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Spatial Ensemble Post-Processing with Standardized Anomalies

Author

Listed:
  • Markus Dabernig
  • Georg J. Mayr
  • Jakob W. Messner
  • Achim Zeileis

Abstract

To post-process ensemble predictions to a particular location, often statistical methods are used, especially in complex terrain such as the Alps. When expanded to several stations, the post-processing has to be repeated at every station individually thus losing information about spatial coherence and increasing computational cost. Therefore, we transform observations and predictions to standardized anomalies. Site- and season-specific characteristics are eliminated by subtracting a climatological mean and dividing by the climatological standard deviation from both observations and numerical forecasts. Then ensemble post-processing can be applied simultaneously at multiple locations. Furthermore, this method allows to forecast even at locations where no observations are available. The skill of these forecasts is comparable to forecasts post-processed individually at every station, and even better on average.

Suggested Citation

  • Markus Dabernig & Georg J. Mayr & Jakob W. Messner & Achim Zeileis, 2016. "Spatial Ensemble Post-Processing with Standardized Anomalies," Working Papers 2016-08, Faculty of Economics and Statistics, Universität Innsbruck.
  • Handle: RePEc:inn:wpaper:2016-08
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    File URL: https://www2.uibk.ac.at/downloads/c4041030/wpaper/2016-08.pdf
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    References listed on IDEAS

    as
    1. Michael Scheuerer & Luca Büermann, 2014. "Spatially adaptive post-processing of ensemble forecasts for temperature," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(3), pages 405-422, April.
    2. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    3. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    statistical post-processing; ensemble post-processing; spatial; temperature; standardized anomalies; climatology; generalized additive model;
    All these keywords.

    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

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