Editorial Type:
Article
Article Type:
Research Article

Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble

Aaron Johnson School of Meteorology, University of Oklahoma, and Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Aaron Johnson in
Current site
Google Scholar
PubMed Close
and
Xuguang Wang School of Meteorology, University of Oklahoma, and Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Xuguang Wang in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Sep 2012
DOI:
https://doi.org/10.1175/MWR-D-11-00356.1
Page(s):
3054–3077
Restricted access

Abstract

Neighborhood and object-based probabilistic precipitation forecasts from a convection-allowing ensemble are verified and calibrated. Calibration methods include logistic regression, one- and two-parameter reliability-based calibration, and cumulative distribution function (CDF)-based bias adjustment. Newly proposed object-based probabilistic forecasts for the occurrence of a forecast object are derived from the percentage of ensemble members with a matching object. Verification and calibration of single- and multimodel subensembles are performed to explore the effect of using multiple models.

The uncalibrated neighborhood-based probabilistic forecasts have skill minima during the afternoon convective maximum. Calibration generally improves the skill, especially during the skill minima, resulting in positive skill. In general all calibration methods perform similarly, with a slight advantage of logistic regression (one-parameter reliability based) calibration for 1-h (6 h) accumulations.

The uncalibrated object-based probabilistic forecasts are, in general, less skillful than the uncalibrated neighborhood-based probabilistic forecasts. Object-based calibration also results in positive skill at all lead times. For object-based calibration the skill is significantly different among the calibration methods, with the logistic regression performing the best and CDF-based bias adjustment performing the worst.

For both the neighborhood and object-based probabilistic forecasts, the impact of using 10 or 25 days of training data for calibration is generally small and is most significant for the two-parameter reliability-based method. An uncalibrated Advanced Research Weather Research and Forecasting Model (ARW-WRF) subensemble is significantly more skillful than an uncalibrated WRF Nonhydrostatic Mesoscale Model (NMM) subensemble. The difference is reduced by calibration. The multimodel subensemble only shows an advantage for the neighborhood-based forecasts beyond 1-day lead time and shows no advantage for the object-based forecasts.

Corresponding author address: Dr. Xuguang Wang, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: xuguang.wang@ou.edu

Abstract

Neighborhood and object-based probabilistic precipitation forecasts from a convection-allowing ensemble are verified and calibrated. Calibration methods include logistic regression, one- and two-parameter reliability-based calibration, and cumulative distribution function (CDF)-based bias adjustment. Newly proposed object-based probabilistic forecasts for the occurrence of a forecast object are derived from the percentage of ensemble members with a matching object. Verification and calibration of single- and multimodel subensembles are performed to explore the effect of using multiple models.

The uncalibrated neighborhood-based probabilistic forecasts have skill minima during the afternoon convective maximum. Calibration generally improves the skill, especially during the skill minima, resulting in positive skill. In general all calibration methods perform similarly, with a slight advantage of logistic regression (one-parameter reliability based) calibration for 1-h (6 h) accumulations.

The uncalibrated object-based probabilistic forecasts are, in general, less skillful than the uncalibrated neighborhood-based probabilistic forecasts. Object-based calibration also results in positive skill at all lead times. For object-based calibration the skill is significantly different among the calibration methods, with the logistic regression performing the best and CDF-based bias adjustment performing the worst.

For both the neighborhood and object-based probabilistic forecasts, the impact of using 10 or 25 days of training data for calibration is generally small and is most significant for the two-parameter reliability-based method. An uncalibrated Advanced Research Weather Research and Forecasting Model (ARW-WRF) subensemble is significantly more skillful than an uncalibrated WRF Nonhydrostatic Mesoscale Model (NMM) subensemble. The difference is reduced by calibration. The multimodel subensemble only shows an advantage for the neighborhood-based forecasts beyond 1-day lead time and shows no advantage for the object-based forecasts.

