Editorial Type:
Article
Article Type:
Research Article

An Investigation of the Short-Term Predictability of Precipitation Using High-Resolution Composite Radar Observations

Evan Ruzanski Colorado State University, Fort Collins, Colorado

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V. Chandrasekar Colorado State University, Fort Collins, Colorado

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Print Publication:
01 May 2012
DOI:
https://doi.org/10.1175/JAMC-D-11-069.1
Page(s):
912–925
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Abstract

The short-term predictability of precipitation patterns observed by meteorological radar is an important concept as it establishes a means to characterize precipitation and provides an upper limit on the extent of useful nowcasting. Predictability also varies on the basis of spatial and temporal scales of the observed meteorological phenomena. This paper describes an investigation of the short-term predictability of precipitation patterns containing microalpha (0.2–2 km) to mesobeta (20–200 km) scales using high-resolution (0.5 km–1 min–1 dBZ) composite radar reflectivity data, extending the analysis presented in previous work to smaller space and time scales. An experimental approach is used in which continuous and categorical lifetimes of radar reflectivity fields in Eulerian and Lagrangian space are used to quantify short-term predictability. The space–time scale dependency of short-term predictability is analyzed, and a practical upper limit on the extent of Lagrangian persistence-based nowcasting is estimated. Connections to the predictability of larger scales are made within the context of previous work. The results show that short-term predictability estimates in terms of lifetime are approximately 14–15 and 20–21 min in Eulerian and Lagrangian space, respectively, and suggest that a linear relationship exists between predictability and space–time structure from microalpha to macrobeta (2000–10 000 km) scales.

Current affiliation: Vaisala, Inc., Louisville, Colorado.

Corresponding author address: Evan Ruzanski, Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523.E-mail: ruzanski@engr.colostate.edu

Abstract

The short-term predictability of precipitation patterns observed by meteorological radar is an important concept as it establishes a means to characterize precipitation and provides an upper limit on the extent of useful nowcasting. Predictability also varies on the basis of spatial and temporal scales of the observed meteorological phenomena. This paper describes an investigation of the short-term predictability of precipitation patterns containing microalpha (0.2–2 km) to mesobeta (20–200 km) scales using high-resolution (0.5 km–1 min–1 dBZ) composite radar reflectivity data, extending the analysis presented in previous work to smaller space and time scales. An experimental approach is used in which continuous and categorical lifetimes of radar reflectivity fields in Eulerian and Lagrangian space are used to quantify short-term predictability. The space–time scale dependency of short-term predictability is analyzed, and a practical upper limit on the extent of Lagrangian persistence-based nowcasting is estimated. Connections to the predictability of larger scales are made within the context of previous work. The results show that short-term predictability estimates in terms of lifetime are approximately 14–15 and 20–21 min in Eulerian and Lagrangian space, respectively, and suggest that a linear relationship exists between predictability and space–time structure from microalpha to macrobeta (2000–10 000 km) scales.

Current affiliation: Vaisala, Inc., Louisville, Colorado.

Corresponding author address: Evan Ruzanski, Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523.E-mail: ruzanski@engr.colostate.edu
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Journal of Applied Meteorology and Climatology

  • Accadia, C., S. Mariani, M. Casaioli, A. Lavagnini, and A. Speranza, 2003: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Wea. Forecasting, 18, 918–932.

    • Search Google Scholar
    • Export Citation

  • Askelson, M. A., J.-P. Aubanac, and J. M. Straka, 2000: An adaptation of the Barnes filter applied to the objective analysis of radar data. Mon. Wea. Rev., 128, 3050–3082.

    • Search Google Scholar
    • Export Citation

  • Atlas, D., and H. C. Banks, 1951: The interpretation of microwave reflections from rainfall. J. Meteor., 8, 271–282.

  • Baldwin, M. E., and J. S. Kain, 2006: Sensitivity of several performance measures to displacement error, bias, and event frequency. Wea. Forecasting, 21, 636–648.

    • Search Google Scholar
    • Export Citation

  • Bellon, A., and I. Zawadzki, 1994: Forecasting of hourly accumulations of precipitation by optimal extrapolation of radar maps. J. Hydrol., 157, 211–233.

