• Allen, S., C. A. T. Ferro, and F. Kwasniok, 2019: Regime-dependent statistical post-processing of ensemble forecasts. Quart. J. Roy. Meteor. Soc., 145, 35353552, https://doi.org/10.1002/qj.3638.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, S., C. A. T. Ferro, and F. Kwasniok, 2020: Recalibrating wind speed forecasts using regime-dependent ensemble model output statistics. Quart. J. Roy. Meteor. Soc., 146, 25762596, https://doi.org/10.1002/qj.3806.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, S., G. R. Evans, P. Buchanan, and F. Kwasniok, 2021: Incorporating the North Atlantic Oscillation into the post-processing of MOGREPS-G wind speed forecasts. Quart. J. Roy. Meteor. Soc., 147, 14031418, https://doi.org/10.1002/qj.3983.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alley, R. B., K. A. Emanuel, and F. Zhang, 2019: Advances in weather prediction. Science, 363, 342344, https://doi.org/10.1126/science.aav7274.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Azzalini, A., 1985: A class of distributions which includes the normal ones. Scand. J. Stat., 12, 171178.

  • Barnes, C., C. M. Brierley, and R. E. Chandler, 2019: New approaches to postprocessing of multi-model ensemble forecasts. Quart. J. Roy. Meteor. Soc., 145, 34793498, https://doi.org/10.1002/qj.3632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Box, G. E., and D. R. Cox, 1964: An analysis of transformations. J. Roy. Stat. Soc., 26, 211243.

  • Bremnes, J. B., 2020: Ensemble postprocessing using quantile function regression based on neural networks and Bernstein polynomials. Mon. Wea. Rev., 148, 403414, https://doi.org/10.1175/MWR-D-19-0227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dabernig, M., I. Schicker, A. Kann, Y. Wang, and M. N. Lang, 2020: Statistical post-processing with standardized anomalies based on a 1 km gridded analysis. Meteor. Z., 29, 265275, https://doi.org/10.1127/metz/2020/1022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, G. R., and et al. , 2020: metoppv/improver: IMPROVER: A library of algorithms for meteorological post-processing (version 0.10.0).GitHub, https://doi.org/10.5281/zenodo.3744431.

    • Crossref
    • Export Citation
  • Feldmann, K., M. Scheuerer, and T. L. Thorarinsdottir, 2015: Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression. Mon. Wea. Rev., 143, 955971, https://doi.org/10.1175/MWR-D-14-00210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldmann, K., D. S. Richardson, and T. Gneiting, 2019: Grid-versus station-based postprocessing of ensemble temperature forecasts. Geophys. Res. Lett., 46, 77447751, https://doi.org/10.1029/2019GL083189.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferro, C. A. T., 2017: Measuring forecast performance in the presence of observation error. Quart. J. Roy. Meteor. Soc., 143, 26652676, https://doi.org/10.1002/qj.3115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedli, L., D. Ginsbourger, and J. Bhend, 2021: Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions. Stochastic Environ. Res. Risk Assess., 35, 215230, https://doi.org/10.1007/s00477-020-01928-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebetsberger, M., J. W. Messner, G. J. Mayr, and A. Zeileis, 2018: Estimation methods for nonhomogeneous regression models: Minimum continuous ranked probability score versus maximum likelihood. Mon. Wea. Rev., 146, 43234338, https://doi.org/10.1175/MWR-D-17-0364.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebetsberger, M., R. Stauffer, G. J. Mayr, and A. Zeileis, 2019: Skewed logistic distribution for statistical temperature post-processing in mountainous areas. Adv. Stat. Climatol. Meteor. Oceanogr., 5, 87100, https://doi.org/10.5194/ascmo-5-87-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glahn, H. R., and D. A. Lowry, 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 12031211, https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2.

    • Crossref
    • 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, https://doi.org/10.1198/016214506000001437.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., and R. Ranjan, 2011: Comparing density forecasts using threshold-and quantile-weighted scoring rules. J. Bus. Econ. Stat., 29, 411422, https://doi.org/10.1198/jbes.2010.08110.

    • Crossref
    • 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, 10981118, https://doi.org/10.1175/MWR2904.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gupta, R. D., and D. Kundu, 2010: Generalized logistic distributions. J. Appl. Stat. Sci., 18, 51–66.

  • Hagelin, S., J. Son, R. Swinbank, A. McCabe, N. Roberts, and W. Tennant, 2017: The Met Office convective-scale ensemble, MOGREPS-UK. Quart. J. Roy. Meteor. Soc., 143, 28462861, https://doi.org/10.1002/qj.3135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2018: Practical aspects of statistical postprocessing. Statistical Postprocessing of Ensemble Forecasts, S. Vannitsem, D. S. Wilks, and J. W. Messner, Eds., Elsevier, 187–217.

