Editorial Type:
Article
Article Type:
Research Article

Predictability of Precipitation from Continental Radar Images. Part III: Operational Nowcasting Implementation (MAPLE)

B. J. Turner Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

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I. Zawadzki Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

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U. Germann MeteoSvizzera, Locarno-Monti, Switzerland

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Print Publication:
01 Feb 2004
DOI:
https://doi.org/10.1175/1520-0450(2004)043<0231:POPFCR>2.0.CO;2
Page(s):
231–248
Restricted access

Abstract

Filtering of nonpredictable scales of precipitation can be used to improve forecast precision (rms). Previous papers have studied the scale dependence of predictability of patterns of instantaneous rainfall rate and of probabilistic forecasts. In this paper, motivated by the often localized, intermittent nature of rainfall, the wavelet transform is used to develop measures of predictability at each scale. These measures are then used to design optimal forecast filters. This method is applied to radar composites of rainfall reflectivity over much of the continental United States and is developed to be appropriate for operational forecasts of rainfall rates and raining areas. For the four precipitation events studied, the average correlation at 4-h lead time was increased from 0.50 for the original nowcasts to 0.62 with forecast filtering. This forecast filtering is incorporated into the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), which now includes variational echo tracking, a semi-Lagrangian advection scheme, scale-based filtering, and appropriate rescaling of the filtered nowcast fields.

Corresponding author address: Dr. Barry Turner, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke W, Montreal QC H3A 2K6, Canada. bturner@cumulus.meteo.mcgill.ca

Abstract

Filtering of nonpredictable scales of precipitation can be used to improve forecast precision (rms). Previous papers have studied the scale dependence of predictability of patterns of instantaneous rainfall rate and of probabilistic forecasts. In this paper, motivated by the often localized, intermittent nature of rainfall, the wavelet transform is used to develop measures of predictability at each scale. These measures are then used to design optimal forecast filters. This method is applied to radar composites of rainfall reflectivity over much of the continental United States and is developed to be appropriate for operational forecasts of rainfall rates and raining areas. For the four precipitation events studied, the average correlation at 4-h lead time was increased from 0.50 for the original nowcasts to 0.62 with forecast filtering. This forecast filtering is incorporated into the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE), which now includes variational echo tracking, a semi-Lagrangian advection scheme, scale-based filtering, and appropriate rescaling of the filtered nowcast fields.

Corresponding author address: Dr. Barry Turner, Department of Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke W, Montreal QC H3A 2K6, Canada. bturner@cumulus.meteo.mcgill.ca

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Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Austin, G. L. and A. Bellon. 1974. The use of digital weather radar records for short-term precipitation forecasting. Quart. J. Roy. Meteor. Soc. 100:658–664.

    • Search Google Scholar
    • Export Citation

  • Bellon, A. and G. L. Austin. 1978. The evaluation of two years of real-time operation of a short-term precipitation forecasting procedure (SHARP). J. Appl. Meteor. 17:1778–1787.

    • 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

  • Bellon, A., S. Lovejoy, and G. L. Austin. 1980. Combining satellite and radar data for the short-range forecasting of precipitation. Mon. Wea. Rev. 108:1554–1566.

    • Search Google Scholar
    • Export Citation

  • Browning, K. A., C. G. Collier, P. R. Larke, P. Menmuir, G. A. Monk, and R. G. Owens. 1982. On the forecasting of frontal rain using a weather radar network. Mon. Wea. Rev. 110:534–552.

    • Search Google Scholar
    • Export Citation

  • Burrus, C. S., R. A. Gopinath, and H. Guo. 1998. Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice Hall, 268 pp.

  • Ersoy, O. K. 1997. Fourier-Related Transforms, Fast Algorithms and Applications. Prentice Hall, 522 pp.

  • 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. and I. Zawadzki. 2004. Scale dependence of the predictability of precipitation from continental radar images. Part II: Probability forecasts. J. Appl. Meteor. 43:74–89.

    • Search Google Scholar
    • Export Citation

  • Hudgins, L. H., M. E. Mayer, and C. A. Friehe. 1993. Wavelet transforms and atmospheric turbulence. Phys. Rev. Lett. 71:3279–3282.

    • Search Google Scholar
    • Export Citation

  • Laroche, S. and I. Zawadzki. 1994. A variational analysis method for retrieval of three-dimensional wind field from single–Doppler radar data. J. Atmos. Sci. 51:2664–2682.

    • Search Google Scholar
    • Export Citation

  • Laroche, S. and I. Zawadzki. 1995. Retrievals of horizontal winds from single-Doppler clear-air data by methods of cross-correlation and variational analysis. J. Atmos. Oceanic Technol. 12:721–738.

    • Search Google Scholar
    • Export Citation

  • Lorenz, E. N. 1969. The predictability of a flow which possesses many scales of motion. Tellus 21:289–307.

  • Mallat, S. G. 1989. A theory for multiresolution decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 11:674–693.

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

  • Schertzer, D. and S. Lovejoy. 1987. Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes. J. Geophys. Res. 92:9693–9714.

    • Search Google Scholar
    • Export Citation

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

  • 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

  • Walker, J. S. 1988. Fourier Analysis. Oxford University Press, 440 pp.

  • Walton, M. L., P. R. Ahnert, and M. D. Hudlow. 1985. Proposed on-site flash flood potential system for NEXRAD. Preprints, Sixth Conf. on Hydrometeorology, Indianapolis, IN, Amer. Meteor. Soc., 122–129.

    • Search Google Scholar
    • Export Citation

  • Weaver, H. J. 1989. Theory of Discrete and Continuous Fourier Analysis. John Wiley and Sons, 307 pp.

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

  • Zawadzki, I. 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
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