Editorial Type:
Article
Article Type:
Research Article

A Geostatistical Framework for Quantifying the Temporal Evolution and Predictability of Rainfall Fields

Marc Schleiss Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Print Publication:
01 Mar 2016
DOI:
https://doi.org/10.1175/JHM-D-15-0137.1
Page(s):
915–929
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Abstract

A geostatistical framework for quantifying the temporal evolution and predictability of rainfall fields for time lags between 5 min and 3 h is proposed. The method is based on the computation of experimental space–time variogram maps of radar reflectivity fields. Two new metrics for quantifying temporal innovation and predictability based on minimum semivariance values at different time lags are proposed. The method is applied to high-resolution composite radar reflectivity maps over the United States to study the evolution of 25 convective and 25 stratiform events during the warm season of 2014. Results show that the temporal innovation can be modeled as the sum of two exponential functions of time lag, with approximately 50% of the total innovation occurring over the first 60 min. The median predictable scales for convective events are on the order of 1.6 km at 5 min, 5 km at 15 min, and 12.7 km at 1 h. Furthermore, the optimal time lag for predicting future innovation, taking into account measurement uncertainty and natural variability, appears to be between 30 and 60 min.

Corresponding author address: Marc Schleiss, Civil and Environmental Engineering, Princeton University, E-Quad E316, Olden Street, Princeton, NJ 08540. E-mail: schleiss.marc@gmail.com

Abstract

A geostatistical framework for quantifying the temporal evolution and predictability of rainfall fields for time lags between 5 min and 3 h is proposed. The method is based on the computation of experimental space–time variogram maps of radar reflectivity fields. Two new metrics for quantifying temporal innovation and predictability based on minimum semivariance values at different time lags are proposed. The method is applied to high-resolution composite radar reflectivity maps over the United States to study the evolution of 25 convective and 25 stratiform events during the warm season of 2014. Results show that the temporal innovation can be modeled as the sum of two exponential functions of time lag, with approximately 50% of the total innovation occurring over the first 60 min. The median predictable scales for convective events are on the order of 1.6 km at 5 min, 5 km at 15 min, and 12.7 km at 1 h. Furthermore, the optimal time lag for predicting future innovation, taking into account measurement uncertainty and natural variability, appears to be between 30 and 60 min.

Corresponding author address: Marc Schleiss, Civil and Environmental Engineering, Princeton University, E-Quad E316, Olden Street, Princeton, NJ 08540. E-mail: schleiss.marc@gmail.com
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Journal of Hydrometeorology

  • Bellon, A., and Zawadzki I. , 1994: Forecasting of hourly accumulations of precipitation by optimal extrapolation of radar maps. J. Hydrol., 157, 211–233, doi:10.1016/0022-1694(94)90106-6.

    • Search Google Scholar
    • Export Citation

  • Berenguer, M., Sempere-Torres D. , and Pegram G. G. S. , 2011: SBMcast: An ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation. J. Hydrol., 404, 226–240, doi:10.1016/j.jhydrol.2011.04.033.

    • Search Google Scholar
    • Export Citation

  • Berne, A., Delrieu G. , and Boudevillain B. , 2009: Variability of the spatial structure of intense Mediterranean precipitation. Adv. Water Resour., 32, 1031–1042, doi:10.1016/j.advwatres.2008.11.008.

    • Search Google Scholar
    • Export Citation

  • Bowler, N. E., Pierce C. E. , and Seed A. W. , 2006: STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Quart. J. Roy. Meteor. Soc., 132, 2127–2155, doi:10.1256/qj.04.100.

    • Search Google Scholar
    • Export Citation

  • Cloke, H. L., and Pappenberger F. , 2009: Ensemble flood forecasting: A review. J. Hydrol., 375, 613–626, doi:10.1016/j.jhydrol.2009.06.005.

    • Search Google Scholar
    • Export Citation

  • Collier, C. G., 2007: Flash flood forecasting: What are the limits of predictability? Quart. J. Roy. Meteor. Soc., 133, 3–23, doi:10.1002/qj.29.