    • Search Google Scholar
    • Export Citation

  • Brandes, E. A., C. E. Ryzhkov, and D. S. Zrnić, 2001: An evaluation of radar rainfall estimates from specific differential phase. J. Atmos. Oceanic Technol., 18, 363–375.

    • Search Google Scholar
    • Export Citation

  • Brotzge, J., K. Brewster, B. Johnson, B. Phillips, M. Preston, D. Westbrook, and M. Zink, 2005: CASA’s first test bed: Integrative Project #1. Preprints, 32nd Conf. on Radar Meteorology, Albuquerque, NM, Amer. Meteor. Soc., 14R.2. [Available online at http://ams.confex.com/ams/pdfpapers/97188.pdf.]

  • Browning, K. A., 1979: The FRONTIERS plan: A strategy for using radar and satellite imagery for very short-range precipitation forecasting. Meteor. Mag., 108, 161–184.

    • Search Google Scholar
    • Export Citation

  • Byers, H. R., and R. R. Braham, 1949: The Thunderstorm: The Final Report of the Thunderstorm Project. U.S. Government Printing Office, 282 pp.

  • Carbone, R. E., M. J. Carpenter, and C. D. Burghart, 1985: Doppler radar sampling limitations in convective storms. J. Atmos. Oceanic Technol., 2, 357–361.

    • Search Google Scholar
    • Export Citation

  • Cartwright, T. J., M. M. Wolfson, B. E. Forman, R. G. Hallowell, M. P. Moore, and K. E. Theriault, 1999: The FAA Terminal Convective Weather Forecast product: Scale separation filter optimization. Preprints, 29th Int. Conf. on Radar Meteorology, Montreal, QC, Canada, Amer. Meteor. Soc., 852–855.

  • Chandrasekar, V., D. McLaughlin, Y. Wang, J. Brotzge, M. Zink, and B. Phillips, 2008: Radar observations of a severe weather event from distributed collaborative adaptive sensing network in the IP1 testbed. Proc. 29th URSI General Assembly, Chicago, IL, International Union of Radio Science, F05.2.

  • Chandrasekar, V., D. Willie, Y. Wang, and S. Lim, 2009: Attenuation margin requirements in a networked radar system for observation of precipitation. Proc. Int. Geoscience and Remote Sensing Symp., Cape Town, South Africa, IEEE, 957–959.

  • Foufoula-Georgiou, E., 1996: On scaling theories of space–time rainfall: Some recent results and open problems. Stochastic Methods in Hydrology: Rain, Landforms and Floods, O. E. Barndorff-Nielsen et al., Eds., World Scientific, Singapore, 25–65.

  • Fujita, T. T., 1981: Tornadoes and downbursts in the context of generalized planetary scales. J. Atmos. Sci., 38, 1511–1534.

  • Gal-Chen, T., and J. C. Wyngaard, 1982: Effects of volume averaging on the line spectra of vertical velocity from multiple-Doppler radar observations. J. Appl. Meteor., 21, 1881–1890.

    • Search Google Scholar
    • Export Citation

  • Germann, U., and I. Zawadzki, 2002: Scale dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Mon. Wea. Rev., 130, 2859–2873.

    • Search Google Scholar
    • Export Citation

  • Germann, U., I. Zawadzki, and B. Turner, 2006: Predictability of precipitation from continental radar images. Part IV: Limits to prediction. J. Atmos. Sci., 63, 2092–2108.

    • Search Google Scholar
    • Export Citation

  • Gilbert, G. K., 1884: Finley’s tornado predictions. Amer. Meteor. J., 1, 166–172.

  • Grecu, M., and W. F. Krajewski, 2000: A large-sample investigation of statistical procedures for radar-based short-term quantitative precipitation forecasting. J. Hydrol., 239, 69–84.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155–167.

  • Hogan, R., C. Ferro, I. Jolliffe, and D. Stephenson, 2010: Equitability revisited: Why the “equitable” threat score is not equitable. Wea. Forecasting, 25, 710–726.

    • Search Google Scholar
    • Export Citation

  • Jacobson, M. Z., 2005: Fundamentals of Atmospheric Modeling. 2nd ed. Cambridge University Press, 828 pp.

  • Jameson, A. R., and A. B. Kostinski, 2001: Reconsideration of the physical and empirical origins of Z–R relations in radar meteorology. Quart. J. Roy. Meteor. Soc., 127, 517–538.

