex.com/ams/pdfpapers/83847.pdf.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., J. Zhang, J. J. Gourley, and K. W. Howard, 2002: Weather radar coverage over the contiguous United States. Wea. Forecasting, 17, 927934, https://doi.org/10.1175/1520-0434(2002)017<0927:WRCOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., D. E. Kingsmill, B. E. Martner, and F. M. Ralph, 2005: The utility of X-band polarimetric radar for quantitative estimates of rainfall parameters. J. Hydrometeor., 6, 248262, https://doi.org/10.1175/JHM424.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Picciotti, E., and Coauthors, 2013: Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: The HYDRORAD project. Nat. Hazards Earth Syst. Sci., 13, 12291241, https://doi.org/10.5194/nhess-13-1229-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porcù, F., L. P. D’Adderio, F. Prodi, and C. Caracciolo, 2013: Effects of altitude on maximum raindrop size and fall velocity as limited by collisional breakup. J. Atmos. Sci., 70, 11291134, https://doi.org/10.1175/JAS-D-12-0100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and A. P. Barros, 2010: Ground observations to characterize the spatial gradients and vertical structure of orographic precipitation—Experiments in the inner region of the Great Smoky Mountains. J. Hydrol., 391, 141156, https://doi.org/10.1016/j.jhydrol.2010.07.013.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, P. L. Heinselman, and S. E. Giangrande, 2005b: The Joint Polarization Experiment: Polarimetric rainfall measurements and hydrometeor classification. Bull. Amer. Meteor. Soc., 86, 809824, https://doi.org/10.1175/BAMS-86-6-809.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Squires, P., 1956: The micro-structure of cumuli in maritime and continental air. Tellus, 8, 443444, https://doi.org/10.3402/tellusa.v8i4.9040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, J., and A. P. Barros, 2013: Prospects for flash flood forecasting in mountainous regions—An investigation of Tropical Storm Fay in the southern Appalachians. J. Hydrol., 506, 6989, https://doi.org/10.1016/j.jhydrol.2013.02.052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Testud, J., E. Le Bouar, E. Obligis, and M. Ali-Mehenni, 2000: The rain profiling algorithm applied to polarimetric weather radar. J. Atmos. Oceanic Technol., 17, 332356, https://doi.org/10.1175/1520-0426(2000)017<0332:TRPAAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulbrich, C. W., and D. Atlas, 2008: Radar measurement of rainfall with and without polarimetry. J. Appl. Meteor. Climatol., 47, 19291939, https://doi.org/10.1175/2007JAMC1804.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitale, J. D., and T. Ryan, 2013: Operational recognition of high precipitation efficiency and low-echo-centroid convection. J. Oper. Meteor, 1, 128143, https://doi.org/10.15191/nwajom.2013.0112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westrick, K. J., C. F. Mass, and B. A. Colle, 1999: The limitations of the WSR-88D radar network for quantitative precipitation measurement over the coastal western United States. Bull. Amer. Meteor. Soc., 80, 22892298, https://doi.org/10.1175/1520-0477(1999)080<2289:TLOTWR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, A. M., and A. P. Barros, 2014: An investigation of warm rainfall microphysics in the southern Appalachians: Orographic enhancement via low-level seeder–feeder interactions. J. Atmos. Sci., 71, 17831805, https://doi.org/10.1175/JAS-D-13-0228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, A. M., and A. P. Barros, 2015: Landform controls on low level moisture convergence and the diurnal cycle of warm season orographic rainfall in the southern Appalachians. J. Hydrol., 531, 475493, https://doi.org/10.1016/j.jhydrol.2015.10.068.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, X., K. Howard, and J. Zhang, 2008: An automated radar technique for the identification of tropical precipitation. J. Hydrometeor, 9, 885902, https://doi.org/10.1175/2007JHM954.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, C. B., B. R. Nelson, A. A. Bradley, J. A. Smith, C. D. Peters-Lidard, A. Kruger, and M. L. Baeck, 1999: An evaluation of NEXRAD precipitation estimates in complex terrain. J. Geophys. Res., 104, 19 69119 703, https://doi.org/10.1029/1999JD900123.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., and R. A. Houze Jr., 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123, 19411963, https://doi.org/10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zrnić, D. S., and A. V. Ryzhkov, 1999: Polarimetry for weather surveillance radars. Bull. Amer. Meteor. Soc., 80, 389406, https://doi.org/10.1175/1520-0477(1999)080<0389:PFWSR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zwiebel, J., J. Van Baelen, S. Anquetin, Y. Pointin, and B. Boudevillain, 2016: Impacts of orography and rain intensity on rainfall structure. The case of the HyMeX IOP7a event. Quart. J. Roy. Meteor. Soc., 142, 310319, https://doi.org/10.1002/qj.2679.

    • Crossref
    • Search Google Scholar
    • Export Citation
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