Discrete Postprocessing of Total Cloud Cover Ensemble Forecasts

Stephan Hemri Heidelberg Institute for Theoretical Studies, Heidelberg, Germany

Search for other papers by Stephan Hemri in
Current site
Google Scholar
PubMed
Close
,
Thomas Haiden European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Search for other papers by Thomas Haiden in
Current site
Google Scholar
PubMed
Close
, and
Florian Pappenberger European Centre for Medium-Range Weather Forecasts, Reading, and School of Geographical Sciences, University of Bristol, Bristol, United Kingdom

Search for other papers by Florian Pappenberger in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model.

Denotes Open Access content.

Corresponding author address: Stephan Hemri, CST Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. E-mail: stephan.hemri@h-its.org

Abstract

This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model.

Denotes Open Access content.

Corresponding author address: Stephan Hemri, CST Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany. E-mail: stephan.hemri@h-its.org
Save
  • Agresti, A., and M. Kateri, 2011: Categorical data analysis. International Encyclopedia of Statistical Science, M. Lovric, Ed., Springer, 206–208.

  • American Meteorological Society, 2015: Cloud cover. Glossary of Meteorology. [Available online at http://glossary.ametsoc.org/wiki/Cloud_cover.]

  • Ananth, C. V., and D. G. Kleinbaum, 1997: Regression models for ordinal responses: A review of methods and applications. Int. J. Epidemiol., 26, 13231333.

    • Search Google Scholar
    • Export Citation
  • Applequist, S., G. E. Gahrs, R. L. Pfeffer, and X.-F. Niu, 2002: Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecasting, 17, 783799, doi:10.1175/1520-0434(2002)017<0783:COMFPQ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ben Bouallègue, Z., 2013: Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms. Wea. Forecasting, 28, 515524, doi:10.1175/WAF-D-12-00062.1.

    • Search Google Scholar
    • Export Citation
  • Bonferroni, C. E., 1936: Teoria statistica delle classi e calcolo delle probabilità (Statistical theory of classes and calculating probability). Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62.

  • Buizza, R., J.-R. Bidlot, N. Wedi, M. Fuentes, M. Hamrud, G. Holt, and F. Vitart, 2007: The new ECMWF VAREPS (variable resolution ensemble prediction system). Quart. J. Roy. Meteor. Soc., 133, 681695, doi:10.1002/qj.75.

    • Search Google Scholar
    • Export Citation
  • Canty, A., and B. Ripley, 2014: boot: Bootstrap R (S-Plus) Functions, version 1.3-13. R package, accessed 3 November 2015. [Available online at http://CRAN.R-project.org/package=boot.]

  • Chmielecki, R. M., and A. E. Raftery, 2011: Probabilistic visibility forecasting using Bayesian model averaging. Mon. Wea. Rev., 139, 16261636, doi:10.1175/2010MWR3516.1.

    • Search Google Scholar
    • Export Citation
  • Dawid, A. P., 1984: Present position and potential developments: Some personal views: Statistical theory: The prequential approach. J. Roy. Stat. Soc., 147A, 278292, doi:10.2307/2981683.

    • Search Google Scholar
    • Export Citation
  • Diak, G. R., M. C. Anderson, W. L. Bland, J. M. Norman, J. M. Mecikalski, and R. M. Aune, 1998: Agricultural management decision aids driven by real-time satellite data. Bull. Amer. Meteor. Soc., 79, 13451355, doi:10.1175/1520-0477(1998)079<1345:AMDADB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Diebold, F. X., T. A. Gunther, and A. S. Tay, 1998: Evaluating density forecasts with applications to financial risk management. Int. Econ. Rev., 39, 863883, doi:10.2307/2527342.

    • Search Google Scholar
    • Export Citation
  • Dixon, H. G., M. Lagerlund, M. J. Spittal, D. J. Hill, S. J. Dobbinson, and M. A. Wakefield, 2008: Use of sun-protective clothing at outdoor leisure settings from 1992 to 2002: Serial cross-sectional observation survey. Cancer Epidemiol. Biomarkers Prev., 17, 428434, doi:10.1158/1055-9965.EPI-07-0369.

    • Search Google Scholar
    • Export Citation
  • Epstein, E. S., 1969: A scoring system for probability forecasts of ranked categories. J. Appl. Meteor., 8, 985987, doi:10.1175/1520-0450(1969)008<0985:ASSFPF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., and A. E. Raftery, 2007: Strictly proper scoring rules, prediction, and estimation. J. Amer. Stat. Assoc., 102, 359378, doi:10.1198/016214506000001437.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 10981118, doi:10.1175/MWR2904.1.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., F. Balabdoui, and A. E. Raftery, 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69B, 243268, doi:10.1111/j.1467-9868.2007.00587.x.

    • Search Google Scholar
    • Export Citation
  • Haiden, T., and J. Trentmann, 2016: Verification of cloudiness and radiation forecasts in the greater Alpine region. Meteor. Z., 25, 315, doi:10.1127/metz/2015/0630.

    • Search Google Scholar
    • Export Citation
  • Haiden, T., R. Forbes, M. Ahlgrimm, and A. Bozzo, 2015: The skill of ECMWF cloudiness forecasts. ECMWF Newsletter, No. 143, ECMWF, Reading, United Kingdom,1419.

  • Hamill, T. M., 2012: Verification of TIGGE multimodel and ECMWF reforecast-calibrated probabilistic precipitation forecasts over the contiguous United States. Mon. Wea. Rev., 140, 22322252, doi:10.1175/MWR-D-11-00220.1.