  • Hamill, T. M., E. Engle, D. Myrick, M. Peroutka, C. Finan, and M. Scheuerer, 2017: The U.S. national blend of models for statistical postprocessing of probability of precipitation and deterministic precipitation amount. Mon. Wea. Rev., 145, 34413463, https://doi.org/10.1175/MWR-D-16-0331.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hemri, S., D. Lisniak, and B. Klein, 2015: Multivariate postprocessing techniques for probabilistic hydrological forecasting. Water Resour. Res., 51, 74367451, https://doi.org/10.1002/2014WR016473.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henzi, A., G.-R. Kleger, and J. F. Ziegel, 2020: Distributional (single) index models. arXiv preprint arXiv:2006.09219.

  • Johnson, N. L., S. Kotz, and N. Balakrishnan, 1995: Continuous Univariate Distributions. John Wiley & Sons, Ltd., 714 pp.

  • Jordan, A., F. Krüger, and S. Lerch, 2017: Evaluating probabilistic forecasts with scoring rules. J. Stat. Software, 90, 137, https://doi.org/10.18637/jss.v090.i12.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.

  • Klein, N., and et al. , 2015: Bayesian structured additive distributional regression with an application to regional income inequality in Germany. Ann. Appl. Stat., 9, 10241052, https://doi.org/10.1214/15-AOAS823.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, W. H., B. M. Lewis, and I. Enger, 1959: Objective prediction of five-day mean temperatures during winter. J. Meteor., 16, 672682, https://doi.org/10.1175/1520-0469(1959)016<0672:OPOFDM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lerch, S., and S. Baran, 2017: Similarity-based semilocal estimation of post-processing models. J. Roy. Stat. Soc., 66, 2951, https://doi.org/10.1111/rssc.12153.

    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/MWR-D-13-00355.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Messner, J. W., G. J. Mayr, and A. Zeileis, 2017: Nonhomogeneous boosting for predictor selection in ensemble postprocessing. Mon. Wea. Rev., 145, 137147, https://doi.org/10.1175/MWR-D-16-0088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Möller, A., and J. Groß, 2020: Probabilistic temperature forecasting with a heteroscedastic autoregressive ensemble postprocessing model. Quart. J. Roy. Meteor. Soc., 146, 211224, https://doi.org/10.1002/qj.3667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oliver, H. J., M. Shin, and O. Sanders, 2018: Cylc: A workflow engine for cycling systems. J. Open Source Software, 3, 737, https://doi.org/10.21105/joss.00737.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oliver, H., and et al. , 2019: Workflow automation for cycling systems: The Cylc workflow engine. Comput. Sci. Eng., 21, 721, https://doi.org/10.1109/MCSE.2019.2906593.

    • Crossref
    • 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, https://doi.org/10.1175/MWR2906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasp, S., and S. Lerch, 2018: Neural networks for postprocessing ensemble weather forecasts. Mon. Wea. Rev., 146, 38853900, https://doi.org/10.1175/MWR-D-18-0187.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., and L. Büermann, 2014: Spatially adaptive post-processing of ensemble forecasts for temperature. J. Roy. Stat. Soc., 63, 405422, https://doi.org/10.1111/rssc.12040.

    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., and G. König, 2014: Gridded, locally calibrated, probabilistic temperature forecasts based on ensemble model output statistics. Quart. J. Roy. Meteor. Soc., 140, 25822590, https://doi.org/10.1002/qj.2323.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., and T. M. Hamill, 2015: Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Mon. Wea. Rev., 143, 45784596, https://doi.org/10.1175/MWR-D-15-0061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., and D. Möller, 2015: Probabilistic wind speed forecasting on a grid based on ensemble model output statistics. Ann. Appl. Stat., 9, 13281349, https://doi.org/10.1214/15-AOAS843.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schuhen, N., T. L. Thorarinsdottir, and T. Gneiting, 2012: Ensemble model output statistics for wind vectors. Mon. Wea. Rev., 140, 32043219, https://doi.org/10.1175/MWR-D-12-00028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schuhen, N., T. Thorarinsdottir, and A. Lenkoski, 2020: Rapid adjustment and post-processing of temperature forecast trajectories. Quart. J. Roy. Meteor. Soc., 146, 963978, https://doi.org/10.1002/qj.3718.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siegert, S., P. G. Sansom, and R. M. Williams, 2016a: Parameter uncertainty in forecast recalibration. Quart. J. Roy. Meteor. Soc., 142, 12131221, https://doi.org/10.1002/qj.2716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siegert, S., D. B. Stephenson, P. G. Sansom, A. A. Scaife, R. Eade, and A. Arribas, 2016b: A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability? J. Climate, 29, 9951012, https://doi.org/10.1175/JCLI-D-15-0196.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sloughter, J. M., A. E. Raftery, T. Gneiting, and C. Fraley, 2007: Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon. Wea. Rev., 135, 32093220, https://doi.org/10.1175/MWR3441.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephenson, D., C. Coelho, F. Doblas-Reyes, and M. Balmaseda, 2005: Forecast assimilation: A unified framework for the combination of multi-model weather and climate predictions. Tellus, 57A, 253264, https://doi.org/10.3402/tellusa.v57i3.14664.