    • Search Google Scholar
    • Export Citation

  • Cuo, L., Pagano T. C. , and Wang Q. J. , 2011: A review of quantitative precipitation forecasts and their use in short- to medium-range streamflow forecasting. J. Hydrometeor., 12, 713–728, doi:10.1175/2011JHM1347.1.

    • Search Google Scholar
    • Export Citation

  • Emmanuel, I., Andrieu H. , Leblois E. , and Flahaut B. , 2012: Temporal and spatial variability of rainfall at the urban hydrological scale. J. Hydrol., 430–431, 162–172, doi:10.1016/j.jhydrol.2012.02.013.

    • Search Google Scholar
    • Export Citation

  • Foresti, L., Panziera L. , Mandapaka P. V. , Germann U. , and Seed A. , 2015: Retrieval of analogue radar images for ensemble nowcasting of orographic rainfall. Meteor. Appl., 22, 141–155, doi:10.1002/met.1416.

    • Search Google Scholar
    • Export Citation

  • Germann, U., and Zawadzki I. , 2002: Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Mon. Wea. Rev., 130, 2859–2873, doi:10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Germann, U., and Zawadzki I. , 2004: Scale dependence of the predictability of precipitation from continental radar images. Part II: Probability forecasts. J. Appl. Meteor., 43, 74–89, doi:10.1175/1520-0450(2004)043<0074:SDOTPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Germann, U., Galli G. , Boscacci M. , and Bolliger M. , 2006a: Radar precipitation measurement in a mountainous region. Quart. J. Roy. Meteor. Soc., 132, 1669–1692, doi:10.1256/qj.05.190.

    • Search Google Scholar
    • Export Citation

  • Germann, U., Zawadzki I. , and Turner B. , 2006b: Predictability of precipitation from continental radar images. Part IV: Limits to prediction. J. Atmos. Sci., 63, 2092–2108, doi:10.1175/JAS3735.1.

    • Search Google Scholar
    • Export Citation

  • Germann, U., Berenguer M. , Sempere-Torres D. , and Zappa M. , 2009: REAL—Ensemble radar precipitation estimation for hydrology in a mountainous region. Quart. J. Roy. Meteor. Soc., 135, 445–456, doi:10.1002/qj.375.

    • Search Google Scholar
    • Export Citation

  • Grecu, M., and Krajewski W. F. , 2000: A large-sample investigation of statistical procedures for radar-based short-term quantitative precipitation forecasting. J. Hydrol., 239, 69–84, doi:10.1016/S0022-1694(00)00360-7.

    • Search Google Scholar
    • Export Citation

  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation

  • Journel, A. G., 1980: The lognormal approach to predicting local distributions of selective mining unit grades. Math. Geol., 12, 285–303, doi:10.1007/BF01029417.

    • Search Google Scholar
    • Export Citation

  • Krajewski, W. F., and Smith J. A. , 2002: Radar hydrology: Rainfall estimation. Adv. Water Resour., 25, 1387–1394, doi:10.1016/S0309-1708(02)00062-3.

    • Search Google Scholar
    • Export Citation

  • Lepioufle, J.-M., Leblois E. , and Creutin J.-D. , 2012: Variography of rainfall accumulation in presence of advection. J. Hydrol., 464–465, 494–504, doi:10.1016/j.jhydrol.2012.07.041.

    • Search Google Scholar
    • Export Citation

  • Lewis, H., and Coauthors, 2015: From months to minutes—Exploring the value of high-resolution rainfall observation and prediction during the UK winter storms of 2013/2014. Meteor. Appl., 22, 90–104, doi:10.1002/met.1493.

    • Search Google Scholar
    • Export Citation

  • Marsan, D., Schertzer D. , and Lovejoy S. , 1996: Causal space–time multifractal processes: Predictability and forecasting of rain fields. J. Geophys. Res., 101, 26 333–26 346, doi:10.1029/96JD01840.

    • Search Google Scholar
    • Export Citation

  • Mascaro, G., Vivoni E. R. , and Deidda R. , 2010: Implications of ensemble quantitative precipitation forecast errors on distributed streamflow forecasting. J. Hydrometeor., 11, 69–86, doi:10.1175/2009JHM1144.1.