    • 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, 14341447, doi:10.1175/1520-0493(2004)132<1434:ERIMFS>2.0.CO;2.

    • 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, 26202632, doi:10.1175/2007MWR2411.1.

    • Search Google Scholar
    • Export Citation
  • Hemri, S., M. Scheuerer, F. Pappenberger, K. Bogner, and T. Haiden, 2014: Trends in the predictive performance of raw ensemble weather forecasts. Geophys. Res. Lett., 41, 91979205, doi:10.1002/2014GL062472.

    • Search Google Scholar
    • Export Citation
  • Köhler, M., 2005: Improved prediction of boundary layer clouds. ECMWF Newsletter, No. 104, ECMWF, Reading, United Kingdom, 1822.

  • Künsch, H. R., 1989: The jackknife and the bootstrap for general stationary observations. Ann. Stat., 17, 12171241, doi:10.1214/aos/1176347265.

    • Search Google Scholar
    • Export Citation
  • McClung, D. M., 2002: The elements of applied avalanche forecasting. Part II: The physical issues and the rules of applied avalanche forecasting. Nat. Hazards, 26, 131146, doi:10.1023/A:1015604600361.

    • Search Google Scholar
    • Export Citation
  • McCullagh, P., 1980: Regression model for ordinal data (with discussion). J. Roy. Stat. Soc., 42B, 109142.

  • Messner, J. W., G. J. Mayr, D. S. Wilks, and A. Zeileis, 2014: Extending extended logistic regression: Extended versus separate versus ordered versus censored. Mon. Wea. Rev., 142, 30033014, doi:10.1175/MWR-D-13-00355.1.

    • Search Google Scholar
    • Export Citation
  • Mittermaier, M., 2012: A critical assessment of surface cloud observations and their use for verifying cloud forecasts. Quart. J. Roy. Meteor. Soc., 138, 17941807, doi:10.1002/qj.1918.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., R. Buizza, T. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73119, doi:10.1002/qj.49712252905.

    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., 1969: On the “ranked probability score.” J. Appl. Meteor., 8, 988989, doi:10.1175/1520-0450(1969)008<0988:OTPS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pelland, S., G. Galanis, and G. Kallos, 2013: Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model. Prog. Photovolt. Res. Appl., 21, 284296, doi:10.1002/pip.1180.

    • Search Google Scholar
    • Export Citation
  • Pinson, P., and R. Hagedorn, 2012: Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations. Meteor. Appl., 19, 484500, doi:10.1002/met.283.

    • Search Google Scholar
    • Export Citation
  • Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 11551174, doi:10.1175/MWR2906.1.

    • Search Google Scholar
    • Export Citation
  • Richardson, D., S. Hemri, K. Bogner, T. Gneiting, T. Haiden, F. Pappenberger, and M. Scheuerer, 2015: Calibration of ECMWF forecasts. ECMWF Newsletter, No. 142, ECMWF, Reading, United Kingdom, 1216.

  • Ripley, B., and W. Venables, 2014: Feed-forward neural networks and multinomial log-linear models, version 7.3-8, R package, accessed 3 November 2015. [Available online at https://cran.r-project.org/web/packages/nnet/.]

  • Roulin, E., and S. Vannitsem, 2012: Postprocessing of ensemble precipitation predictions with extended logistic regression based on hindcasts. Mon. Wea. Rev., 140, 874888, doi:10.1175/MWR-D-11-00062.1.

    • Search Google Scholar
    • Export Citation
  • Ruiz, J. J., and C. Saulo, 2012: How sensitive are probabilistic precipitation forecasts to the choice of calibration algorithms and the ensemble generation method? Part I: Sensitivity to calibration methods. Meteor. Appl., 19, 302313, doi:10.1002/met.286.

    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., 2014: Probabilistic quantitative precipitation forecasting using ensemble model output statistics. Quart. J. Roy. Meteor. Soc., 140, 10861096, doi:10.1002/qj.2183.

    • Search Google Scholar
    • Export Citation
  • Schmeits, M. J., and K. J. Kok, 2010: A comparison between raw ensemble output, (modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Mon. Wea. Rev., 138, 41994211, doi:10.1175/2010MWR3285.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, J. W., and R. Buizza, 2003: Using weather ensemble predictions in electricity demand forecasting. Int. J. Forecasting, 19, 5770, doi:10.1016/S0169-2070(01)00123-6.

    • Search Google Scholar
    • Export Citation
  • Venables, W. N., and B. D. Ripley, 2002: Modern Applied Statistics with S. 4th ed. Springer, 495 pp.

  • Wacker, S., and Coauthors, 2015: Cloud observations in Switzerland using hemispherical sky cameras. J. Geophys. Res. Atmos., 120, 695707, doi:10.1002/2014JD022643.

    • Search Google Scholar
    • Export Citation
  • Walker, S. H., and D. B. Duncan, 1967: Estimation of the probability of an event as a function of several independent variables. Biometrika, 54, 167179, doi:10.1093/biomet/54.1-2.167.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361368, doi:10.1002/met.134.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., and T. M. Hamill, 2007: Comparisons of ensemble-MOS methods using GFS forecasts. Mon. Wea. Rev., 135, 23792390, doi:10.1175/MWR3402.1.

    • Search Google Scholar
    • Export Citation
  • Ye, Q. Z., and S. S. Chen, 2013: The ultimate meteorological question from observational astronomers: How good is the cloud cover forecast? Mon. Not. Roy. Astron. Soc., 428, 32883294, doi:10.1093/mnras/sts278.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 831 149 6
PDF Downloads 518 104 10