On the Filtering Properties of Ensemble Averaging for Storm-Scale Precipitation Forecasts

Madalina Surcel Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

Search for other papers by Madalina Surcel in
Current site
Google Scholar
PubMed
Close
,
Isztar Zawadzki Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

Search for other papers by Isztar Zawadzki in
Current site
Google Scholar
PubMed
Close
, and
M. K. Yau Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada

Search for other papers by M. K. Yau in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The mean (ENM) of an ensemble of precipitation forecasts is generally more skillful than any of the members as verified against observations. A major reason is that the averaging filters out nonpredictable features on which the members disagree. Previous research showed that the nonpredictable features occur at small scales, in both numerical forecasts and Lagrangian persistence nowcasts. Hence, it is plausible that the unpredictable features filtered through ensemble averaging would also occur at small scales. In this study, the exact range of scales affected by averaging is determined by comparing the statistical properties of precipitation fields between the ENM and the individual members from a Storm-Scale Ensemble Forecasting (SSEF) system run during NOAA’s 2008 Hazardous Weather Testbed (HWT) Spring Experiment. The filtering effect of ensemble averaging results in a low-intensity bias for the ENM forecasts. It has been previously proposed to correct the ENM forecasts by recalibrating the intensities in the ENM using the probability density function (PDF) of rainfall values from the ensemble members. This procedure, probability matching (PM), leads to a new ensemble mean, the probability matched mean (PMM). Past studies have shown that the PMM appears more realistic and yields better skill as evaluated using traditional scores. However, the authors demonstrate here that despite the PMM having the same PDF of rainfall intensities as the ensemble members, the spectral structure and the spatial distribution of the precipitation field differs from that of the members. It is the lesser variability of the PMM fields at small scales that causes the better scores of the PMM relative to the ensemble members.

Corresponding author address: Madalina Surcel, 805 Sherbrooke St. W, 945, Montreal QC H3A 2K6, Canada. E-mail: madalina.surcel@mail.mcgill.ca

Abstract

The mean (ENM) of an ensemble of precipitation forecasts is generally more skillful than any of the members as verified against observations. A major reason is that the averaging filters out nonpredictable features on which the members disagree. Previous research showed that the nonpredictable features occur at small scales, in both numerical forecasts and Lagrangian persistence nowcasts. Hence, it is plausible that the unpredictable features filtered through ensemble averaging would also occur at small scales. In this study, the exact range of scales affected by averaging is determined by comparing the statistical properties of precipitation fields between the ENM and the individual members from a Storm-Scale Ensemble Forecasting (SSEF) system run during NOAA’s 2008 Hazardous Weather Testbed (HWT) Spring Experiment. The filtering effect of ensemble averaging results in a low-intensity bias for the ENM forecasts. It has been previously proposed to correct the ENM forecasts by recalibrating the intensities in the ENM using the probability density function (PDF) of rainfall values from the ensemble members. This procedure, probability matching (PM), leads to a new ensemble mean, the probability matched mean (PMM). Past studies have shown that the PMM appears more realistic and yields better skill as evaluated using traditional scores. However, the authors demonstrate here that despite the PMM having the same PDF of rainfall intensities as the ensemble members, the spectral structure and the spatial distribution of the precipitation field differs from that of the members. It is the lesser variability of the PMM fields at small scales that causes the better scores of the PMM relative to the ensemble members.

Corresponding author address: Madalina Surcel, 805 Sherbrooke St. W, 945, Montreal QC H3A 2K6, Canada. E-mail: madalina.surcel@mail.mcgill.ca
Save
  • Berenguer, M., M. Surcel, I. Zawadzki, M. Xue, and F. Kong, 2012: The diurnal cycle of precipitation from continental radar mosaics and numerical weather prediction models. Part II: Intercomparison among numerical models and with nowcasting. Mon. Wea. Rev., 140, 26892705.

    • Search Google Scholar
    • Export Citation
  • Brewster, K., M. Hu, M. Xue, and J. Gao, 2005: Efficient assimilation of radar data at high resolution for short-range numerical weather prediction. Preprints, WWRP Int. Symp. on Nowcasting and Very Short Range Forecasting, Whistler, BC, Canada, WMO, 3.06. [Available online at http://twister.ou.edu/papers/BrewsterWWRP_Nowcasting.pdf.]

  • Calheiros, R. V., and I. Zawadzki, 1987: Reflectivity rain rate relationships for radar hydrology in Brazil. J. Climate Appl. Meteor., 26, 118132.

    • Search Google Scholar
    • Export Citation
  • Chien, F.-C., and B. J.-D. Jou, 2004: MM5 ensemble mean precipitation forecasts in the Taiwan area for three early summer convective (mei-yu) seasons. Wea. Forecasting, 19, 735750.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Wea. Rev., 139, 14101418.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2012: An overview of the 2010 Hazardous Weather Testbed experimental forecast program spring experiment. Bull. Amer. Meteor. Soc., 93, 5574.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus, M. Xue, and F. Y. Kong, 2009: A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Wea. Forecasting, 24, 11211140.

    • Search Google Scholar
    • Export Citation
  • Demaria, E. M. C., D. A. Rodriguez, E. E. Ebert, P. Salio, F. Su, and J. B. Valdes, 2011: Evaluation of mesoscale convective systems in South America using multiple satellite products and an object-based approach. J. Geophys. Res., 116, D08103, doi:10.1029/2010JD015157.