Corresponding author address: Dr. Xuguang Wang, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: xuguang.wang@ou.edu
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Alhamed, A., S. Lakshmivarahan, and D. J. Stensrud, 2002: Cluster analysis of multimodel ensemble data from SAMEX. Mon. Wea. Rev., 130, 226–256.

    • Search Google Scholar
    • Export Citation

  • Aligo, E. A., W. A. Gallus, and M. Segal, 2007: Summer rainfall forecast spread in an ensemble initialized with different soil moisture analyses. Wea. Forecasting, 22, 299–314.

    • Search Google Scholar
    • Export Citation

  • Atger, F., 1999: The skill of ensemble prediction systems. Mon. Wea. Rev., 127, 1941–1953.

  • Atger, F., 2003: Spatial and interannual variability of the reliability of ensemble-based probabilistic forecasts: Consequences for calibration. Mon. Wea. Rev., 131, 1509–1523.

    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., G. A. Grell, J. M. Brown, T. G. Smirnova, and R. Bleck, 2004: Mesoscale weather prediction with the RUC hybrid isentropic-terrain-following coordinate model. Mon. Wea. Rev., 132, 473–494.

    • Search Google Scholar
    • Export Citation

  • Berner, J., S.-Y. Ha, J. P. Hacker, A. Fournier, and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 1972–1995.

    • Search Google Scholar
    • Export Citation

  • Bouallegue, Z. B., S. Theis, and C. Gebhardt, 2011: From verification results to probabilistic products: Spatial techniques applied to ensemble forecasting. Abstracts of the Fifth International Verification Methods Workshop, 1–7 December 2011, Melbourne, Australia, CAWCR Tech. Rep. 046, 4–5. [Available online at http://www.cawcr.gov.au/publications/technicalreports/CTR_046.pdf.]

  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 1–3.

  • Candille, G., S. Beauregard, and N. Gagnon, 2010: Bias correction and multiensemble in the NAEFS context or wow to get a “free calibration” through a multiensemble approach. Mon. Wea. Rev., 138, 4268–4281.

    • Search Google Scholar
    • Export Citation

  • Carley, J. R., B. R. J. Schwedler, M. E. Baldwin, R. J. Trapp, J. Kwiatkowski, J. Logsdon, and S. J. Weiss, 2011: A proposed model-based methodology for feature-specific prediction for high-impact weather. Wea. Forecasting, 26, 243–249.

    • 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, 1410–1418.

    • Search Google Scholar
    • Export Citation

  • Davis, C., B. Brown, and R. Bullock, 2006: Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 1772–1784.

    • Search Google Scholar
    • Export Citation

  • Davis, C., B. Brown, R. Bullock, and J. Halley-Gotway, 2009: The method for object-based diagnostic evaluation (MODE) applied to numerical forecasts from the 2005 NSSL/SPC Spring Program. Wea. Forecasting, 24, 1252–1267.

    • Search Google Scholar
    • Export Citation

  • Done, J., C. Davis, and M. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecast (WRF) model. Atmos. Sci. Lett., 5, 110–117.

    • 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. Proc. WMO Expert Team Meeting on Ensemble Prediction System, Exeter, United Kingdom, WMO. [Available online at http://www.emc.ncep.noaa.gov/mmb/SREF/WMO06_full.pdf.]

  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107.

    • Search Google Scholar
    • Export Citation

  • Ebert, E., 2009: Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 1498–1510.

  • Ebert, E., and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179–202.

    • Search Google Scholar
    • Export Citation

  • Eckel, F. A., and M. K. Walters, 1998: Calibrated probabilistic quantitative precipitation forecasts based on the MRF ensemble. Wea. Forecasting, 13, 1132–1147.

    • Search Google Scholar
    • Export Citation

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

  • Ek, M. B., K. E. Mitchell, Y. Lin, P. Grunmann, E. Rogers, G. Gayno, and V. Koren, 2003: Implementation of upgraded Noah land-surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation

  • Ferrier, B. S., 1994: A double-moment multiple-phase four-class bulk ice scheme. Part I: Description. J. Atmos. Sci., 51, 249–280.

