Editorial Type:
Article
Article Type:
Research Article

An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-Level Wind Prediction

Jared A. Lee Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado, and Applied Research Laboratory, The Pennsylvania State University, State College, and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Walter C. Kolczynski Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Tyler C. McCandless Applied Research Laboratory, The Pennsylvania State University, State College, and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Sue Ellen Haupt Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado, and Applied Research Laboratory, The Pennsylvania State University, State College, and Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

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Print Publication:
01 Jul 2012
DOI:
https://doi.org/10.1175/MWR-D-11-00065.1
Page(s):
2270–2286
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Abstract

Ensembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncertainty. Limited computational resources constrain the size of ensembles, so choices must be made about which members to include. No known objective methodology exists to guide users in choosing which combinations of physics parameterizations to include in an NWP ensemble, however. This study presents such a methodology.

The authors build an NWP ensemble using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). This 24-member ensemble varies physics parameterizations for 18 randomly selected 48-h forecast periods in boreal summer 2009. Verification focuses on 2-m temperature and 10-m wind components at forecast lead times from 12 to 48 h. Various statistical guidance methods are employed for down-selection, calibration, and verification of the ensemble forecasts. The ensemble down-selection is accomplished with principal component analysis. The ensemble PDF is then statistically dressed, or calibrated, using Bayesian model averaging. The postprocessing techniques presented here result in a recommended down-selected ensemble that is about half the size of the original ensemble yet produces similar forecast performance, and still includes critical diversity in several types of physics schemes.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Current affiliation: Department of Meteorology, Naval Postgraduate School, Monterey, California.

Corresponding author address: Sue Ellen Haupt, National Center for Atmospheric Research, Research Applications Laboratory, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: haupt@ucar.edu

Abstract

Ensembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncertainty. Limited computational resources constrain the size of ensembles, so choices must be made about which members to include. No known objective methodology exists to guide users in choosing which combinations of physics parameterizations to include in an NWP ensemble, however. This study presents such a methodology.

The authors build an NWP ensemble using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). This 24-member ensemble varies physics parameterizations for 18 randomly selected 48-h forecast periods in boreal summer 2009. Verification focuses on 2-m temperature and 10-m wind components at forecast lead times from 12 to 48 h. Various statistical guidance methods are employed for down-selection, calibration, and verification of the ensemble forecasts. The ensemble down-selection is accomplished with principal component analysis. The ensemble PDF is then statistically dressed, or calibrated, using Bayesian model averaging. The postprocessing techniques presented here result in a recommended down-selected ensemble that is about half the size of the original ensemble yet produces similar forecast performance, and still includes critical diversity in several types of physics schemes.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Current affiliation: Department of Meteorology, Naval Postgraduate School, Monterey, California.

Corresponding author address: Sue Ellen Haupt, National Center for Atmospheric Research, Research Applications Laboratory, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: haupt@ucar.edu
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  • Bao, L., T. Gneiting, E. P. Grimit, P. Guttorp, and A. E. Raftery, 2010: Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction. Mon. Wea. Rev., 138, 1811–1821.

    • Search Google Scholar
    • Export Citation

  • Berrocal, V. J., A. E. Raftery, and T. Gneiting, 2007: Combining spatial statistical and ensemble information in probabilistic weather forecasts. Mon. Wea. Rev., 135, 1386–1402.

    • Search Google Scholar
    • Export Citation

  • Bowler, N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703–722.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., W. A. Gallus Jr., and T.-C. Chen, 2008: Contributions of mixed physics versus perturbed initial/lateral boundary conditions to ensemble-based precipitation forecast skill. Mon. Wea. Rev., 136, 2140–2156.

    • Search Google Scholar
    • Export Citation

  • Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367–374.

  • Dee, D. P., and A. M. da Silva, 1999: Maximum-likelihood estimation of forecast and observation error covariance parameters. Part I: Methodology. Mon. Wea. Rev., 127, 1822–1834.

    • Search Google Scholar
    • Export Citation

  • Delle Monache, L., A. Fournier, T. M. Hopson, Y. Liu, B. Mahoney, G. Roux, and T. Warner, 2011: Kalman filter, analog and wavelet postprocessing in the NCAR-Xcel operational wind-energy forecasting system. Extended Abstracts, Second Conf. on Weather, Climate, and the New Energy Economy, Seattle, WA, Amer. Meteor. Soc., 4C-2. [Available online at http://ams.confex.com/ams/91Annual/webprogram/Paper186510.html.]

  • Du, J., and Coauthors, 2009: Recent upgrade of NCEP short-range ensemble forecast (SREF) system. Preprints, 19th Conf. on Numerical Weather Prediction/23rd Conf. on Weather Analysis and Forecasting, Omaha, NE, Amer. Meteor. Soc., 4A.4. [Available online at http://ams.confex.com/ams/23WAF19NWP/techprogram/paper_153264.htm.]

  • Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range forecasting. Wea. Forecasting, 20, 328–350.

  • Efron, B., 1979: Bootstrap methods: Another look at the jackknife. Ann. Stat., 7, 1–26.

  • Elmore, K. L., M. E. Baldwin, and D. M. Schultz, 2006: Field significance revisited: Spatial bias errors in forecasts as applied to the Eta Model. Mon. Wea. Rev., 134, 519–531.

    • Search Google Scholar
    • Export Citation

  • Fraley, C., A. E. Raftery, and T. Gneiting, 2010: Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging. Mon. Wea. Rev., 138, 190–202.

    • Search Google Scholar
    • Export Citation

  • Fujita, T., D. J. Stensrud, and D. C. Dowell, 2007: Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties. Mon. Wea. Rev., 135, 1846–1868.

