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Short-Range Ensemble Forecast Post-processing

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Abstract

Short-term hydrological ensemble forecasts do not usually account for the uncertainty in the initial conditions. Consequently, raw forecasts are often biased and under-dispersed and must be post-processed. Both precipitation and streamflow forecasts for short lead-time depart from the Gaussian distribution, and this important characteristic limits the choice of possible post-processing approaches. Post-processing is performed by calibrating a statistical model using a training dataset containing past forecasts and the corresponding observations. This chapter covers the most common post-processing approaches for short-term hydrological forecasts. They are divided into four categories: analog methods, regressions, kernel dressing, and Bayesian Model Averaging. The vast majority of post-processing methods can be categorized as regression-based. A selection of the most commonly encountered ones in hydrology is presented: quantile regression, nonhomogeneous regression, and logistic regression. Any post-processing approach brings benefits and drawbacks, which are discussed at the end of this chapter. However, according to the few existing comparative studies, no single method is appropriate for all forecasting situation. Therefore, the reader should make his or her own mind regarding which one to choose, according to his or her own specific needs and limitations.

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Author information

Authors and Affiliations

  1. Civil Engineering Department, Université de Sherbrooke, Sherbrooke, QC, Canada

    M. -A. Boucher

  2. Institut Royal Météorologique de Belgique, Bruxelles, Belgium

    Emmanuel Roulin

  3. Environment and Climate Change Canada, Dorval, QC, Canada

    Vincent Fortin

Authors
  1. M. -A. Boucher
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  2. Emmanuel Roulin
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  3. Vincent Fortin
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Corresponding author

Correspondence to M. -A. Boucher .

Editor information

Editors and Affiliations

  1. Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

    Florian Pappenberger

  3. European Commission DG Joint Research Ce, Ispra, Italy

    Jutta Thielen

  4. National Center for Atmospheric Research, Boulder, Colorado, USA

    Andy Wood

  5. University of Reading, Reading, Berkshire, United Kingdom

    Hannah L. Cloke

  6. Consulting Hydrologist, Annapolis, USA

    John C. Schaake

Section Editor information

  1. National Center for Atmospheric Research, Boulder, USA

    Andy Wood Scientist

  2. Research Applications Laboratory, National Center for Atmospheric Research, Colorado, USA

    Thomas Hopson Scientist

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Boucher, M.A., Roulin, E., Fortin, V. (2018). Short-Range Ensemble Forecast Post-processing. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_71-1

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  • DOI: https://doi.org/10.1007/978-3-642-40457-3_71-1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40457-3

  • Online ISBN: 978-3-642-40457-3

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