  • Fortin, V., A.-C. Favre, and M. Saïd, 2006: Probabilistic forecasting from ensemble prediction systems: Improving upon the best-member method by using a different weight and dressing kernel for each member. Quart. J. Roy. Meteor. Soc., 132, 1349–1369.

    • Search Google Scholar
    • Export Citation

  • Gallus, W. A., Jr., 2010: Application of object-based verification techniques to ensemble precipitation forecasts. Wea. Forecasting, 25, 144–158.

    • Search Google Scholar
    • Export Citation

  • Gallus, W. A., Jr., and J. F. Bresch, 2006: Comparison of impacts of WRF dynamic core, physics package, and initial conditions on warm season rainfall forecasts. Mon. Wea. Rev., 134, 2632–2641.

    • Search Google Scholar
    • Export Citation

  • Gao, J.-D., 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, 457–469.

    • 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, 1416–1430.

    • 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

  • Hacker, J. P., and Coauthors, 2011: The U.S. Air Force Weather Agency’s mesoscale ensemble: Scientific description and performance results. Tellus, 63A, 1–17.

    • Search Google Scholar
    • Export Citation

  • Hagedorn, R., T. M. Hamill, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: Two-meter temperatures. Mon. Wea. Rev., 136, 2608–2619.

    • Search Google Scholar
    • Export Citation

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

  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550–560.

  • Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta–RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312–1327.

  • Hamill, T. M., and S. J. Colucci, 1998: Evaluation of Eta–RSM ensemble probabilistic precipitation forecasts. Mon. Wea. Rev., 126, 711–724.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Mon. Wea. Rev., 134, 3209–3229.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and J. Juras, 2007: Measuring forecast skill: Is it real skill or is it the varying climatology? Quart. J. Roy. Meteor. Soc., 132, 2905–2923.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble reforecasting: Improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, 1434–1447.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: Precipitation. Mon. Wea. Rev., 136, 2620–2632.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120.

    • Search Google Scholar
    • Export Citation

  • Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the SAMEX ’98 ensemble forecasts. Mon. Wea. Rev., 129, 73–91.

    • Search Google Scholar
    • Export Citation

  • Hu, M., M. Xue, and K. Brewster, 2006: 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, 675–698.

    • Search Google Scholar
    • Export Citation

  • Janjic, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945.

    • Search Google Scholar
    • Export Citation

  • Janjic, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271–285.

  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2011a: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 3673–3693.

    • Search Google Scholar
    • Export Citation

  • Johnson, A., X. Wang, M. Xue, and F. Kong, 2011b: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Season-long ensemble clustering and implication for optimal ensemble design. Mon. Wea. Rev., 139, 3694–3710.

    • Search Google Scholar
    • Export Citation

  • Johnson, C., and R. Swinbank, 2009: Medium-range multi-model ensemble combination and calibration. Quart. J. Roy. Meteor. Soc., 135, 777–794.

    • Search Google Scholar
    • Export Citation

  • Kass, R. E., and A. E. Raftery, 1995: Bayes factors. J. Amer. Stat. Soc., 90, 773–795.

  • Kong, F., and Coauthors, 2007: Preliminary analysis on the real-time storm-scale ensemble forecasts produced as a part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 3B.2. [Available online at https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124667.htm.]

  • Kong, F., and Coauthors, 2008: Real-time storm-scale ensemble forecasting during the 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.]

  • Kong, F., and Coauthors, 2009: A real-time storm-scale ensemble forecast system: 2009 Spring Experiment. Proc. 10th WRF Users’ Workshop, Boulder, CO, NCAR, 3B.7.