    • Search Google Scholar
    • Export Citation

  • Krajewski, W. F., G. Villarini, and J. A. Smith, 2010: Radar-rainfall uncertainties. Bull. Amer. Meteor. Soc., 91, 87–94.

  • Lakshmanan, V., R. Rabin, and V. DeBrunner, 2003: Multiscale storm identification and forecast. J. Atmos. Res., 67–68, 367–380.

  • Lee, G. W., and I. Zawadzki, 2005: Variability of drop size distributions: Time-scale dependence of the variability and its effects on rainfall estimation. J. Appl. Meteor., 44, 241–255.

    • Search Google Scholar
    • Export Citation

  • Liu, Y., V. N. Bringi, and M. Maki, 2006: Improved rain attenuation correction algorithms for radar reflectivity and differential reflectivity with adaptation to drop shape model variation. Proc. Int. Geoscience and Remote Sensing Symp., Denver, CO, IEEE, 1910–1913.

  • Liu, Y., V. Chandrasekar, and V. N. Bringi, 2007: Real-time three-dimensional radar mosaic in CASA IP1 testbed. Proc. Int. Geoscience and Remote Sensing Symp., Barcelona, Spain, IEEE, 2754–2757.

  • Mason, I. B., 1989: Dependence of the critical success index on sample climate and threshold probability. Aust. Meteor. Mag., 37, 75–81.

    • Search Google Scholar
    • Export Citation

  • Mason, I. B., 2003: Binary events. Forecast Verification—A Practitioner’s Guide in Atmospheric Science, I. T. Joliffe and D. B. Stephenson, Eds., John Wiley and Sons, 37–67.

  • McLaughlin, D., and Coauthors, 2009: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Amer. Meteor. Soc., 90, 1797–1817.

    • Search Google Scholar
    • Export Citation

  • Mesinger, F., 2008: Bias adjusted precipitation threat scores. Adv. Geosci., 16, 137–142.

  • Mueller, C., T. Saxen, R. Roberts, J. Wilson, T. Betancourt, S. Dettling, N. Oien, and J. Yee, 2003: NCAR Auto-Nowcast system. Wea. Forecasting, 18, 545–561.

    • Search Google Scholar
    • Export Citation

  • Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc., 56, 527–530.

  • Park, S. G., M. Maki, K. Iwanami, V. N. Bringi, and V. Chandrasekar, 2005: Correction of radar reflectivity and differential reflectivity for rain attenuation at X-band. Part II: Evaluation and application. J. Atmos. Oceanic Technol., 22, 1633–1655.

    • Search Google Scholar
    • Export Citation

  • Pellarin, T., G. Delrieu, G.-M. Saulnier, H. Andrieu, B. Vignal, and J. D. Creutin, 2002: Hydrologic visibility of weather radar systems operating in mountainous regions: Case study for the Ardéche catchment (France). J. Hydrometeor., 3, 539–555.

    • Search Google Scholar
    • Export Citation

  • Perica, S., and E. Foufoula-Georgiou, 1996: Linkage of scaling and thermodynamic parameters of rainfall: Results from midlatitude mesoscale convective systems. J. Geophys. Res., 101, 7431–7448.

    • Search Google Scholar
    • Export Citation

  • Porcù, F., and D. Capacci, 2008: Seasonal sensitivity of a VIS–NIR–IR rain–no rain classifier. Meteor. Atmos. Phys., 101, 147–157.

    • Search Google Scholar
    • Export Citation

  • Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562–584.

    • Search Google Scholar
    • Export Citation

  • Rosenfeld, D., and C. W. Ulbricht, 2003: Cloud microphysical properties, processes, and rainfall estimation opportunities. Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, R. M. Wakimoto and R. Srivastava, Eds., Amer. Meteor. Soc., 237–258.

  • Ruzanski, E., and V. Chandrasekar, 2011: Scale filtering for improved nowcasting performance in a high-resolution X-band radar network. IEEE Trans. Geosci. Remote Sens., 49, 2296–2307.

    • Search Google Scholar
    • Export Citation

  • Ruzanski, E., V. Chandrasekar, and Y. Wang, 2011: The CASA nowcasting system. J. Atmos. Oceanic Technol., 28, 640–655.