    • Search Google Scholar
    • Export Citation

  • Onof, C., Northrop P. , Wheater H. S. , and Isham V. , 1996: Spatiotemporal storm structure and scaling property analysis for modeling. J. Geophys. Res., 101, 26 415–26 425, doi:10.1029/96JD01378.

    • Search Google Scholar
    • Export Citation

  • Panziera, L., Germann U. , Gabella M. , and Mandapaka P. V. , 2011: NORA–Nowcasting of Orographic Rainfall by means of Analogues. Quart. J. Roy. Meteor. Soc., 137, 2106–2123, doi:10.1002/qj.878.

    • Search Google Scholar
    • Export Citation

  • Panziera, L., James C. N. , and Germann U. , 2015: Mesoscale organization and structure of orographic precipitation producing flash floods in the Lago Maggiore region. Quart. J. Roy. Meteor. Soc., 141, 224–248, doi:10.1002/qj.2351.

    • Search Google Scholar
    • Export Citation

  • Paschalis, A., Molnar P. , Fatichi S. , and Burlando P. , 2013: A stochastic model for high-resolution space–time precipitation simulation. Water Resour. Res., 49, 8400–8417, doi:10.1002/2013WR014437.

    • Search Google Scholar
    • Export Citation

  • Paschalis, A., Fatichi S. , Molnar P. , Rimkus S. , and Burlando P. , 2014: On the effects of small scale space–time variability of rainfall on basin flood response. J. Hydrol., 514, 313–327, doi:10.1016/j.jhydrol.2014.04.014.

    • Search Google Scholar
    • Export Citation

  • Potvin, G., 1993: Space–time correlations and Taylor’s hypothesis for rainfall. M.S. thesis, Dept. of Physics, McGill University, 68 pp.

  • Radhakrishna, B., Zawadzki I. , and Fabry F. , 2012: Predictability of precipitation from continental radar images. Part V: Growth and decay. J. Atmos. Sci., 69, 3336–3349, doi:10.1175/JAS-D-12-029.1.

    • Search Google Scholar
    • Export Citation

  • Schleiss, M., and Smith J. A. , 2015: A method to estimate the 3D–time structure of the raindrop size distribution using radar and disdrometer data. J. Hydrometeor., 16, 1222–1242, doi:10.1175/JHM-D-14-0182.1.

    • Search Google Scholar
    • Export Citation

  • Schleiss, M., Chamoun S. , and Berne A. , 2014: Stochastic simulation of intermittent rainfall using the concept of dry drift. Water Resour. Res., 50, 2329–2349, doi:10.1002/2013WR014641.

    • Search Google Scholar
    • Export Citation

  • Seed, A. W., 2003: A dynamic and spatial scaling approach to advection forecasting. J. Appl. Meteor., 42, 381–388, doi:10.1175/1520-0450(2003)042<0381:ADASSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Taylor, G. I., 1938: The spectrum of turbulence. Proc. Roy. Soc. London, A164, 476–490, doi:10.1098/rspa.1938.0032.

  • Thorndahl, S., Smith J. A. , Baeck M. L. , and Krajewski W. F. , 2014: Analyses of the temporal and spatial structures of heavy rainfall from a catalog of high-resolution radar rainfall fields. Atmos. Res., 144, 111–125, doi:10.1016/j.atmosres.2014.03.013.

    • Search Google Scholar
    • Export Citation

  • Turner, B. J., Zawadzki I. , and Germann U. , 2004: Predictability of precipitation from continental radar images. Part III: Operational nowcasting implementation (MAPLE). J. Appl. Meteor., 43, 231–248, doi:10.1175/1520-0450(2004)043<0231:POPFCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tuttle, J. D., and Foote G. B. , 1990: Determination of the boundary layer airflow from a single Doppler radar. J. Atmos. Oceanic Technol., 7, 218–232, doi:10.1175/1520-0426(1990)007<0218:DOTBLA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zawadzki, I., Morneau J. , and Laprise R. , 1994: Predictability of precipitation patterns: An operational approach. J. Appl. Meteor., 33, 1562–1571, doi:10.1175/1520-0450(1994)033<1562:POPPAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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