  • Kong, F., and Coauthors, 2010: Evaluation of CAPS multi-model storm-scale ensemble forecast for the NOAA HWT 2010 Spring Experiment. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P4.18. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_175822.htm.]

  • Krzysztofowicz, R., and A. A. Sigrest, 1999: Calibration of probabilistic quantitative precipitation forecasts. Wea. Forecasting, 14, 427–442.

    • Search Google Scholar
    • Export Citation

  • Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the absorption of solar radiation in the earth’s atmosphere. J. Atmos. Sci., 31, 118–133.

    • Search Google Scholar
    • Export Citation

  • Lin, Y., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092.

    • Search Google Scholar
    • Export Citation

  • Mittermaier, M., 2007: Improving short-range high-resolution model precipitation forecast skill using time-lagged ensembles. Quart. J. Roy. Meteor. Soc., 133, 1487–1500.

    • Search Google Scholar
    • Export Citation

  • Mittermaier, M., and N. Roberts, 2010: Intercomparison of spatial forecast verification methods: Identifying skillful spatial scales using the fractions skill score. Wea. Forecasting, 25, 343–354.

    • Search Google Scholar
    • Export Citation

  • Mittermaier, M., N. Roberts, and S. A. Thompson, 2012: A long-term assessment of precipitation forecast skill using the fractions skill score. Meteor. Appl., doi:10.1002/met.296, in press.

    • Search Google Scholar
    • Export Citation

  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595–600.

  • Noh, Y., W. G. Cheon, S. Y. Hong, and S. Raasch, 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 421–427.

    • Search Google Scholar
    • Export Citation

  • Nutter, P., D. Stensrud, and M. Xue, 2004: Effects of coarsely resolved and temporally interpolated lateral boundary conditions on the dispersion of limited-area ensemble forecasts. Mon. Wea. Rev., 132, 2358–2377.

    • Search Google Scholar
    • Export Citation

  • Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. J. Shutts, M. Steinheimer, and A. Weisheimer, 2009: Stochastic parametrization and model uncertainty. ECMWF Tech. Memo. 598, 42 pp. [Available online at http://www.ecmwf.int/publications/library/ecpublications/_pdf/tm/501-600/tm598.pdf.]

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

    • Search Google Scholar
    • Export Citation

  • Richardson, D. S., 2001: Ensembles using multiple models and analyses. Quart. J. Roy. Meteor. Soc., 127, 1847–1864.

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

  • Schaffer, C. J., W. A. Gallus, and M. Segal, 2011: Improving probabilistic ensemble forecasts of convection through the application of QPF–POP relationships. Wea. Forecasting, 26, 319–336.

    • Search Google Scholar
    • Export Citation

  • Schwartz, C. S., and Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263–280.

    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note NCAR/TN-468+STR, 88 pp. [Available from UCAR Communications, P.O. Box 3000, Boulder, CO 80307.]

  • Sloughter, J. M., A. E. Raftery, T. Gneiting, and C. Fraley, 2007: Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon. Wea. Rev., 135, 3209–3220.

    • Search Google Scholar
    • Export Citation

  • SPC, cited 2011: Probabilistic outlook information. NWS Storm Prediction Center. [Available online at http://www.spc.noaa.gov/products/outlook/probinfo.html.]

  • Stensrud, D. J., and N. Yussouf, 2003: Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev., 131, 2510–2524.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Tao, W.-K., and Coauthors, 2003: Microphysics, radiation, and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97–137.

    • Search Google Scholar
    • Export Citation

  • Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257–268.

    • Search Google Scholar
    • Export Citation

  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115.

    • Search Google Scholar
    • Export Citation

  • Toth, Z., Y. Zhu, and T. Marchok, 2001: The use of ensembles to identify forecasts with small and large uncertainty. Wea. Forecasting, 16, 463–477.

    • Search Google Scholar
    • Export Citation

  • Voisin, N., J. C. Schaake, and D. P. Lettenmaier, 2010: Calibration and downscaling methods for quantitative ensemble precipitation forecasts. Wea. Forecasting, 25, 1603–1627.