  • Ryde, J. W., 1946: The attenuation and radar echoes produced at centimeter wavelengths by various meteorological phenomena. Meteorological Factors in Radio Wave Propagation, E. Appleton, Ed., Physical Society of London, 169–189.

  • Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570–575.

  • Seed, A. W., 2003: A dynamic and spatial scaling approach to advection forecasting. J. Appl. Meteor., 42, 381–388.

  • Seo, B. C., and W. F. Krajewski, 2010: Scale dependence of radar rainfall uncertainty: Initial evaluation of NEXRAD’s new super-resolution data for hydrologic applications. J. Hydrometeor., 11, 1191–1198.

    • Search Google Scholar
    • Export Citation

  • Sinclair, S., and G. G. S. Pegram, 2005: Empirical mode decomposition in 2-D space and time: A tool for space–time rainfall analysis and nowcasting. Hydrol. Earth Syst. Sci., 9, 127–137.

    • Search Google Scholar
    • Export Citation

  • Steiner, M., J. A. Smith, and R. Uijlenhoet, 2004: A microphysical interpretation of radar reflectivity–rain rate relationships. J. Atmos. Sci., 61, 1114–1131.

    • Search Google Scholar
    • Export Citation

  • Stephenson, D. B., B. Casati, C. A. T. Ferro, and C. A. Wilson, 2008: The extreme dependency score: A non-vanishing measure for forecasts of rare events. Meteor. Appl., 15, 41–50.

    • Search Google Scholar
    • Export Citation

  • Tartaglione, N., 2010: Relationship between precipitation forecast errors and skill scores of dichotomous forecasts. Wea. Forecasting, 25, 355–365.

    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., and C. A. Doswell, 2000: Radar data objective analysis. J. Atmos. Oceanic Technol., 17, 105–120.

  • Tsonis, A. A., and G. L. Austin, 1981: An evaluation of extrapolation techniques for the short-term prediction of rain amounts. Atmos.–Ocean, 19, 54–65.

    • Search Google Scholar
    • Export Citation

  • Turner, B. J., I. Zawadzki, and U. Germann, 2004: Predictability of precipitation from continental radar images. Part III: Operational nowcasting implementation (MAPLE). J. Appl. Meteor., 43, 231–248.

    • Search Google Scholar
    • Export Citation

  • Van Horne, M. P., E. R. Vivoni, D. Entekhabi, R. N. Hoffman, and C. Grassotti, 2006: Evaluating the effects of image filtering in short-term radar rainfall forecasting for hydrological applications. Meteor. Appl., 13, 289–303.

    • Search Google Scholar
    • Export Citation

  • Weisman, M. L., and J. B. Klemp, 1986: Characteristics of isolated convective storms. Mesoscale Meteorology and Forecasting, P. S. Ray, Ed., Amer. Meteor. Soc., 331–358.

  • Weygandt, S., A. Loughe, S. Benjamin, and J. Mahoney, 2004: Scale sensitivities in model precipitation skill scores during IHOP. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc, 16A.8. [Available online at http://ams.confex.com/ams/pdfpapers/81986.pdf.]

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Wilson, J. W., N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon, 1998: Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Soc., 79, 2079–2099.

    • Search Google Scholar
    • Export Citation

  • Wolfson, M. M., B. E. Forman, R. G. Hallowell, and M. P. Moore, 1999: The growth and decay storm tracker. Preprints, Eighth Conf. on Aviation, Range, and Aerospace Meteor, Dallas, TX, Amer. Meteor. Soc., 58–62.

  • Xu, K., C. K. Wikle, and N. I. Fox, 2005: A kernel-based spatio-temporal dynamical model for nowcasting weather radar reflectivities. J. Amer. Stat. Soc., 100, 1134–1144.

    • Search Google Scholar
    • Export Citation

  • Zawadzki, I., 1973: Statistical properties of precipitation patterns. J. Appl. Meteor., 12, 459–472.

  • Zawadzki, I., J. Morneau, and R. Laprise, 1994: Predictability of precipitation patterns: An operational approach. J. Appl. Meteor., 33, 1562–1571.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., K. Howard, and J. J. Gourley, 2005: Constructing three-dimensional multiple-radar reflectivity mosaics: Examples of convective of stratiform rain echoes. J. Atmos. Oceanic Technol., 22, 30–42.

    • Search Google Scholar
    • Export Citation
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