    • Search Google Scholar
    • Export Citation

  • Gneiting, T., A. E. Raftery, A. H. Westveld III, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098–1118.

    • Search Google Scholar
    • Export Citation

  • Gneiting, T., K. Larson, K. Westrick, M. C. Genton, and E. Aldrich, 2006: Calibrated probabilistic forecasting at the Stateline Wind Energy Center: The regime-switching space-time method. J. Amer. Stat. Assoc., 101, 968–979.

    • Search Google Scholar
    • Export Citation

  • Grimit, E. P., and C. F. Mass, 2007: Measuring the ensemble spread-error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135, 203–221.

    • Search Google Scholar
    • Export Citation

  • Hacker, J. P., and Coauthors, 2011: The U.S. Air Force Weather Agency’s mesoscale ensemble: Scientific description and performance results. Tellus, 63A, 625–641.

    • Search Google Scholar
    • Export Citation

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

  • Houtekamer, P. L., 1993: Global and local skill forecasts. Mon. Wea. Rev., 121, 1834–1846.

  • Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225–1242.

    • Search Google Scholar
    • Export Citation

  • Jolliffe, I. T., 2002: Principal Component Analysis. 2nd ed. Springer, 487 pp.

  • Jones, M. S., B. A. Colle, and J. S. Tongue, 2007: Evaluation of a mesoscale short-range ensemble forecast system over the northeast United States. Wea. Forecasting, 22, 36–55.

    • Search Google Scholar
    • Export Citation

  • Kann, A., C. Wittmann, Y. Wang, and X. Ma, 2009: Calibrating 2-m temperature of limited-area ensemble forecasts using high-resolution analysis. Mon. Wea. Rev., 137, 3373–3387.

    • Search Google Scholar
    • Export Citation

  • Kolczynski, W. C., D. R. Stauffer, S. E. Haupt, and A. Deng, 2009: Ensemble variance calibration for representing meteorological uncertainty for atmospheric transport and dispersion modeling. J. Appl. Meteor. Climatol., 48, 2001–2021.

    • Search Google Scholar
    • Export Citation

  • Kolczynski, W. C., D. R. Stauffer, S. E. Haupt, N. S. Altman, and A. Deng, 2011: Investigation of ensemble variance as a measure of true forecast variance. Mon. Wea. Rev., 139, 3954–3963.

    • Search Google Scholar
    • Export Citation

  • Lee, J. A., L. J. Peltier, S. E. Haupt, J. C. Wyngaard, D. R. Stauffer, and A. Deng, 2009: Improving SCIPUFF dispersion forecasts with NWP ensembles. J. Appl. Meteor. Climatol., 48, 2305–2319.

    • Search Google Scholar
    • Export Citation

  • Lewellen, W. S., and R. I. Sykes, 1989: Meteorological data needs for modeling air quality uncertainties. J. Atmos. Oceanic Technol., 6, 759–768.

    • Search Google Scholar
    • Export Citation

  • Liu, Y., and Coauthors, 2011: Wind energy forecasting with the NCAR RTFDDA and ensemble RTFDDA systems. Extended Abstracts, Second Conf. on Weather, Climate, and the New Energy Economy, Seattle, WA, Amer. Meteor. Soc., WC-2. [Available online at http://ams.confex.com/ams/91Annual/webprogram/Paper186591.html.]

  • Livezey, R. E., and W. Y. Chen, 1983: Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev., 111, 46–59.

    • Search Google Scholar
    • Export Citation

  • Mierswa, I., M. Wurst, R. Klinkenberg, M. Scholz, and T. Euler, 2006: YALE: Rapid prototyping for complex data mining tasks. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), T. Eliassi-Rad et al., Eds., ACM Press, 935–940.

  • NCAR, 2011: Weather Research and Forecasting ARW version 3 modeling system user’s guide. NCAR Mesoscale and Microscale Meteorology Division, 362 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3/contents.html.]

  • Raftery, A. E., F. Balabdaoui, T. Gneiting, and M. Polakowski, 2003: Using Bayesian model averaging to calibrate forecast ensembles. Tech. Rep. 440, Dept. of Statistics, University of Washington, 28 pp. [Available online at http://www.stat.washington.edu/research/reports/2003/tr440.pdf.]

  • Raftery, A. E., F. Balabdaoui, T. Gneiting, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155–1174.

    • Search Google Scholar
    • Export Citation

  • Roulston, M. S., and L. A. Smith, 2003: Combining dynamical and statistical ensembles. Tellus, 55A, 16–30.

  • Seidman, A. N., 1981: Averaging techniques in long-range weather forecasting. Mon. Wea. Rev., 109, 1367–1379.

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp.

  • Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2010: Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J. Amer. Stat. Assoc., 105, 25–35.

    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., J.-W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 2077–2107.

    • Search Google Scholar
    • Export Citation

  • Sykes, R. I., S. F. Parker, and D. S. Henn, 2004: SCIPUFF version 2.0, technical documentation. ARAP Tech. Rep. 727, Titan Corporation, Princeton, NJ, 284 pp.

  • Warner, T. T., R.-S. Sheu, J. F. Bowers, R. I. Sykes, G. C. Dodd, and D. S. Henn, 2002: Ensemble simulations with coupled atmospheric dynamic and dispersion models: Illustrating uncertainties in dosage simulations. J. Appl. Meteor., 41, 488–504.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 626 pp.

  • Witten, I. H., and E. Frank, 2005: Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. Morgan Kaufmann, 525 pp.

  • Wyngaard, J. C., 2010: Turbulence in the Atmosphere. Cambridge University Press, 393 pp.

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