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Handbook of Hydrometeorological Ensemble Forecasting pp 1–24Cite as

Multi-model Combination and Seamless Prediction

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Abstract

(Hydro-) Meteorological predictions are inherently uncertain. Forecasters are trying to estimate and to ultimately also reduce predictive uncertainty. Atmospheric ensemble prediction systems (EPS) provide forecast ensembles that give a first idea of forecast uncertainty. Combining EPS forecasts, issued by different weather services, to multi-model ensembles gives an even better understanding of forecast uncertainty. This article reviews state of the art statistical post-processing methods that allow for sound combinations of multi-model ensemble forecasts. The aim of statistical post-processing is to maximize the sharpness of the predictive distribution subject to calibration. This article focuses on the well-established parametric approaches: Bayesian model averaging (BMA) and ensemble model output statistics (EMOS). Both are readily available and can be used for straightforward implementation of methods for multi-model ensemble combination. Furthermore, methods to ensure seamless predictions in the context of statistical post-processing are summarized. These methods are mainly based on different types of copula approaches. Since skill of (statistically post-processed) ensemble forecasts is generally assessed using particular verification methods, an overview over such methods to verify probabilistic forecasts is provided as well.

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Authors and Affiliations

  1. Department of Computational Statistics, HITS gGmbH, Schloß-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany

    Stephan Hemri

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  1. Stephan Hemri
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Correspondence to Stephan Hemri .

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

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Hemri, S. (2015). Multi-model Combination and Seamless Prediction. 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_19-1

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  • DOI: https://doi.org/10.1007/978-3-642-40457-3_19-1

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  • Online ISBN: 978-3-642-40457-3

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