IDEAS home Printed from
   My bibliography  Save this paper

Tricks for improving non-homogeneous regression for probabilistic precipitation forecasts: Perfect predictions, heavy tails, and link functions


  • Manuel Gebetsberger


  • Jakob W. Messner


  • Georg J. Mayr


  • Achim Zeileis



Raw ensemble forecasts display large errors in predicting precipitation amounts and its forecast uncertainty, especially in mountainous regions where local effects are often not captured. Therefore, statistical post-processing is typically applied to obtain automatically corrected weather forecasts where precipitation represents one of the most challenging quantities. This study applies the non-homogenous regression framework as a start-of-the-art ensemble post-processing technique to predict a full forecast distribution and improves its forecast performance with three statistical tricks. First of all, a novel split-type approach effectively accounts for perfect ensemble predictions that can occur. Additionally, the statistical model assumes a censored logistic distribution to deal with the heavy tails of precipitation amounts. Finally, the optimization of regression coefficients for the scale parameter is investigated with suitable link-functions. These three refinements are tested for stations in the European Alps for lead-times from +24h to +48h and accumulation periods of 24 and 6 hours. Results highlight an improvement due to a combination of the three statistical tricks against the default post-processing method which does not account for perfect ensemble predictions. Probabilistic forecasts for precipitation amounts as well as the probability of precipitation events could be improved, especially for 6 hour sums.

Suggested Citation

  • Manuel Gebetsberger & Jakob W. Messner & Georg J. Mayr & Achim Zeileis, 2016. "Tricks for improving non-homogeneous regression for probabilistic precipitation forecasts: Perfect predictions, heavy tails, and link functions," Working Papers 2016-28, Faculty of Economics and Statistics, University of Innsbruck.
  • Handle: RePEc:inn:wpaper:2016-28

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Thordis L. Thorarinsdottir & Tilmann Gneiting, 2010. "Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 371-388.
    Full references (including those not matched with items on IDEAS)

    More about this item


    non-homogeneous regression; censored logistic distribution; log-link; probabilistic precipitation forecasts; operational forecasting;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inn:wpaper:2016-28. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Janette Walde). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.