    • Search Google Scholar
    • Export Citation

  • Wandishin, M. S., S. L. Mullen, D. J. Stensrud, and H. E. Brooks, 2001: Evaluation of a short-range multimodel ensemble system. Mon. Wea. Rev., 129, 729–747.

    • Search Google Scholar
    • Export Citation

  • Wang, X., and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 1140–1158.

    • Search Google Scholar
    • Export Citation

  • Wang, X., and C. H. Bishop, 2005: Improvement of ensemble reliability with a new dressing kernel. Quart. J. Roy. Meteor. Soc., 131, 965–986.

    • Search Google Scholar
    • Export Citation

  • Wang, X., C. H. Bishop, and S. J. Julier, 2004: Which is better, an ensemble of positive–negative pairs or a centered spherical simplex ensemble? Mon. Wea. Rev., 132, 1590–1605.

    • Search Google Scholar
    • Export Citation

  • Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116–5131.

    • Search Google Scholar
    • Export Citation

  • Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132–5147.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2002: Smoothing forecast ensembles with fitted probability distributions. Quart. J. Roy. Meteor. Soc., 128, 2821–2836.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences: An Introduction. 2nd ed. Academic Press, 467 pp.

  • Wilks, D. S., and T. M. Hamill, 2007: Comparison of ensemble-MOS methods using GFS reforecasts. Mon. Wea. Rev., 135, 2379–2390.

  • Wood, A. W., E. P. Maurer, A. Kumar, and D. P. Lettenmaier, 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation

  • Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161–193.

    • Search Google Scholar
    • Export Citation

  • 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, 139–170.

    • Search Google Scholar
    • Export Citation

  • Xue, M., and Coauthors, 2007: CAPS realtime storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B.1. [Available online at https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124587.htm.]

  • 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.]

  • Xue, M., and Coauthors, 2009: CAPS realtime 4-km multi-model convection-allowing ensemble and 1-km convection-resolving forecasts from the NOAA Hazardous Weather Testbed 2009 Spring Experiment. Preprints, 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 16A.2. [Available online at https://ams.confex.com/ams/23WAF19NWP/techprogram/paper_154323.htm.]

  • Xue, M., and Coauthors, 2010: CAPS realtime storm scale ensemble and high resolution forecasts for the NOAA Hazardous Weather Testbed 2010 Spring Experiment. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 7B.3. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176056.htm.]

  • Yuan, H., X. Gao, S. L. Mullen, S. Sorooshian, J. Du, and H. H. Juang, 2007: Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network. Wea. Forecasting, 22, 1287–1303.

    • Search Google Scholar
    • Export Citation

  • Yuan, H., J. A. McGinley, P. J. Schultz, C. J. Anderson, and C. Lu, 2008: Short-range precipitation forecasts from time-lagged multimodel ensembles during the HMT-West-2006 campaign. J. Hydrometeor., 9, 477–491.

    • Search Google Scholar
    • Export Citation

  • Yussouf, N., D. J. Stensrud, and S. Lakshmivarahan, 2004: Cluster analysis of multimodel ensemble data over New England. Mon. Wea. Rev., 132, 2452–2462.

    • 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, 30–42.

    • Search Google Scholar
    • Export Citation

  • Zhu, Y., and Z. Toth, 2000: Calibration of probabilistic quantitative precipitation forecasts. Preprints, Annual GCIP PI Meeting, Potomac, MD, GEWEX Continental-Scale International Project. [Available online at http://www.emc.ncep.noaa.gov/gmb/yzhu/html/pub.html.]

  • Zhu, Y., G. Yyengar, Z. Toth, S. M. Tracton, and T. Marchok, 1996: Objective evaluation of the NCEP global ensemble forecasting system. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., J79–J82.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 457 120 9
PDF Downloads 322 72 4