    • Search Google Scholar
    • Export Citation
  • Taillardat, M., O. Mestre, M. Zamo, and P. Naveau, 2016: Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics. Mon. Wea. Rev., 144, 23752393, https://doi.org/10.1175/MWR-D-15-0260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, Y., H. W. Lean, and J. Bornemann, 2013: The benefits of the Met Office variable resolution NWP model for forecasting convection. Meteor. Appl., 20, 417426, https://doi.org/10.1002/met.1300.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorarinsdottir, T. L., and T. Gneiting, 2010: Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroscedastic censored regression. J. Roy. Stat. Soc., 173, 371388, https://doi.org/10.1111/j.1467-985X.2009.00616.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorarinsdottir, T. L., and N. Schuhen, 2018: Verification: Assessment of calibration and accuracy. Statistical Postprocessing of Ensemble Forecasts, S. Vannitsem, D. S. Wilks, and J. Messner, Eds., Elsevier, 155–186.

  • Tödter, J., and B. Ahrens, 2012: Generalization of the ignorance score: Continuous ranked version and its decomposition. Mon. Wea. Rev., 140, 20052017, https://doi.org/10.1175/MWR-D-11-00266.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Schaeybroeck, B., and S. Vannitsem, 2015: Ensemble post-processing using member-by-member approaches: Theoretical aspects. Quart. J. Roy. Meteor. Soc., 141, 807818, https://doi.org/10.1002/qj.2397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Von Storch, H., and F. W. Zwiers, 2001: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Wilks, D. S., 2019: Statistical Methods in the Atmospheric Sciences. 4th ed. Elsevier, 840 pp..

  • Williams, R., C. A. T. Ferro, and F. Kwasniok, 2014: A comparison of ensemble post-processing methods for extreme events. Quart. J. Roy. Meteor. Soc., 140, 11121120, https://doi.org/10.1002/qj.2198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeo, I.-K., and R. A. Johnson, 2000: A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954959, https://doi.org/10.1093/biomet/87.4.954.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 136 136 24
Full Text Views 38 38 16
PDF Downloads 43 43 13

Accounting for Skew when Postprocessing MOGREPS-UK Temperature Forecast Fields

View More View Less
  • 1 a Department of Mathematics, University of Exeter, Exeter, United Kingdom
  • | 2 b Met Office, Exeter, United Kingdom
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

When statistically postprocessing temperature forecasts, it is almost always assumed that the future temperature follows a Gaussian distribution conditional on the output of an ensemble prediction system. Recent studies, however, have demonstrated that it can at times be beneficial to employ alternative parametric families when postprocessing temperature forecasts that are either asymmetric or heavier-tailed than the normal distribution. In this article, we compare choices of the parametric distribution used within the ensemble model output statistics (EMOS) framework to statistically postprocess 2-m temperature forecast fields generated by the Met Office’s regional, convection-permitting ensemble prediction system, MOGREPS-UK Specifically, we study the normal, logistic, and skew-logistic distributions. A flexible alternative is also introduced that first applies a Yeo–Johnson transformation to the temperature forecasts prior to postprocessing, so that they more readily conform to the assumptions made by established postprocessing methods. It is found that accounting for the skewness of temperature when postprocessing can enhance the performance of the resulting forecast field, particularly during summer and winter and in mountainous regions.

Allen’s current affiliation: Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sam Allen, sam.allen@stat.unibe.ch

Abstract

When statistically postprocessing temperature forecasts, it is almost always assumed that the future temperature follows a Gaussian distribution conditional on the output of an ensemble prediction system. Recent studies, however, have demonstrated that it can at times be beneficial to employ alternative parametric families when postprocessing temperature forecasts that are either asymmetric or heavier-tailed than the normal distribution. In this article, we compare choices of the parametric distribution used within the ensemble model output statistics (EMOS) framework to statistically postprocess 2-m temperature forecast fields generated by the Met Office’s regional, convection-permitting ensemble prediction system, MOGREPS-UK Specifically, we study the normal, logistic, and skew-logistic distributions. A flexible alternative is also introduced that first applies a Yeo–Johnson transformation to the temperature forecasts prior to postprocessing, so that they more readily conform to the assumptions made by established postprocessing methods. It is found that accounting for the skewness of temperature when postprocessing can enhance the performance of the resulting forecast field, particularly during summer and winter and in mountainous regions.

Allen’s current affiliation: Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sam Allen, sam.allen@stat.unibe.ch
Save