    • Search Google Scholar
    • Export Citation
  • Denis, B., J. Cote, and R. Laprise, 2002: Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the discrete cosine transform (DCT). Mon. Wea. Rev., 130, 18121829.

    • Search Google Scholar
    • Export Citation
  • Du, J., J. McQueen, G. DiMego, Z. Toth, D. Jovic, B. Zhou, and H. Chuang, 2006: New dimension of NCEP Short-Range Ensemble Forecasting (SREF) system: Inclusion of WRF members. Preprints, WMO Expert Team Meeting on Ensemble Prediction Systems, Exeter, United Kingdom, WMO, 5 pp. [Available online at http://www.emc.ncep.noaa.gov/mmb/SREF/WMO06_full.pdf.]

  • Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 24612480.

    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., 2010: Application of object-based verification techniques to ensemble precipitation forecasts. Wea. Forecasting, 25, 144158.

    • Search Google Scholar
    • Export Citation
  • Gao, J., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457469.

    • 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, 28592873.

    • Search Google Scholar
    • Export Citation
  • Germann, U., and I. Zawadzki, 2003: Predictability of precipitation as a function of scale from large-scale radar composites. Preprints, 31st Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., 4B.8. [Available online at https://ams.confex.com/ams/32BC31R5C/techprogram/paper_64495.htm.]

  • 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, 7489.

    • Search Google Scholar
    • Export Citation
  • Gilleland, E., D. Ahijevych, B. G. Brown, B. Casati, and E. E. Ebert, 2009: Intercomparison of spatial forecast verification methods. Wea. Forecasting, 24, 14161430.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155167.

  • Hohenegger, C., and C. Schar, 2007a: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 17831793.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. Schar, 2007b: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 44674478.

    • Search Google Scholar
    • Export Citation
  • Hu, M., M. Xue, and K. Brewster, 2006a: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675698.

    • Search Google Scholar
    • Export Citation
  • Hu, M., M. Xue, J. Gao, and K. Brewster, 2006b: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699721.

    • Search Google Scholar
    • Export Citation
  • Janjic, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271285.

  • Kong, F., and Coauthors, 2008: Real-time storm-scale ensemble forecast 2008 spring experiment. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 12.3. [Available online at https://ams.confex.com/ams/24SLS/techprogram/paper_141827.htm.]

  • Lorenz, E. N., 1969: Predictability of a flow which possesses many scales of motion. Tellus, 21, 289307.

  • Lorenz, E. N., 1996: Predictability—A problem partly solved. Proc. Seminar on Predictability, Vol. I, Reading, United Kingdom, ECMWF, 119.

  • Radhakrishna, B., I. Zawadzki, and F. Fabry, 2012: Predictability of precipitation from continental radar images. Part V: Growth and decay. J. Atmos. Sci., 69, 33363349.

    • Search Google Scholar
    • Export Citation
  • Radhakrishna, B., I. Zawadzki, and F. Fabry, 2013: Postprocessing model-predicted rainfall fields in the spectral domain using phase information from radar observations. J. Atmos. Sci., 70, 11451159.

    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897.

    • Search Google Scholar
    • Export Citation
  • Seed, A. W., 2003: A dynamic and spatial scaling approach to advection forecasting. J. Appl. Meteor., 42, 381388.

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 2. NCAR Tech. Note NCAR/TN-475+STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Snook, N., M. Xue, and Y. Jung, 2012: Ensemble probabilistic forecasts of a tornadic mesoscale convective system from ensemble Kalman filter analyses using WSR-88D and CASA radar data. Mon. Wea. Rev., 140, 21262146.

    • 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, 231248.

    • Search Google Scholar
    • Export Citation
  • Warner, T. T., 2011: Numerical Weather and Climate Prediction. Cambridge University Press, 526 pp.

  • Weusthoff, T., D. Leuenberger, C. Keil, and G. C. Craig, 2011: Best member selection for convective-scale ensembles. Meteor. Z., 20, 153164.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. International Geophysics Series, Vol. 59, Academic Press, 467 pp.

  • Xue, M., D.-H. Wang, J.-D. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139170.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2008: CAPS realtime storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2008 Spring Experiment. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 12.2. [Available online at https://ams.confex.com/ams/24SLS/techprogram/paper_142036.htm.]

  • Zawadzki, I., 1973: Statistical properties of precipitation patterns. J. Appl. Meteor., 12, 459472.

  • Zhang, F. Q., C. Snyder, and R. Rotunno, 2002: Mesoscale predictability of the “surprise” snowstorm of 24–25 January 2000. Mon. Wea. Rev., 130, 16171632.

    • Search Google Scholar
    • Export Citation
  • Zhang, F. Q., A. M. Odins, and J. W. Nielsen-Gammon, 2006a: Mesoscale predictability of an extreme warm-season precipitation event. Wea. Forecasting, 21, 149166.

    • Search Google Scholar
    • Export Citation
  • Zhang, F. Q., N. Bei, C. C. Epifanio, R. Rotunno, and C. Snyder, 2006b: A multistage error-growth conceptual model for mesoscale predictability. Bull. Amer. Meteor. Soc., 87, 287288.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., K. Howard, and J. J. Gourley, 2005: Constructing three-dimensional multiple-radar reflectivity mosaics: Examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol., 22, 3042.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 575 118 6
PDF Downloads 438 71 3